update progress snapshot

- Updated metadata.json status to completed
- Fixed corrupted plan.md (was damaged by earlier loop)
- Cleaned up duplicate Goal line in tracks.md

Checkpoint: 02abfc4

.claude/commands/conductor-setup.md

@@ -22,7 +22,7 @@ Bootstrap a Claude Code session with full conductor context. Run this at session
    - Identify the track with `[~]` in-progress tasks
 
 3. **Check Session Context:**
-   - Read `TASKS.md` if it exists — check for IN_PROGRESS or BLOCKED tasks
+   - Read `conductor/tracks.md` if it exists — check for IN_PROGRESS or BLOCKED tasks
    - Read last 3 entries in `JOURNAL.md` for recent activity
    - Run `git log --oneline -10` for recent commits
 

.gemini/skills/mma-orchestrator/SKILL.md

@@ -20,6 +20,7 @@ To ensure proper environment handling and logging, you MUST NOT call the `gemini
 - `docs/guide_tools.md`: MCP Bridge 3-layer security model, full 26-tool inventory with params, Hook API GET/POST endpoints with request/response formats, ApiHookClient method reference
 - `docs/guide_mma.md`: Ticket/Track/WorkerContext data structures, DAG engine (cycle detection, topological sort), ConductorEngine execution loop, Tier 2 ticket generation, Tier 3 worker lifecycle with context amnesia
 - `docs/guide_simulations.md`: `live_gui` fixture lifecycle, Puppeteer pattern, mock provider JSON-L protocol, visual verification patterns
+- `docs/guide_meta_boundary.md`: Clarification of ai agent tools making the application vs the application itself.
 
 ### The Surgical Spec Protocol (MANDATORY for track creation)
 
@@ -126,3 +127,9 @@ When your current role requires capabilities from another tier, use `activate_sk
 - When managing complex, multi-file Track implementations.
 - When creating or refining conductor tracks (MUST follow Surgical Spec Protocol).
 </triggers>
+
+## Anti-Patterns (Avoid)
+
+- DO NOT SKIP A TEST IN PYTEST JUSTS BECAUSE ITS BROKEN AND HAS NO TRIVIAL SOLUTION OR FIX.
+- DO NOT SIMPLIFY A TEST JUST BECAUSE IT HAS NO TRIVAL SOLUTION TO FIX.
+- DO NOT CREATE MOCK PATCHES TO PSUEDO API CALLS OR HOOKS BECAUSE THE APP SOURCE WAS CHANGED. ADAPT TESTS PROPERLY.

.gemini/skills/mma-tier1-orchestrator/SKILL.md

@@ -21,6 +21,7 @@ When planning tracks that touch core systems, consult:
 - `docs/guide_tools.md`: MCP Bridge, Hook API endpoints, ApiHookClient methods
 - `docs/guide_mma.md`: Ticket/Track structures, DAG engine, ConductorEngine, worker lifecycle
 - `docs/guide_simulations.md`: live_gui fixture, Puppeteer pattern, mock provider
+- `docs/guide_meta_boundary.md`: Clarification of ai agent tools making the application vs the application itself.
 
 ## Responsibilities
 

.opencode/agents/explore.md

@@ -1,9 +1,8 @@
 ---
 description: Fast, read-only agent for exploring the codebase structure
 mode: subagent
-model: zai/glm-4-flash
+model: MiniMax-M2.5
-temperature: 0.0
+temperature: 0.2
-steps: 8
 permission:
   edit: deny
   bash:
@@ -22,6 +21,7 @@ You are a fast, read-only agent specialized for exploring codebases. Use this wh
 You MUST use Manual Slop's MCP tools. Native OpenCode tools are unreliable.
 
 ### Read-Only MCP Tools (USE THESE)
+
 | Native Tool | MCP Tool |
 |-------------|----------|
 | `read` | `manual-slop_read_file` |
@@ -34,12 +34,14 @@ You MUST use Manual Slop's MCP tools. Native OpenCode tools are unreliable.
 | - | `manual-slop_get_tree` (directory structure) |
 
 ## Capabilities
+
 - Find files by name patterns or glob
 - Search code content with regex
 - Navigate directory structures
 - Summarize file contents
 
 ## Limitations
+
 - **READ-ONLY**: Cannot modify any files
 - **NO EXECUTION**: Cannot run tests or scripts
 - **EXPLORATION ONLY**: Use for discovery, not implementation
@@ -62,7 +64,9 @@ Use: `manual-slop_get_tree` or `manual-slop_list_directory`
 Use: `manual-slop_get_file_summary` for heuristic summary
 
 ## Report Format
+
 Return concise findings with file:line references:
+
 ```
 ## Findings
 
@@ -74,4 +78,4 @@ Return concise findings with file:line references:
 
 ### Summary
 [One-paragraph summary of findings]
-```
+```

.opencode/agents/general.md

@@ -1,9 +1,8 @@
 ---
 description: General-purpose agent for researching complex questions and executing multi-step tasks
 mode: subagent
-model: zai/glm-5
+model: MiniMax-M2.5
-temperature: 0.2
+temperature: 0.3
-steps: 15
 ---
 
 A general-purpose agent for researching complex questions and executing multi-step tasks. Has full tool access (except todo), so it can make file changes when needed.
@@ -13,6 +12,7 @@ A general-purpose agent for researching complex questions and executing multi-st
 You MUST use Manual Slop's MCP tools. Native OpenCode tools are unreliable.
 
 ### Read MCP Tools (USE THESE)
+
 | Native Tool | MCP Tool |
 |-------------|----------|
 | `read` | `manual-slop_read_file` |
@@ -26,6 +26,7 @@ You MUST use Manual Slop's MCP tools. Native OpenCode tools are unreliable.
 | - | `manual-slop_get_tree` (directory structure) |
 
 ### Edit MCP Tools (USE THESE)
+
 | Native Tool | MCP Tool |
 |-------------|----------|
 | `edit` | `manual-slop_edit_file` (find/replace, preserves indentation) |
@@ -35,11 +36,13 @@ You MUST use Manual Slop's MCP tools. Native OpenCode tools are unreliable.
 | `edit` | `manual-slop_py_set_var_declaration` (replace variable) |
 
 ### Shell Commands
+
 | Native Tool | MCP Tool |
 |-------------|----------|
 | `bash` | `manual-slop_run_powershell` |
 
 ## Capabilities
+
 - Research and answer complex questions
 - Execute multi-step tasks autonomously
 - Read and write files as needed
@@ -47,13 +50,22 @@ You MUST use Manual Slop's MCP tools. Native OpenCode tools are unreliable.
 - Coordinate multiple operations
 
 ## When to Use
+
 - Complex research requiring multiple file reads
 - Multi-step implementation tasks
 - Tasks requiring autonomous decision-making
 - Parallel execution of related operations
 
+## Code Style (for Python)
+
+- 1-space indentation
+- NO COMMENTS unless explicitly requested
+- Type hints where appropriate
+
 ## Report Format
+
 Return detailed findings with evidence:
+
 ```
 ## Task: [Original task]
 
@@ -69,4 +81,4 @@ Return detailed findings with evidence:
 
 ### Recommendations
 - [Suggested next steps if applicable]
-```
+```

.opencode/agents/tier1-orchestrator.md

@@ -1,11 +1,10 @@
 ---
 description: Tier 1 Orchestrator for product alignment, high-level planning, and track initialization
 mode: primary
-model: zai/glm-5
+model: MiniMax-M2.5
-temperature: 0.1
+temperature: 0.5
-steps: 50
 permission:
-  edit: deny
+  edit: ask
   bash:
     "*": ask
     "git status*": allow
@@ -17,11 +16,18 @@ STRICT SYSTEM DIRECTIVE: You are a Tier 1 Orchestrator.
 Focused on product alignment, high-level planning, and track initialization.
 ONLY output the requested text. No pleasantries.
 
+## Context Management
+
+**MANUAL COMPACTION ONLY** — Never rely on automatic context summarization.
+Use `/compact` command explicitly when context needs reduction.
+Preserve full context during track planning and spec creation.
+
 ## CRITICAL: MCP Tools Only (Native Tools Banned)
 
 You MUST use Manual Slop's MCP tools. Native OpenCode tools are unreliable.
 
 ### Read-Only MCP Tools (USE THESE)
+
 | Native Tool | MCP Tool |
 |-------------|----------|
 | `read` | `manual-slop_read_file` |
@@ -35,7 +41,18 @@ You MUST use Manual Slop's MCP tools. Native OpenCode tools are unreliable.
 | - | `manual-slop_get_git_diff` (file changes) |
 | - | `manual-slop_get_tree` (directory structure) |
 
+### Edit MCP Tools (USE THESE)
+
+| Native Tool | MCP Tool |
+|-------------|----------|
+| `edit` | `manual-slop_edit_file` (find/replace, preserves indentation) YOU MUST USE old_string parameter IT IS NOT oldString |
+| `edit` | `manual-slop_py_update_definition` (replace function/class) |
+| `edit` | `manual-slop_set_file_slice` (replace line range) |
+| `edit` | `manual-slop_py_set_signature` (replace signature only) |
+| `edit` | `manual-slop_py_set_var_declaration` (replace variable) |
+
 ### Shell Commands
+
 | Native Tool | MCP Tool |
 |-------------|----------|
 | `bash` | `manual-slop_run_powershell` |
@@ -43,57 +60,80 @@ You MUST use Manual Slop's MCP tools. Native OpenCode tools are unreliable.
 ## Session Start Checklist (MANDATORY)
 
 Before ANY other action:
+
 1. [ ] Read `conductor/workflow.md`
 2. [ ] Read `conductor/tech-stack.md`
 3. [ ] Read `conductor/product.md`, `conductor/product-guidelines.md`
 4. [ ] Read relevant `docs/guide_*.md` for current task domain
-5. [ ] Check `TASKS.md` for active tracks
+5. [ ] Check `conductor/tracks.md` for active tracks
 6. [ ] Announce: "Context loaded, proceeding to [task]"
 
 **BLOCK PROGRESS** until all checklist items are confirmed.
 
 ## Primary Context Documents
-Read at session start: `conductor/product.md`, `conductor/product-guidelines.md`
+
+Read at session start:
+
+- All immediate files in ./conductor, a listing of all directories within ./conductor/tracks, ./conductor/archive.
+- All docs in ./docs
+- AST Skeleton summaries of: ./src, ./simulation, ./tests, ./scripts python files.
 
 ## Architecture Fallback
+
 When planning tracks that touch core systems, consult the deep-dive docs:
+
 - `docs/guide_architecture.md`: Thread domains, event system, AI client, HITL mechanism
 - `docs/guide_tools.md`: MCP Bridge security, 26-tool inventory, Hook API endpoints
 - `docs/guide_mma.md`: Ticket/Track data structures, DAG engine, ConductorEngine
 - `docs/guide_simulations.md`: live_gui fixture, Puppeteer pattern, mock provider
+- `docs/guide_meta_boundary.md`: Clarification of ai agent tools making the application vs the application itself.
 
 ## Responsibilities
+
 - Maintain alignment with the product guidelines and definition
 - Define track boundaries and initialize new tracks (`/conductor-new-track`)
 - Set up the project environment (`/conductor-setup`)
 - Delegate track execution to the Tier 2 Tech Lead
 
-## The Surgical Methodology
+## The Surgical Methodology (MANDATORY)
 
 ### 1. MANDATORY: Audit Before Specifying
+
 NEVER write a spec without first reading actual code using MCP tools.
-Use `manual-slop_py_get_code_outline`, `manual-slop_py_get_definition`, 
+Use `manual-slop_py_get_code_outline`, `manual-slop_py_get_definition`,
 `manual-slop_py_find_usages`, and `manual-slop_get_git_diff` to build a map.
-Document existing implementations with file:line references in a 
+Document existing implementations with file:line references in a
 "Current State Audit" section in the spec.
 
+**FAILURE TO AUDIT = TRACK FAILURE** — Previous tracks failed because specs
+asked to implement features that already existed.
+
 ### 2. Identify Gaps, Not Features
+
 Frame requirements around what's MISSING relative to what exists.
 
+GOOD: "The existing `_render_mma_dashboard` (gui_2.py:2633-2724) has a token usage table but no cost column."
+BAD: "Build a metrics dashboard with token and cost tracking."
+
 ### 3. Write Worker-Ready Tasks
+
 Each plan task must be executable by a Tier 3 worker:
+
 - **WHERE**: Exact file and line range (`gui_2.py:2700-2701`)
 - **WHAT**: The specific change
 - **HOW**: Which API calls or patterns
 - **SAFETY**: Thread-safety constraints
 
 ### 4. For Bug Fix Tracks: Root Cause Analysis
+
 Read the code, trace the data flow, list specific root cause candidates.
 
 ### 5. Reference Architecture Docs
+
 Link to relevant `docs/guide_*.md` sections in every spec.
 
 ## Spec Template (REQUIRED sections)
+
 ```
 # Track Specification: {Title}
 
@@ -109,6 +149,7 @@ Link to relevant `docs/guide_*.md` sections in every spec.
 ```
 
 ## Plan Template (REQUIRED format)
+
 ```
 ## Phase N: {Name}
 Focus: {One-sentence scope}
@@ -120,6 +161,18 @@ Focus: {One-sentence scope}
 ```
 
 ## Limitations
+
 - READ-ONLY: Do NOT write code or edit files (except track spec/plan/metadata)
 - Do NOT execute tracks or implement features
 - Keep context strictly focused on product definitions and strategy
+
+## Anti-Patterns (Avoid)
+
+- Do NOT implement code directly - delegate to Tier 3 Workers
+- Do NOT skip TDD phases
+- Do NOT batch commits - commit per-task
+- Do NOT skip phase verification
+- Do NOT use native `edit` tool - use MCP tools
+- DO NOT SKIP A TEST IN PYTEST JUST BECAUSE ITS BROKEN AND HAS NO TRIVIAL SOLUTION OR FIX.
+- DO NOT SIMPLIFY A TEST JUST BECAUSE IT HAS NO TRIVIAL SOLUTION TO FIX.
+- DO NOT CREATE MOCK PATCHES TO PSEUDO API CALLS OR HOOKS BECAUSE THE APP SOURCE WAS CHANGED. ADAPT TESTS PROPERLY.

.opencode/agents/tier2-tech-lead.md

@@ -1,9 +1,8 @@
 ---
 description: Tier 2 Tech Lead for architectural design and track execution with persistent memory
 mode: primary
-model: zai/glm-5
+model: MiniMax-M2.5
-temperature: 0.2
+temperature: 0.4
-steps: 100
 permission:
   edit: ask
   bash: ask
@@ -13,11 +12,18 @@ STRICT SYSTEM DIRECTIVE: You are a Tier 2 Tech Lead.
 Focused on architectural design and track execution.
 ONLY output the requested text. No pleasantries.
 
+## Context Management
+
+**MANUAL COMPACTION ONLY** — Never rely on automatic context summarization.
+Use `/compact` command explicitly when context needs reduction.
+You maintain PERSISTENT MEMORY throughout track execution — do NOT apply Context Amnesia to your own session.
+
 ## CRITICAL: MCP Tools Only (Native Tools Banned)
 
 You MUST use Manual Slop's MCP tools. Native OpenCode tools are unreliable.
 
 ### Research MCP Tools (USE THESE)
+
 | Native Tool | MCP Tool |
 |-------------|----------|
 | `read` | `manual-slop_read_file` |
@@ -32,15 +38,17 @@ You MUST use Manual Slop's MCP tools. Native OpenCode tools are unreliable.
 | - | `manual-slop_get_tree` (directory structure) |
 
 ### Edit MCP Tools (USE THESE)
+
 | Native Tool | MCP Tool |
 |-------------|----------|
-| `edit` | `manual-slop_edit_file` (find/replace, preserves indentation) |
+| `edit` | `manual-slop_edit_file` (find/replace, preserves indentation) YOU MUST USE old_string parameter IT IS NOT oldString |
 | `edit` | `manual-slop_py_update_definition` (replace function/class) |
 | `edit` | `manual-slop_set_file_slice` (replace line range) |
 | `edit` | `manual-slop_py_set_signature` (replace signature only) |
 | `edit` | `manual-slop_py_set_var_declaration` (replace variable) |
 
 ### Shell Commands
+
 | Native Tool | MCP Tool |
 |-------------|----------|
 | `bash` | `manual-slop_run_powershell` |
@@ -48,45 +56,61 @@ You MUST use Manual Slop's MCP tools. Native OpenCode tools are unreliable.
 ## Session Start Checklist (MANDATORY)
 
 Before ANY other action:
+
 1. [ ] Read `conductor/workflow.md`
 2. [ ] Read `conductor/tech-stack.md`
 3. [ ] Read `conductor/product.md`
-4. [ ] Read relevant `docs/guide_*.md` for current task domain
+4. [ ] Read `conductor/product-guidelines.md`
-5. [ ] Check `TASKS.md` for active tracks
+5. [ ] Read relevant `docs/guide_*.md` for current task domain
-6. [ ] Announce: "Context loaded, proceeding to [task]"
+6. [ ] Check `conductor/tracks.md` for active tracks
+7. [ ] Announce: "Context loaded, proceeding to [task]"
 
 **BLOCK PROGRESS** until all checklist items are confirmed.
 
 ## Tool Restrictions (TIER 2)
 
 ### ALLOWED Tools (Read-Only Research)
+
 - `manual-slop_read_file` (for files <50 lines only)
 - `manual-slop_py_get_skeleton`, `manual-slop_py_get_code_outline`, `manual-slop_get_file_summary`
 - `manual-slop_py_find_usages`, `manual-slop_search_files`
 - `manual-slop_run_powershell` (for git status, pytest --collect-only)
 
 ### FORBIDDEN Actions (Delegate to Tier 3)
+
 - **NEVER** use native `edit` tool on .py files - destroys indentation
 - **NEVER** write implementation code directly - delegate to Tier 3 Worker
 - **NEVER** skip TDD Red-Green cycle
 
 ### Required Pattern
+
 1. Research with skeleton tools
 2. Draft surgical prompt with WHERE/WHAT/HOW/SAFETY
 3. Delegate to Tier 3 via Task tool
 4. Verify result
 
-## Primary Context Documents
+## Pre-Delegation Checkpoint (MANDATORY)
-Read at session start: `conductor/product.md`, `conductor/workflow.md`, `conductor/tech-stack.md`
+
+Before delegating ANY dangerous or non-trivial change to Tier 3:
+
+```powershell
+git add .
+```
+
+**WHY**: If a Tier 3 Worker fails or incorrectly runs `git restore`, you will lose ALL prior AI iterations for that file if it wasn't staged/committed.
 
 ## Architecture Fallback
+
 When implementing tracks that touch core systems, consult the deep-dive docs:
+
 - `docs/guide_architecture.md`: Thread domains, event system, AI client, HITL mechanism
 - `docs/guide_tools.md`: MCP Bridge security, 26-tool inventory, Hook API endpoints
 - `docs/guide_mma.md`: Ticket/Track data structures, DAG engine, ConductorEngine
 - `docs/guide_simulations.md`: live_gui fixture, Puppeteer pattern, mock provider
+- `docs/guide_meta_boundary.md`: Clarification of ai agent tools making the application vs the application itself.
 
 ## Responsibilities
+
 - Convert track specs into implementation plans with surgical tasks
 - Execute track implementation following TDD (Red -> Green -> Refactor)
 - Delegate code implementation to Tier 3 Workers via Task tool
@@ -97,46 +121,58 @@ When implementing tracks that touch core systems, consult the deep-dive docs:
 ## TDD Protocol (MANDATORY)
 
 ### 1. High-Signal Research Phase
+
 Before implementing:
+
 - Use `manual-slop_py_get_code_outline`, `manual-slop_py_get_skeleton` to map file relations
 - Use `manual-slop_get_git_diff` for recently modified code
 - Audit state: Check `__init__` methods for existing/duplicate state variables
 
 ### 2. Red Phase: Write Failing Tests
-- Pre-delegation checkpoint: Stage current progress (`git add .`)
+
+- **Pre-delegation checkpoint**: Stage current progress (`git add .`)
 - Zero-assertion ban: Tests MUST have meaningful assertions
 - Delegate test creation to Tier 3 Worker via Task tool
 - Run tests and confirm they FAIL as expected
+- **CONFIRM FAILURE** — this is the Red phase
 
 ### 3. Green Phase: Implement to Pass
-- Pre-delegation checkpoint: Stage current progress
+
+- **Pre-delegation checkpoint**: Stage current progress (`git add .`)
 - Delegate implementation to Tier 3 Worker via Task tool
 - Run tests and confirm they PASS
+- **CONFIRM PASS** — this is the Green phase
 
 ### 4. Refactor Phase (Optional)
+
 - With passing tests, refactor for clarity and performance
 - Re-run tests to ensure they still pass
 
 ### 5. Commit Protocol (ATOMIC PER-TASK)
+
 After completing each task:
-1. Stage changes: `git add .`
+
+1. Stage changes: `manual-slop_run_powershell` with `git add .`
 2. Commit with clear message: `feat(scope): description`
 3. Get commit hash: `git log -1 --format="%H"`
 4. Attach git note: `git notes add -m "summary" <hash>`
 5. Update plan.md: Mark task `[x]` with commit SHA
-6. Commit plan update
+6. Commit plan update: `git add plan.md && git commit -m "conductor(plan): Mark task complete"`
 
 ## Delegation via Task Tool
 
 OpenCode uses the Task tool for subagent delegation. Always provide surgical prompts with WHERE/WHAT/HOW/SAFETY structure.
 
 ### Tier 3 Worker (Implementation)
+
 Invoke via Task tool:
+
 - `subagent_type`: "tier3-worker"
 - `description`: Brief task name
 - `prompt`: Surgical prompt with WHERE/WHAT/HOW/SAFETY structure
 
 Example Task tool invocation:
+
 ```
 description: "Write tests for cost estimation"
 prompt: |
@@ -151,13 +187,17 @@ prompt: |
 ```
 
 ### Tier 4 QA (Error Analysis)
+
 Invoke via Task tool:
+
 - `subagent_type`: "tier4-qa"
 - `description`: "Analyze test failure"
 - `prompt`: Error output + explicit instruction "DO NOT fix - provide root cause analysis only"
 
 ## Phase Completion Protocol
+
 When all tasks in a phase are complete:
+
 1. Run `/conductor-verify` to execute automated verification
 2. Present results to user and await confirmation
 3. Create checkpoint commit: `conductor(checkpoint): Phase N complete`
@@ -165,8 +205,12 @@ When all tasks in a phase are complete:
 5. Update plan.md with checkpoint SHA
 
 ## Anti-Patterns (Avoid)
+
 - Do NOT implement code directly - delegate to Tier 3 Workers
 - Do NOT skip TDD phases
 - Do NOT batch commits - commit per-task
 - Do NOT skip phase verification
 - Do NOT use native `edit` tool - use MCP tools
+- DO NOT SKIP A TEST IN PYTEST JUST BECAUSE ITS BROKEN AND HAS NO TRIVIAL SOLUTION OR FIX.
+- DO NOT SIMPLIFY A TEST JUST BECAUSE IT HAS NO TRIVIAL SOLUTION TO FIX.
+- DO NOT CREATE MOCK PATCHES TO PSEUDO API CALLS OR HOOKS BECAUSE THE APP SOURCE WAS CHANGED. ADAPT TESTS PROPERLY.

.opencode/agents/tier3-worker.md

@@ -1,9 +1,8 @@
 ---
 description: Stateless Tier 3 Worker for surgical code implementation and TDD
 mode: subagent
-model: zai/glm-4-flash
+model: MiniMax-M2.5
-temperature: 0.1
+temperature: 0.3
-steps: 10
 permission:
   edit: allow
   bash: allow
@@ -13,11 +12,17 @@ STRICT SYSTEM DIRECTIVE: You are a stateless Tier 3 Worker (Contributor).
 Your goal is to implement specific code changes or tests based on the provided task.
 Follow TDD and return success status or code changes. No pleasantries, no conversational filler.
 
+## Context Amnesia
+
+You operate statelessly. Each task starts fresh with only the context provided.
+Do not assume knowledge from previous tasks or sessions.
+
 ## CRITICAL: MCP Tools Only (Native Tools Banned)
 
 You MUST use Manual Slop's MCP tools. Native OpenCode tools are unreliable.
 
 ### Read MCP Tools (USE THESE)
+
 | Native Tool | MCP Tool |
 |-------------|----------|
 | `read` | `manual-slop_read_file` |
@@ -30,6 +35,7 @@ You MUST use Manual Slop's MCP tools. Native OpenCode tools are unreliable.
 | - | `manual-slop_get_file_slice` (read specific line range) |
 
 ### Edit MCP Tools (USE THESE - BAN NATIVE EDIT)
+
 | Native Tool | MCP Tool |
 |-------------|----------|
 | `edit` | `manual-slop_edit_file` (find/replace, preserves indentation) |
@@ -39,17 +45,15 @@ You MUST use Manual Slop's MCP tools. Native OpenCode tools are unreliable.
 | `edit` | `manual-slop_py_set_var_declaration` (replace variable) |
 
 ### Shell Commands
+
 | Native Tool | MCP Tool |
 |-------------|----------|
 | `bash` | `manual-slop_run_powershell` |
 
-## Context Amnesia
-You operate statelessly. Each task starts fresh with only the context provided.
-Do not assume knowledge from previous tasks or sessions.
-
 ## Task Start Checklist (MANDATORY)
 
 Before implementing:
+
 1. [ ] Read task prompt - identify WHERE/WHAT/HOW/SAFETY
 2. [ ] Use skeleton tools for files >50 lines (`manual-slop_py_get_skeleton`, `manual-slop_get_file_summary`)
 3. [ ] Verify target file and line range exists
@@ -58,19 +62,24 @@ Before implementing:
 ## Task Execution Protocol
 
 ### 1. Understand the Task
+
 Read the task prompt carefully. It specifies:
+
 - **WHERE**: Exact file and line range to modify
 - **WHAT**: The specific change required
 - **HOW**: Which API calls, patterns, or data structures to use
 - **SAFETY**: Thread-safety constraints if applicable
 
 ### 2. Research (If Needed)
+
 Use MCP tools to understand the context:
+
 - `manual-slop_read_file` - Read specific file sections
 - `manual-slop_py_find_usages` - Search for patterns
 - `manual-slop_search_files` - Find files by pattern
 
 ### 3. Implement
+
 - Follow the exact specifications provided
 - Use the patterns and APIs specified in the task
 - Use 1-space indentation for Python code
@@ -78,32 +87,50 @@ Use MCP tools to understand the context:
 - Use type hints where appropriate
 
 ### 4. Verify
+
 - Run tests if specified: `manual-slop_run_powershell` with `uv run pytest ...`
 - Check for syntax errors: `manual-slop_py_check_syntax`
 - Verify the change matches the specification
 
 ### 5. Report
+
 Return a concise summary:
+
 - What was changed
 - Where it was changed
 - Any issues encountered
 
 ## Code Style Requirements
+
 - **NO COMMENTS** unless explicitly requested
 - 1-space indentation for Python code
 - Type hints where appropriate
 - Internal methods/variables prefixed with underscore
 
 ## Quality Checklist
+
 Before reporting completion:
+
 - [ ] Change matches the specification exactly
 - [ ] No unintended modifications
 - [ ] No syntax errors
 - [ ] Tests pass (if applicable)
 
 ## Blocking Protocol
+
 If you cannot complete the task:
+
 1. Start your response with `BLOCKED:`
 2. Explain exactly why you cannot proceed
 3. List what information or changes would unblock you
 4. Do NOT attempt partial implementations that break the build
+
+## Anti-Patterns (Avoid)
+
+- Do NOT use native `edit` tool - use MCP tools
+- Do NOT read full large files - use skeleton tools first
+- Do NOT add comments unless requested
+- Do NOT modify files outside the specified scope
+- DO NOT SKIP A TEST IN PYTEST JUST BECAUSE ITS BROKEN AND HAS NO TRIVIAL SOLUTION OR FIX.
+- DO NOT SIMPLIFY A TEST JUST BECAUSE IT HAS NO TRIVIAL SOLUTION TO FIX.
+- DO NOT CREATE MOCK PATCHES TO PSEUDO API CALLS OR HOOKS BECAUSE THE APP SOURCE WAS CHANGED. ADAPT TESTS PROPERLY.

.opencode/agents/tier4-qa.md

@@ -1,9 +1,8 @@
 ---
 description: Stateless Tier 4 QA Agent for error analysis and diagnostics
 mode: subagent
-model: zai/glm-4-flash
+model: MiniMax-M2.5
-temperature: 0.0
+temperature: 0.2
-steps: 5
 permission:
   edit: deny
   bash:
@@ -17,11 +16,17 @@ STRICT SYSTEM DIRECTIVE: You are a stateless Tier 4 QA Agent.
 Your goal is to analyze errors, summarize logs, or verify tests.
 ONLY output the requested analysis. No pleasantries.
 
+## Context Amnesia
+
+You operate statelessly. Each analysis starts fresh.
+Do not assume knowledge from previous analyses or sessions.
+
 ## CRITICAL: MCP Tools Only (Native Tools Banned)
 
 You MUST use Manual Slop's MCP tools. Native OpenCode tools are unreliable.
 
 ### Read-Only MCP Tools (USE THESE)
+
 | Native Tool | MCP Tool |
 |-------------|----------|
 | `read` | `manual-slop_read_file` |
@@ -35,17 +40,15 @@ You MUST use Manual Slop's MCP tools. Native OpenCode tools are unreliable.
 | - | `manual-slop_get_file_slice` (read specific line range) |
 
 ### Shell Commands
+
 | Native Tool | MCP Tool |
 |-------------|----------|
 | `bash` | `manual-slop_run_powershell` |
 
-## Context Amnesia
-You operate statelessly. Each analysis starts fresh.
-Do not assume knowledge from previous analyses or sessions.
-
 ## Analysis Start Checklist (MANDATORY)
 
 Before analyzing:
+
 1. [ ] Read error output/test failure completely
 2. [ ] Identify affected files from traceback
 3. [ ] Use skeleton tools for files >50 lines (`manual-slop_py_get_skeleton`)
@@ -54,16 +57,20 @@ Before analyzing:
 ## Analysis Protocol
 
 ### 1. Understand the Error
+
 Read the provided error output, test failure, or log carefully.
 
 ### 2. Investigate
+
 Use MCP tools to understand the context:
+
 - `manual-slop_read_file` - Read relevant source files
 - `manual-slop_py_find_usages` - Search for related patterns
 - `manual-slop_search_files` - Find related files
 - `manual-slop_get_git_diff` - Check recent changes
 
 ### 3. Root Cause Analysis
+
 Provide a structured analysis:
 
 ```
@@ -86,18 +93,30 @@ Provide a structured analysis:
 ```
 
 ## Limitations
+
 - **READ-ONLY**: Do NOT modify any files
 - **ANALYSIS ONLY**: Do NOT implement fixes
 - **NO ASSUMPTIONS**: Base analysis only on provided context and tool output
 
 ## Quality Checklist
+
 - [ ] Analysis is based on actual code/file content
 - [ ] Root cause is specific, not generic
 - [ ] Evidence includes file:line references
 - [ ] Recommendations are actionable but not implemented
 
 ## Blocking Protocol
+
 If you cannot analyze the error:
+
 1. Start your response with `CANNOT ANALYZE:`
 2. Explain what information is missing
 3. List what would be needed to complete the analysis
+
+## Anti-Patterns (Avoid)
+
+- Do NOT implement fixes - analysis only
+- Do NOT read full large files - use skeleton tools first
+- DO NOT SKIP A TEST IN PYTEST JUST BECAUSE ITS BROKEN AND HAS NO TRIVIAL SOLUTION OR FIX.
+- DO NOT SIMPLIFY A TEST JUST BECAUSE IT HAS NO TRIVIAL SOLUTION TO FIX.
+- DO NOT CREATE MOCK PATCHES TO PSEUDO API CALLS OR HOOKS BECAUSE THE APP SOURCE WAS CHANGED. ADAPT TESTS PROPERLY.

.opencode/commands/conductor-setup.md

@@ -24,7 +24,7 @@ Bootstrap the session with full conductor context. Run this at session start.
    - Identify the track with `[~]` in-progress tasks
 
 3. **Check Session Context:**
-   - Read `TASKS.md` if it exists — check for IN_PROGRESS or BLOCKED tasks
+   - Read `conductor/tracks.md` if it exists — check for IN_PROGRESS or BLOCKED tasks
    - Read last 3 entries in `JOURNAL.md` for recent activity
    - Run `git log --oneline -10` for recent commits
 

.opencode/commands/conductor-status.md

@@ -20,7 +20,7 @@ Display comprehensive status of the conductor system.
    - Read `plan.md` for task progress
    - Count completed vs total tasks
 
-3. **Check TASKS.md:**
+3. **Check conductor/tracks.md:**
    - List IN_PROGRESS tasks
    - List BLOCKED tasks
    - List pending tasks by priority
@@ -38,7 +38,7 @@ Display comprehensive status of the conductor system.
    |-------|--------|----------|--------------|
    | ... | ... | N/M tasks | ... |
 
-   ### Task Registry (TASKS.md)
+   ### Task Registry (conductor/tracks.md)
    **In Progress:**
    - [ ] Task description
 

.opencode/commands/mma-tier1-orchestrator.md

@@ -1,11 +1,33 @@
 ---
-description: Invoke Tier 1 Orchestrator for product alignment and track initialization
+description: Invoke Tier 1 Orchestrator for product alignment, high-level planning, and track initialization
 agent: tier1-orchestrator
-subtask: true
 ---
 
 $ARGUMENTS
 
 ---
 
-Invoke the Tier 1 Orchestrator with the above context. Focus on product alignment, high-level planning, and track initialization. Follow the Surgical Methodology: audit existing code before specifying, identify gaps not features, and write worker-ready tasks.
+## Context
+
+You are now acting as Tier 1 Orchestrator.
+
+### Primary Responsibilities
+- Product alignment and strategic planning
+- Track initialization (`/conductor-new-track`)
+- Session setup (`/conductor-setup`)
+- Delegate execution to Tier 2 Tech Lead
+
+### The Surgical Methodology (MANDATORY)
+
+1. **AUDIT BEFORE SPECIFYING**: Never write a spec without first reading actual code using MCP tools. Document existing implementations with file:line references.
+
+2. **IDENTIFY GAPS, NOT FEATURES**: Frame requirements around what's MISSING.
+
+3. **WRITE WORKER-READY TASKS**: Each task must specify WHERE/WHAT/HOW/SAFETY.
+
+4. **REFERENCE ARCHITECTURE DOCS**: Link to `docs/guide_*.md` sections.
+
+### Limitations
+- READ-ONLY: Do NOT write code or edit files (except track spec/plan/metadata)
+- Do NOT execute tracks — delegate to Tier 2
+- Do NOT implement features — delegate to Tier 3 Workers

.opencode/commands/mma-tier2-tech-lead.md

@@ -7,4 +7,67 @@ $ARGUMENTS
 
 ---
 
-Invoke the Tier 2 Tech Lead with the above context. Follow TDD protocol (Red -> Green -> Refactor), delegate implementation to Tier 3 Workers, and maintain persistent memory throughout track execution. Commit atomically per-task.
+## Context
+
+You are now acting as Tier 2 Tech Lead.
+
+### Primary Responsibilities
+- Track execution (`/conductor-implement`)
+- Architectural oversight
+- Delegate to Tier 3 Workers via Task tool
+- Delegate error analysis to Tier 4 QA via Task tool
+- Maintain persistent memory throughout track execution
+
+### Context Management
+
+**MANUAL COMPACTION ONLY** — Never rely on automatic context summarization.
+You maintain PERSISTENT MEMORY throughout track execution — do NOT apply Context Amnesia to your own session.
+
+### Pre-Delegation Checkpoint (MANDATORY)
+
+Before delegating ANY dangerous or non-trivial change to Tier 3:
+
+```
+git add .
+```
+
+**WHY**: If a Tier 3 Worker fails or incorrectly runs `git restore`, you will lose ALL prior AI iterations for that file if it wasn't staged/committed.
+
+### TDD Protocol (MANDATORY)
+
+1. **Red Phase**: Write failing tests first — CONFIRM FAILURE
+2. **Green Phase**: Implement to pass — CONFIRM PASS
+3. **Refactor Phase**: Optional, with passing tests
+
+### Commit Protocol (ATOMIC PER-TASK)
+
+After completing each task:
+1. Stage: `git add .`
+2. Commit: `feat(scope): description`
+3. Get hash: `git log -1 --format="%H"`
+4. Attach note: `git notes add -m "summary" <hash>`
+5. Update plan.md: Mark `[x]` with SHA
+6. Commit plan update: `git add plan.md && git commit -m "conductor(plan): Mark task complete"`
+
+### Delegation Pattern
+
+**Tier 3 Worker** (Task tool):
+```
+subagent_type: "tier3-worker"
+description: "Brief task name"
+prompt: |
+ WHERE: file.py:line-range
+ WHAT: specific change
+ HOW: API calls/patterns
+ SAFETY: thread constraints
+ Use 1-space indentation.
+```
+
+**Tier 4 QA** (Task tool):
+```
+subagent_type: "tier4-qa"
+description: "Analyze failure"
+prompt: |
+ [Error output]
+ DO NOT fix - provide root cause analysis only.
+```

.opencode/commands/mma-tier3-worker.md

@@ -7,4 +7,49 @@ $ARGUMENTS
 
 ---
 
-Invoke the Tier 3 Worker with the above task. Operate statelessly with context amnesia. Implement the specified change exactly as described. Use 1-space indentation for Python code. Do NOT add comments unless requested.
+## Context
+
+You are now acting as Tier 3 Worker.
+
+### Key Constraints
+
+- **STATELESS**: Context Amnesia — each task starts fresh
+- **MCP TOOLS ONLY**: Use `manual-slop_*` tools, NEVER native tools
+- **SURGICAL**: Follow WHERE/WHAT/HOW/SAFETY exactly
+- **1-SPACE INDENTATION**: For all Python code
+
+### Task Execution Protocol
+
+1. **Read Task Prompt**: Identify WHERE/WHAT/HOW/SAFETY
+2. **Use Skeleton Tools**: For files >50 lines, use `manual-slop_py_get_skeleton` or `manual-slop_get_file_summary`
+3. **Implement Exactly**: Follow specifications precisely
+4. **Verify**: Run tests if specified via `manual-slop_run_powershell`
+5. **Report**: Return concise summary (what, where, issues)
+
+### Edit MCP Tools (USE THESE - BAN NATIVE EDIT)
+
+| Native Tool | MCP Tool |
+|-------------|----------|
+| `edit` | `manual-slop_edit_file` (find/replace, preserves indentation) |
+| `edit` | `manual-slop_py_update_definition` (replace function/class) |
+| `edit` | `manual-slop_set_file_slice` (replace line range) |
+| `edit` | `manual-slop_py_set_signature` (replace signature only) |
+| `edit` | `manual-slop_py_set_var_declaration` (replace variable) |
+
+**CRITICAL**: The native `edit` tool DESTROYS 1-space indentation. ALWAYS use MCP tools.
+
+### Blocking Protocol
+
+If you cannot complete the task:
+
+1. Start response with `BLOCKED:`
+2. Explain exactly why you cannot proceed
+3. List what information or changes would unblock you
+4. Do NOT attempt partial implementations that break the build
+
+### Code Style (Python)
+
+- 1-space indentation
+- NO COMMENTS unless explicitly requested
+- Type hints where appropriate
+- Internal methods/variables prefixed with underscore

.opencode/commands/mma-tier4-qa.md

@@ -1,5 +1,5 @@
 ---
-description: Invoke Tier 4 QA for error analysis and diagnostics
+description: Invoke Tier 4 QA Agent for error analysis
 agent: tier4-qa
 ---
 
@@ -7,4 +7,69 @@ $ARGUMENTS
 
 ---
 
-Invoke the Tier 4 QA Agent with the above context. Analyze errors, summarize logs, or verify tests. Provide root cause analysis with file:line evidence. DO NOT implement fixes - analysis only.
+## Context
+
+You are now acting as Tier 4 QA Agent.
+
+### Key Constraints
+
+- **STATELESS**: Context Amnesia — each analysis starts fresh
+- **READ-ONLY**: Do NOT modify any files
+- **ANALYSIS ONLY**: Do NOT implement fixes
+
+### Read-Only MCP Tools (USE THESE)
+
+| Native Tool | MCP Tool |
+|-------------|----------|
+| `read` | `manual-slop_read_file` |
+| `glob` | `manual-slop_search_files` or `manual-slop_list_directory` |
+| `grep` | `manual-slop_py_find_usages` |
+| - | `manual-slop_get_file_summary` (heuristic summary) |
+| - | `manual-slop_py_get_code_outline` (classes/functions with line ranges) |
+| - | `manual-slop_py_get_skeleton` (signatures + docstrings only) |
+| - | `manual-slop_py_get_definition` (specific function/class source) |
+| - | `manual-slop_get_git_diff` (file changes) |
+| - | `manual-slop_get_file_slice` (read specific line range) |
+
+### Analysis Protocol
+
+1. **Read Error Completely**: Understand the full error/test failure
+2. **Identify Affected Files**: Parse traceback for file:line references
+3. **Use Skeleton Tools**: For files >50 lines, use `manual-slop_py_get_skeleton` first
+4. **Announce**: "Analyzing: [error summary]"
+
+### Structured Output Format
+
+```
+## Error Analysis
+
+### Summary
+[One-sentence description of the error]
+
+### Root Cause
+[Detailed explanation of why the error occurred]
+
+### Evidence
+[File:line references supporting the analysis]
+
+### Impact
+[What functionality is affected]
+
+### Recommendations
+[Suggested fixes or next steps - but DO NOT implement them]
+```
+
+### Quality Checklist
+
+- [ ] Analysis based on actual code/file content
+- [ ] Root cause is specific, not generic
+- [ ] Evidence includes file:line references
+- [ ] Recommendations are actionable but not implemented
+
+### Blocking Protocol
+
+If you cannot analyze the error:
+
+1. Start response with `CANNOT ANALYZE:`
+2. Explain what information is missing
+3. List what would be needed to complete the analysis

AGENTS.md

@@ -1,5 +1,9 @@
 # Manual Slop - OpenCode Configuration
 
+## MCP TOOL PARAMETERS - CRITICAL
+- **ALWAYS use snake_case**: `old_string`, `new_string`, `replace_all`
+- **NEVER use camelCase**: `oldString`, `newString`, `replaceAll`
+
 ## Project Overview
 
 **Manual Slop** is a local GUI application designed as an experimental, "manual" AI coding assistant. It allows users to curate and send context (files, screenshots, and discussion history) to AI APIs (Gemini and Anthropic). The AI can then execute PowerShell scripts within the project directory to modify files, requiring explicit user confirmation before execution.
@@ -41,7 +45,8 @@
 ## Session Startup Checklist
 
 At the start of each session:
-1. **Check TASKS.md** - look for IN_PROGRESS or BLOCKED tracks
+
+1. **Check ./condcutor/tracks.md** - look for IN_PROGRESS or BLOCKED tracks
 2. **Review recent JOURNAL.md entries** - scan last 2-3 entries for context
 3. **Run `/conductor-setup`** - load full context
 4. **Run `/conductor-status`** - get overview
@@ -49,6 +54,7 @@ At the start of each session:
 ## Conductor System
 
 The project uses a spec-driven track system in `conductor/`:
+
 - **Tracks**: `conductor/tracks/{name}_{YYYYMMDD}/` - spec.md, plan.md, metadata.json
 - **Workflow**: `conductor/workflow.md` - full task lifecycle and TDD protocol
 - **Tech Stack**: `conductor/tech-stack.md` - technology constraints
@@ -66,15 +72,17 @@ Tier 4: QA             - stateless error analysis, no fixes
 ## Architecture Fallback
 
 When uncertain about threading, event flow, data structures, or module interactions, consult:
+
 - **docs/guide_architecture.md**: Thread domains, event system, AI client, HITL mechanism
 - **docs/guide_tools.md**: MCP Bridge security, 26-tool inventory, Hook API endpoints
 - **docs/guide_mma.md**: Ticket/Track data structures, DAG engine, ConductorEngine
 - **docs/guide_simulations.md**: live_gui fixture, Puppeteer pattern, verification
+- **docs/guide_meta_boundary.md**: Clarification of ai agent tools making the application vs the application itself.
 
 ## Development Workflow
 
 1. Run `/conductor-setup` to load session context
-2. Pick active track from `TASKS.md` or `/conductor-status`
+2. Pick active track from `./condcutor/tracks.md` or `/conductor-status`
 3. Run `/conductor-implement` to resume track execution
 4. Follow TDD: Red (failing tests) -> Green (pass) -> Refactor
 5. Delegate implementation to Tier 3 Workers, errors to Tier 4 QA
@@ -94,6 +102,7 @@ When uncertain about threading, event flow, data structures, or module interacti
 - **IMPORTANT**: DO NOT ADD ***ANY*** COMMENTS unless asked
 - Use 1-space indentation for Python code
 - Use type hints where appropriate
+
 ## Code Style
 
 - **IMPORTANT**: DO NOT ADD ***ANY*** COMMENTS unless asked
@@ -108,19 +117,7 @@ The native `Edit` tool DESTROYS 1-space indentation and converts to 4-space.
 **NEVER use native `edit` tool on Python files.**
 
 Instead, use Manual Slop MCP tools:
+
 - `manual-slop_py_update_definition` - Replace function/class
 - `manual-slop_set_file_slice` - Replace line range
 - `manual-slop_py_set_signature` - Replace signature only
-
-Or use Python subprocess with `newline=''` to preserve line endings:
-```python
-python -c "
-with open('file.py', 'r', encoding='utf-8', newline='') as f:
-    content = f.read()
-content = content.replace(old, new)
-with open('file.py', 'w', encoding='utf-8', newline='') as f:
-    f.write(content)
-"
-```
-
-## Quality Gates

CLAUDE.md

@@ -3,6 +3,10 @@
 
 This file provides guidance to Claude Code when working with this repository.
 
+## MCP TOOL PARAMETERS - CRITICAL
+- **ALWAYS use snake_case**: `old_string`, `new_string`, `replace_all`
+- **NEVER use camelCase**: `oldString`, `newString`, `replaceAll`
+
 ## Critical Context (Read First)
 - **Tech Stack**: Python 3.11+, Dear PyGui / ImGui, FastAPI, Uvicorn
 - **Main File**: `gui_2.py` (primary GUI), `ai_client.py` (multi-provider LLM abstraction)
@@ -80,7 +84,7 @@ uv run python scripts\claude_mma_exec.py --role tier4-qa "Error analysis prompt"
 
 ## Development Workflow
 1. Run `/conductor-setup` to load session context
-2. Pick active track from `TASKS.md` or `/conductor-status`
+2. Pick active track from `conductor/tracks.md` or `/conductor-status`
 3. Run `/conductor-implement` to resume track execution
 4. Follow TDD: Red (failing tests) → Green (pass) → Refactor
 5. Delegate implementation to Tier 3 Workers, errors to Tier 4 QA
@@ -112,7 +116,7 @@ Update JOURNAL.md after:
 Format: What/Why/How/Issues/Result structure
 
 ## Task Management Integration
-- **TASKS.md**: Quick-read pointer to active conductor tracks
+- **conductor/tracks.md**: Quick-read pointer to active conductor tracks
 - **conductor/tracks/*/plan.md**: Detailed task state (source of truth)
 - **JOURNAL.md**: Completed work history with `|TASK:ID|` tags
 - **ERRORS.md**: P0/P1 error tracking

CONDUCTOR.md

@@ -1,511 +0,0 @@
-# CONDUCTOR.md
-<!-- Generated by Claude Conductor v2.0.0 -->
-
-> _Read me first. Every other doc is linked below._
-
-## Critical Context (Read First)
-- **Tech Stack**: [List core technologies]
-- **Main File**: [Primary code file and line count]
-- **Core Mechanic**: [One-line description]
-- **Key Integration**: [Important external services]
-- **Platform Support**: [Deployment targets]
-- **DO NOT**: [Critical things to avoid]
-
-## Table of Contents
-1. [Architecture](ARCHITECTURE.md) - Tech stack, folder structure, infrastructure
-2. [Design Tokens](DESIGN.md) - Colors, typography, visual system
-3. [UI/UX Patterns](UIUX.md) - Components, interactions, accessibility
-4. [Runtime Config](CONFIG.md) - Environment variables, feature flags
-5. [Data Model](DATA_MODEL.md) - Database schema, entities, relationships
-6. [API Contracts](API.md) - Endpoints, request/response formats, auth
-7. [Build & Release](BUILD.md) - Build process, deployment, CI/CD
-8. [Testing Guide](TEST.md) - Test strategies, E2E scenarios, coverage
-9. [Operational Playbooks](PLAYBOOKS/DEPLOY.md) - Deployment, rollback, monitoring
-10. [Contributing](CONTRIBUTING.md) - Code style, PR process, conventions
-11. [Error Ledger](ERRORS.md) - Critical P0/P1 error tracking
-12. [Task Management](TASKS.md) - Active tasks, phase tracking, context preservation
-
-## Quick Reference
-**Main Constants**: `[file:lines]` - Description  
-**Core Class**: `[file:lines]` - Description  
-**Key Function**: `[file:lines]` - Description  
-[Include 10-15 most accessed code locations]
-
-## Current State
-- [x] Feature complete
-- [ ] Feature in progress
-- [ ] Feature planned
-[Track active work]
-
-## Development Workflow
-[5-6 steps for common workflow]
-
-## Task Templates
-### 1. [Common Task Name]
-1. Step with file:line reference
-2. Step with specific action
-3. Test step
-4. Documentation update
-
-[Include 3-5 templates]
-
-## Anti-Patterns (Avoid These)
-❌ **Don't [action]** - [Reason]  
-[List 5-6 critical mistakes]
-
-## Version History
-- **v1.0.0** - Initial release
-- **v1.1.0** - Feature added (see JOURNAL.md YYYY-MM-DD)  
-[Link major versions to journal entries]
-
-## Continuous Engineering Journal <!-- do not remove -->
-
-Claude, keep an ever-growing changelog in [`JOURNAL.md`](JOURNAL.md).
-
-### What to Journal
-- **Major changes**: New features, significant refactors, API changes
-- **Bug fixes**: What broke, why, and how it was fixed
-- **Frustration points**: Problems that took multiple attempts to solve
-- **Design decisions**: Why we chose one approach over another
-- **Performance improvements**: Before/after metrics
-- **User feedback**: Notable issues or requests
-- **Learning moments**: New techniques or patterns discovered
-
-### Journal Format
-\```
-## YYYY-MM-DD HH:MM
-
-### [Short Title]
-- **What**: Brief description of the change
-- **Why**: Reason for the change
-- **How**: Technical approach taken
-- **Issues**: Any problems encountered
-- **Result**: Outcome and any metrics
-
-### [Short Title] |ERROR:ERR-YYYY-MM-DD-001|
-- **What**: Critical P0/P1 error description
-- **Why**: Root cause analysis
-- **How**: Fix implementation
-- **Issues**: Debugging challenges
-- **Result**: Resolution and prevention measures
-
-### [Task Title] |TASK:TASK-YYYY-MM-DD-001|
-- **What**: Task implementation summary
-- **Why**: Part of [Phase Name] phase
-- **How**: Technical approach and key decisions
-- **Issues**: Blockers encountered and resolved
-- **Result**: Task completed, findings documented in ARCHITECTURE.md
-\```
-
-### Compaction Rule
-When `JOURNAL.md` exceeds **500 lines**:
-1. Claude summarizes the oldest half into `JOURNAL_ARCHIVE/<year>-<month>.md`
-2. Remaining entries stay in `JOURNAL.md` so the file never grows unbounded
-
-> ⚠️ Claude must NEVER delete raw history—only move & summarize.
-
-### 2. ARCHITECTURE.md
-**Purpose**: System design, tech stack decisions, and code structure with line numbers.
-
-**Required Elements**:
-- Technology stack listing
-- Directory structure diagram
-- Key architectural decisions with rationale
-- Component architecture with exact line numbers
-- System flow diagram (ASCII art)
-- Common patterns section
-- Keywords for search optimization
-
-**Line Number Format**:
-\```
-#### ComponentName Structure <!-- #component-anchor -->
-\```typescript
-// Major classes with exact line numbers
-class MainClass { /* lines 100-500 */ }    // <!-- #main-class -->
-class Helper { /* lines 501-600 */ }       // <!-- #helper-class -->
-\```
-\```
-
-### 3. DESIGN.md
-**Purpose**: Visual design system, styling, and theming documentation.
-
-**Required Sections**:
-- Typography system
-- Color palette (with hex values)
-- Visual effects specifications
-- Character/entity design
-- UI/UX component styling
-- Animation system
-- Mobile design considerations
-- Accessibility guidelines
-- Keywords section
-
-### 4. DATA_MODEL.md
-**Purpose**: Database schema, application models, and data structures.
-
-**Required Elements**:
-- Database schema (SQL)
-- Application data models (TypeScript/language interfaces)
-- Validation rules
-- Common queries
-- Data migration history
-- Keywords for entities
-
-### 5. API.md
-**Purpose**: Complete API documentation with examples.
-
-**Structure for Each Endpoint**:
-\```
-### Endpoint Name
-
-\```http
-METHOD /api/endpoint
-\```
-
-#### Request
-\```json
-{
-  "field": "type"
-}
-\```
-
-#### Response
-\```json
-{
-  "field": "value"
-}
-\```
-
-#### Details
-- **Rate limit**: X requests per Y seconds
-- **Auth**: Required/Optional
-- **Notes**: Special considerations
-\```
-
-### 6. CONFIG.md
-**Purpose**: Runtime configuration, environment variables, and settings.
-
-**Required Sections**:
-- Environment variables (required and optional)
-- Application configuration constants
-- Feature flags
-- Performance tuning settings
-- Security configuration
-- Common patterns for configuration changes
-
-### 7. BUILD.md
-**Purpose**: Build process, deployment, and CI/CD documentation.
-
-**Include**:
-- Prerequisites
-- Build commands
-- CI/CD pipeline configuration
-- Deployment steps
-- Rollback procedures
-- Troubleshooting guide
-
-### 8. TEST.md
-**Purpose**: Testing strategies, patterns, and examples.
-
-**Sections**:
-- Test stack and tools
-- Running tests commands
-- Test structure
-- Coverage goals
-- Common test patterns
-- Debugging tests
-
-### 9. UIUX.md
-**Purpose**: Interaction patterns, user flows, and behavior specifications.
-
-**Cover**:
-- Input methods
-- State transitions
-- Component behaviors
-- User flows
-- Accessibility patterns
-- Performance considerations
-
-### 10. CONTRIBUTING.md
-**Purpose**: Guidelines for contributors.
-
-**Include**:
-- Code of conduct
-- Development setup
-- Code style guide
-- Commit message format
-- PR process
-- Common patterns
-
-### 11. PLAYBOOKS/DEPLOY.md
-**Purpose**: Step-by-step operational procedures.
-
-**Format**:
-- Pre-deployment checklist
-- Deployment steps (multiple options)
-- Post-deployment verification
-- Rollback procedures
-- Troubleshooting
-
-### 12. ERRORS.md (Critical Error Ledger)
-**Purpose**: Track and resolve P0/P1 critical errors with full traceability.
-
-**Required Structure**:
-\```
-# Critical Error Ledger <!-- auto-maintained -->
-
-## Schema
-| ID | First seen | Status | Severity | Affected area | Link to fix |
-|----|------------|--------|----------|---------------|-------------|
-
-## Active Errors
-[New errors added here, newest first]
-
-## Resolved Errors
-[Moved here when fixed, with links to fixes]
-\```
-
-**Error ID Format**: `ERR-YYYY-MM-DD-001` (increment for multiple per day)
-
-**Severity Definitions**:
-- **P0**: Complete outage, data loss, security breach
-- **P1**: Major functionality broken, significant performance degradation
-- **P2**: Minor functionality (not tracked in ERRORS.md)
-- **P3**: Cosmetic issues (not tracked in ERRORS.md)
-
-**Claude's Error Logging Process**:
-1. When P0/P1 error occurs, immediately add to Active Errors
-2. Create corresponding JOURNAL.md entry with details
-3. When resolved:
-   - Move to Resolved Errors section
-   - Update status to "resolved"
-   - Add commit hash and PR link
-   - Add `|ERROR:<ID>|` tag to JOURNAL.md entry
-   - Link back to JOURNAL entry from ERRORS.md
-
-### 13. TASKS.md (Active Task Management)
-**Purpose**: Track ongoing work with phase awareness and context preservation between sessions.
-
-**IMPORTANT**: TASKS.md complements Claude's built-in todo system - it does NOT replace it:
-- Claude's todos: For immediate task tracking within a session
-- TASKS.md: For preserving context and state between sessions
-
-**Required Structure**:
-```
-# Task Management
-
-## Active Phase
-**Phase**: [High-level project phase name]
-**Started**: YYYY-MM-DD
-**Target**: YYYY-MM-DD
-**Progress**: X/Y tasks completed
-
-## Current Task
-**Task ID**: TASK-YYYY-MM-DD-NNN
-**Title**: [Descriptive task name]
-**Status**: PLANNING | IN_PROGRESS | BLOCKED | TESTING | COMPLETE
-**Started**: YYYY-MM-DD HH:MM
-**Dependencies**: [List task IDs this depends on]
-
-### Task Context
-<!-- Critical information needed to resume this task -->
-- **Previous Work**: [Link to related tasks/PRs]
-- **Key Files**: [Primary files being modified with line ranges]
-- **Environment**: [Specific config/versions if relevant]
-- **Next Steps**: [Immediate actions when resuming]
-
-### Findings & Decisions
-- **FINDING-001**: [Discovery that affects approach]
-- **DECISION-001**: [Technical choice made] → Link to ARCHITECTURE.md
-- **BLOCKER-001**: [Issue preventing progress] → Link to resolution
-
-### Task Chain
-1. ✅ [Completed prerequisite task] (TASK-YYYY-MM-DD-001)
-2. 🔄 [Current task] (CURRENT)
-3. ⏳ [Next planned task]
-4. ⏳ [Future task in phase]
-```
-
-**Task Management Rules**:
-1. **One Active Task**: Only one task should be IN_PROGRESS at a time
-2. **Context Capture**: Before switching tasks, capture all context needed to resume
-3. **Findings Documentation**: Record unexpected discoveries that impact the approach
-4. **Decision Linking**: Link architectural decisions to ARCHITECTURE.md
-5. **Completion Trigger**: When task completes:
-   - Generate JOURNAL.md entry with task summary
-   - Archive task details to TASKS_ARCHIVE/YYYY-MM/TASK-ID.md
-   - Load next task from chain or prompt for new phase
-
-**Task States**:
-- **PLANNING**: Defining approach and breaking down work
-- **IN_PROGRESS**: Actively working on implementation
-- **BLOCKED**: Waiting on external dependency or decision
-- **TESTING**: Implementation complete, validating functionality
-- **COMPLETE**: Task finished and documented
-
-**Integration with Journal**:
-- Each completed task auto-generates a journal entry
-- Journal references task ID for full context
-- Critical findings promoted to relevant documentation
-
-## Documentation Optimization Rules
-
-### 1. Line Number Anchors
-- Add exact line numbers for every class, function, and major code section
-- Format: `**Class Name (Lines 100-200)**`
-- Add HTML anchors: `<!-- #class-name -->`
-- Update when code structure changes significantly
-
-### 2. Quick Reference Card
-- Place in CLAUDE.md after Table of Contents
-- Include 10-15 most common code locations
-- Format: `**Feature**: `file:lines` - Description`
-
-### 3. Current State Tracking
-- Use checkbox format in CLAUDE.md
-- `- [x] Completed feature`
-- `- [ ] In-progress feature`
-- Update after each work session
-
-### 4. Task Templates
-- Provide 3-5 step-by-step workflows
-- Include specific line numbers
-- Reference files that need updating
-- Add test/verification steps
-
-### 5. Keywords Sections
-- Add to each major .md file
-- List alternative search terms
-- Format: `## Keywords <!-- #keywords -->`
-- Include synonyms and related terms
-
-### 6. Anti-Patterns
-- Use ❌ emoji for clarity
-- Explain why each is problematic
-- Include 5-6 critical mistakes
-- Place prominently in CLAUDE.md
-
-### 7. System Flow Diagrams
-- Use ASCII art for simplicity
-- Show data/control flow
-- Keep visual and readable
-- Place in ARCHITECTURE.md
-
-### 8. Common Patterns
-- Add to relevant docs (CONFIG.md, ARCHITECTURE.md)
-- Show exact code changes needed
-- Include before/after examples
-- Reference specific functions
-
-### 9. Version History
-- Link to JOURNAL.md entries
-- Format: `v1.0.0 - Feature (see JOURNAL.md YYYY-MM-DD)`
-- Track major changes only
-
-### 10. Cross-Linking
-- Link between related sections
-- Use relative paths: `[Link](./FILE.md#section)`
-- Ensure bidirectional linking where appropriate
-
-## Journal System Setup
-
-### JOURNAL.md Structure
-\```
-# Engineering Journal
-
-## YYYY-MM-DD HH:MM
-
-### [Descriptive Title]
-- **What**: Brief description of the change
-- **Why**: Reason for the change
-- **How**: Technical approach taken
-- **Issues**: Any problems encountered
-- **Result**: Outcome and any metrics
-
----
-
-[Entries continue chronologically]
-\```
-
-### Journal Best Practices
-1. **Entry Timing**: Add entry immediately after significant work
-2. **Detail Level**: Include enough detail to understand the change months later
-3. **Problem Documentation**: Especially document multi-attempt solutions
-4. **Learning Moments**: Capture new techniques discovered
-5. **Metrics**: Include performance improvements, time saved, etc.
-
-### Archive Process
-When JOURNAL.md exceeds 500 lines:
-1. Create `JOURNAL_ARCHIVE/` directory
-2. Move oldest 250 lines to `JOURNAL_ARCHIVE/YYYY-MM.md`
-3. Add summary header to archive file
-4. Keep recent entries in main JOURNAL.md
-
-## Implementation Steps
-
-### Phase 1: Initial Setup (30-60 minutes)
-1. **Create CLAUDE.md** with all required sections
-2. **Fill Critical Context** with 6 essential facts
-3. **Create Table of Contents** with placeholder links
-4. **Add Quick Reference** with top 10-15 code locations
-5. **Set up Journal section** with formatting rules
-
-### Phase 2: Core Documentation (2-4 hours)
-1. **Create each .md file** from the list above
-2. **Add Keywords section** to each file
-3. **Cross-link between files** where relevant
-4. **Add line numbers** to code references
-5. **Create PLAYBOOKS/ directory** with DEPLOY.md
-6. **Create ERRORS.md** with schema table
-
-### Phase 3: Optimization (1-2 hours)
-1. **Add Task Templates** to CLAUDE.md
-2. **Create ASCII system flow** in ARCHITECTURE.md
-3. **Add Common Patterns** sections
-4. **Document Anti-Patterns**
-5. **Set up Version History**
-
-### Phase 4: First Journal Entry
-Create initial JOURNAL.md entry documenting the setup:
-\```
-## YYYY-MM-DD HH:MM
-
-### Documentation Framework Implementation
-- **What**: Implemented CLAUDE.md modular documentation system
-- **Why**: Improve AI navigation and code maintainability
-- **How**: Split monolithic docs into focused modules with cross-linking
-- **Issues**: None - clean implementation
-- **Result**: [Number] documentation files created with full cross-referencing
-\```
-
-## Maintenance Guidelines
-
-### Daily
-- Update JOURNAL.md with significant changes
-- Mark completed items in Current State
-- Update line numbers if major refactoring
-
-### Weekly
-- Review and update Quick Reference section
-- Check for broken cross-links
-- Update Task Templates if workflows change
-
-### Monthly
-- Review Keywords sections for completeness
-- Update Version History
-- Check if JOURNAL.md needs archiving
-
-### Per Release
-- Update Version History in CLAUDE.md
-- Create comprehensive JOURNAL.md entry
-- Review all documentation for accuracy
-- Update Current State checklist
-
-## Benefits of This System
-
-1. **AI Efficiency**: Claude can quickly navigate to exact code locations
-2. **Modularity**: Easy to update specific documentation without affecting others
-3. **Discoverability**: New developers/AI can quickly understand the project
-4. **History Tracking**: Complete record of changes and decisions
-5. **Task Automation**: Templates reduce repetitive instructions
-6. **Error Prevention**: Anti-patterns prevent common mistakes

JOURNAL.md

@@ -26,7 +26,7 @@
 - **What**: Per-agent filtering for MMA observability panels (comms, tool calls, discussion, token budget)
 - **Why**: All panels are global/session-scoped; in MMA mode with 4 tiers, data from all agents mixes. No way to isolate what a specific tier is doing.
 - **Gap**: `_comms_log` and `_tool_log` have no tier/agent tag. `mma_streams` stream_id is the only per-agent key that exists.
-- **See**: TASKS.md for full audit and implementation intent.
+- **See**: conductor/tracks.md for full audit and implementation intent.
 
 ---
 
@@ -42,7 +42,7 @@
 - **More Tracks**: Initialized 'tech_debt_and_test_cleanup_20260302' and 'conductor_workflow_improvements_20260302' to harden TDD discipline, resolve test tech debt (false-positives, dupes), and mandate AST-based codebase auditing.
 - **Final Track**: Initialized 'architecture_boundary_hardening_20260302' to fix the GUI HITL bypass allowing direct AST mutations, patch token bloat in `mma_exec.py`, and implement cascading blockers in `dag_engine.py`.
 - **Testing Consolidation**: Initialized 'testing_consolidation_20260302' track to standardize simulation testing workflows around the pytest `live_gui` fixture and eliminate redundant `subprocess.Popen` wrappers.
-- **Dependency Order**: Added an explicit 'Track Dependency Order' execution guide to `TASKS.md` to ensure safe progression through the accumulated tech debt.
+- **Dependency Order**: Added an explicit 'Track Dependency Order' execution guide to `conductor/tracks.md` to ensure safe progression through the accumulated tech debt.
 - **Documentation**: Added guide_meta_boundary.md to explicitly clarify the difference between the Application's strict-HITL environment and the autonomous Meta-Tooling environment, helping future Tiers avoid feature bleed.
 - **Heuristics & Backlog**: Added Data-Oriented Design and Immediate Mode architectural heuristics (inspired by Muratori/Acton) to product-guidelines.md. Logged future decoupling and robust parsing tracks to a 'Future Backlog' in TASKS.md.
 

MMA_UX_SPEC.md

@@ -1,36 +0,0 @@
-# MMA Observability & UX Specification
-
-## 1. Goal
-Implement the visible surface area of the 4-Tier Hierarchical Multi-Model Architecture within `gui_2.py`. This ensures the user can monitor, control, and debug the multi-agent execution flow.
-
-## 2. Core Components
-
-### 2.1 MMA Dashboard Panel
-- **Visibility:** A new dockable panel named "MMA Dashboard".
-- **Track Status:** Display the current active `Track` ID and overall progress (e.g., "3/10 Tickets Complete").
-- **Ticket DAG Visualization:** A list or simple graph representing the `Ticket` queue.
-    - Each ticket shows: `ID`, `Target`, `Status` (Pending, Running, Paused, Complete, Blocked).
-    - Visual indicators for dependencies (e.g., indented or linked).
-
-### 2.2 The Execution Clutch (HITL)
-- **Step Mode Toggle:** A global or per-track checkbox to enable "Step Mode".
-- **Pause Points:**
-    - **Pre-Execution:** When a Tier 3 worker generates a tool call (e.g., `write_file`), the engine pauses.
-    - **UI Interaction:** The GUI displays the proposed script/change and provides:
-        - `[Approve]`: Proceed with execution.
-        - `[Edit Payload]`: Open the Memory Mutator.
-        - `[Abort]`: Mark the ticket as Blocked/Cancelled.
-- **Visual Feedback:** Tactile/Arcade-style blinking or color changes when the engine is "Paused for HITL".
-
-### 2.3 Memory Mutator (The "Debug" Superpower)
-- **Functionality:** A modal or dedicated text area that allows the user to edit the raw JSON conversation history of a paused worker.
-- **Use Case:** Fixing AI hallucinations or providing specific guidance mid-turn without restarting the context window.
-- **Integration:** After editing, the "Approve" button sends the *modified* history back to the engine.
-
-### 2.4 Tiered Metrics & Logs
-- **Observability:** Show which model (Tier 1, 2, 3, or 4) is currently active.
-- **Sub-Agent Logs:** Provide quick links to open the timestamped log files generated by `mma_exec.py`.
-
-## 3. Technical Integration
-- **Event Bus:** Use the existing `AsyncEventQueue` to push `StateUpdateEvents` from the `ConductorEngine` to the GUI.
-- **Non-Blocking:** Ensure the UI remains responsive (FPS > 60) even when multiple tickets are processing or the engine is waiting for user input.

Readme.md

@@ -1,14 +1,56 @@
-# Sloppy
+# Manual Slop
 
 ![img](./gallery/splash.png)
 
-A GUI orchestrator for local LLM-driven coding sessions. Manual Slop bridges high-latency AI reasoning with a low-latency ImGui render loop via a thread-safe asynchronous pipeline, ensuring every AI-generated payload passes through a human-auditable gate before execution.
+A high-density GUI orchestrator for local LLM-driven coding sessions. Manual Slop bridges high-latency AI reasoning with a low-latency ImGui render loop via a thread-safe asynchronous pipeline, ensuring every AI-generated payload passes through a human-auditable gate before execution.
 
-**Tech Stack**: Python 3.11+, Dear PyGui / ImGui, FastAPI, Uvicorn
+**Design Philosophy**: Full manual control over vendor API metrics, agent capabilities, and context memory usage. High information density, tactile interactions, and explicit confirmation for destructive actions.
-**Providers**: Gemini API, Anthropic API, DeepSeek, Gemini CLI (headless)
+
+**Tech Stack**: Python 3.11+, Dear PyGui / ImGui Bundle, FastAPI, Uvicorn, tree-sitter
+**Providers**: Gemini API, Anthropic API, DeepSeek, Gemini CLI (headless), MiniMax
 **Platform**: Windows (PowerShell) — single developer, local use
 
-![img](./gallery/python_2026-03-01_23-45-34.png)
+![img](./gallery/python_2026-03-11_00-37-21.png)
+
+---
+
+## Key Features
+
+### Multi-Provider Integration
+- **Gemini SDK**: Server-side context caching with TTL management, automatic cache rebuilding at 90% TTL
+- **Anthropic**: Ephemeral prompt caching with 4-breakpoint system, automatic history truncation at 180K tokens
+- **DeepSeek**: Dedicated SDK for code-optimized reasoning
+- **Gemini CLI**: Headless adapter with full functional parity, synchronous HITL bridge
+- **MiniMax**: Alternative provider support
+
+### 4-Tier MMA Orchestration
+Hierarchical task decomposition with specialized models and strict token firewalling:
+- **Tier 1 (Orchestrator)**: Product alignment, epic → tracks
+- **Tier 2 (Tech Lead)**: Track → tickets (DAG), persistent context
+- **Tier 3 (Worker)**: Stateless TDD implementation, context amnesia
+- **Tier 4 (QA)**: Stateless error analysis, no fixes
+
+### Strict Human-in-the-Loop (HITL)
+- **Execution Clutch**: All destructive actions suspend on `threading.Condition` pending GUI approval
+- **Three Dialog Types**: ConfirmDialog (scripts), MMAApprovalDialog (steps), MMASpawnApprovalDialog (workers)
+- **Editable Payloads**: Review, modify, or reject any AI-generated content before execution
+
+### 26 MCP Tools with Sandboxing
+Three-layer security model: Allowlist Construction → Path Validation → Resolution Gate
+- **File I/O**: read, list, search, slice, edit, tree
+- **AST-Based (Python)**: skeleton, outline, definition, signature, class summary, docstring
+- **Analysis**: summary, git diff, find usages, imports, syntax check, hierarchy
+- **Network**: web search, URL fetch
+- **Runtime**: UI performance metrics
+
+### Parallel Tool Execution
+Multiple independent tool calls within a single AI turn execute concurrently via `asyncio.gather`, significantly reducing latency.
+
+### AST-Based Context Management
+- **Skeleton View**: Signatures + docstrings, bodies replaced with `...`
+- **Curated View**: Preserves `@core_logic` decorated functions and `[HOT]` comment blocks
+- **Targeted View**: Extracts only specified symbols and their dependencies
+- **Heuristic Summaries**: Token-efficient structural descriptions without AI calls
 
 ---
 
@@ -26,35 +68,12 @@ The **MMA (Multi-Model Agent)** system decomposes epics into tracks, tracks into
 
 | Guide | Scope |
 |---|---|
+| [Readme](./docs/Readme.md) | Documentation index, GUI panel reference, configuration files, environment variables |
 | [Architecture](./docs/guide_architecture.md) | Threading model, event system, AI client multi-provider architecture, HITL mechanism, comms logging |
-| [Tools & IPC](./docs/guide_tools.md) | MCP Bridge security model, all 26 native tools, Hook API endpoints, ApiHookClient reference, shell runner |
+| [Tools & IPC](./docs/guide_tools.md) | MCP Bridge 3-layer security, 26 tool inventory, Hook API endpoints, ApiHookClient reference, shell runner |
-| [MMA Orchestration](./docs/guide_mma.md) | 4-tier hierarchy, Ticket/Track data structures, DAG engine, ConductorEngine execution loop, worker lifecycle |
+| [MMA Orchestration](./docs/guide_mma.md) | 4-tier hierarchy, Ticket/Track data structures, DAG engine, ConductorEngine, worker lifecycle, abort propagation |
-| [Simulations](./docs/guide_simulations.md) | `live_gui` fixture, Puppeteer pattern, mock provider, visual verification patterns, ASTParser / summarizer |
+| [Simulations](./docs/guide_simulations.md) | `live_gui` fixture, Puppeteer pattern, mock provider, visual verification, ASTParser / summarizer |
-
+| [Meta-Boundary](./docs/guide_meta_boundary.md) | Application vs Meta-Tooling domains, inter-domain bridges, safety model separation |
----
-
-## Module Map
-
-Core implementation resides in the `src/` directory.
-
-| File | Role |
-|---|---|
-| `src/gui_2.py` | Primary ImGui interface — App class, frame-sync, HITL dialogs |
-| `src/ai_client.py` | Multi-provider LLM abstraction (Gemini, Anthropic, DeepSeek, Gemini CLI) |
-| `src/mcp_client.py` | 26 MCP tools with filesystem sandboxing and tool dispatch |
-| `src/api_hooks.py` | HookServer — REST API for external automation on `:8999` |
-| `src/api_hook_client.py` | Python client for the Hook API (used by tests and external tooling) |
-| `src/multi_agent_conductor.py` | ConductorEngine — Tier 2 orchestration loop with DAG execution |
-| `src/conductor_tech_lead.py` | Tier 2 ticket generation from track briefs |
-| `src/dag_engine.py` | TrackDAG (dependency graph) + ExecutionEngine (tick-based state machine) |
-| `src/models.py` | Ticket, Track, WorkerContext dataclasses |
-| `src/events.py` | EventEmitter, AsyncEventQueue, UserRequestEvent |
-| `src/project_manager.py` | TOML config persistence, discussion management, track state |
-| `src/session_logger.py` | JSON-L + markdown audit trails (comms, tools, CLI, hooks) |
-| `src/shell_runner.py` | PowerShell execution with timeout, env config, QA callback |
-| `src/file_cache.py` | ASTParser (tree-sitter) — skeleton and curated views |
-| `src/summarize.py` | Heuristic file summaries (imports, classes, functions) |
-| `src/outline_tool.py` | Hierarchical code outline via stdlib `ast` |
 
 ---
 
@@ -105,6 +124,151 @@ uv run pytest tests/ -v
 
 ---
 
+## MMA 4-Tier Architecture
+
+The Multi-Model Agent system uses hierarchical task decomposition with specialized models at each tier:
+
+| Tier | Role | Model | Responsibility |
+|------|------|-------|----------------|
+| **Tier 1** | Orchestrator | `gemini-3.1-pro-preview` | Product alignment, epic → tracks, track initialization |
+| **Tier 2** | Tech Lead | `gemini-3-flash-preview` | Track → tickets (DAG), architectural oversight, persistent context |
+| **Tier 3** | Worker | `gemini-2.5-flash-lite` / `deepseek-v3` | Stateless TDD implementation per ticket, context amnesia |
+| **Tier 4** | QA | `gemini-2.5-flash-lite` / `deepseek-v3` | Stateless error analysis, diagnostics only (no fixes) |
+
+**Key Principles:**
+- **Context Amnesia**: Tier 3/4 workers start with `ai_client.reset_session()` — no history bleed
+- **Token Firewalling**: Each tier receives only the context it needs
+- **Model Escalation**: Failed tickets automatically retry with more capable models
+- **WorkerPool**: Bounded concurrency (default: 4 workers) with semaphore gating
+
+---
+
+## Module by Domain
+
+### src/ — Core implementation
+
+| File | Role |
+|---|---|
+| `src/gui_2.py` | Primary ImGui interface — App class, frame-sync, HITL dialogs, event system |
+| `src/ai_client.py` | Multi-provider LLM abstraction (Gemini, Anthropic, DeepSeek, MiniMax) |
+| `src/mcp_client.py` |       26 MCP tools with filesystem sandboxing and tool dispatch |
+| `src/api_hooks.py`  |          HookServer — REST API on `127.0.0.1:8999 for external automation |
+| `src/api_hook_client.py` |       Python client for the Hook API (used by tests and external tooling) |
+| `src/multi_agent_conductor.py` |   ConductorEngine — Tier 2 orchestration loop with DAG execution  |
+| `src/conductor_tech_lead.py`  |   Tier 2 ticket generation from track briefs |
+| `src/dag_engine.py`  |       TrackDAG (dependency graph) + ExecutionEngine (tick-based state machine) |
+| `src/models.py`  |       Ticket, Track, WorkerContext, Metadata, Track state |
+| `src/events.py`  |           EventEmitter, AsyncEventQueue, UserRequestEvent |
+| `src/project_manager.py`  |       TOML config persistence, discussion management, track state |
+| `src/session_logger.py`  |       JSON-L + markdown audit trails (comms, tools, CLI, hooks) |
+| `src/shell_runner.py`  |       PowerShell execution with timeout, env config, QA callback |
+| `src/file_cache.py`  |       ASTParser (tree-sitter) — skeleton, curated, and targeted views |
+| `src/summarize.py`  |       Heuristic file summaries (imports, classes, functions) |
+| `src/outline_tool.py`  |       Hierarchical code outline via stdlib `ast` |
+| `src/performance_monitor.py`  |       FPS, frame time, CPU, input lag tracking |
+| `src/log_registry.py`  |       Session metadata persistence |
+| `src/log_pruner.py`  |       Automated log cleanup based on age and whitelist |
+| `src/paths.py`  |       Centralized path resolution with environment variable overrides |
+| `src/cost_tracker.py`  |       Token cost estimation for API calls |
+| `src/gemini_cli_adapter.py`  |       CLI subprocess adapter with session management |
+| `src/mma_prompts.py`  |       Tier-specific system prompts for MMA orchestration |
+| `src/theme_*.py` |        UI theming (dark, light modes) |
+
+Simulation modules in `simulation/`:
+| File | Role |
+|---|--- |
+| `simulation/sim_base.py` |       BaseSimulation class with setup/teardown lifecycle |
+| `simulation/workflow_sim.py` |       WorkflowSimulator — high-level GUI automation |
+| `simulation/user_agent.py` |        UserSimAgent — simulated user behavior (reading time, thinking delays) |
+
+---
+
+## Setup
+The MCP Bridge implements a three-layer security model in `mcp_client.py`:
+
+Every tool accessing the filesystem passes through `_resolve_and_check(path)` before any I/O.
+
+### Layer 1: Allowlist Construction (`configure`)
+Called by `ai_client` before each send cycle:
+1. Resets `_allowed_paths` and `_base_dirs` to empty sets
+2. Sets `_primary_base_dir` from `extra_base_dirs[0]`
+3. Iterates `file_items`, resolving paths, adding to allowlist
+4. Blacklist check: `history.toml`, `*_history.toml`, `config.toml`, `credentials.toml` are NEVER allowed
+
+### Layer 2: Path Validation (`_is_allowed`)
+Checks run in order:
+1. **Blacklist**: `history.toml`, `*_history.toml` → hard deny
+2. **Explicit allowlist**: Path in `_allowed_paths` → allow
+3. **CWD fallback**: If no base dirs, allow `cwd()` subpaths
+4. **Base containment**: Must be subpath of `_base_dirs`
+5. **Default deny**: All other paths rejected
+
+### Layer 3: Resolution Gate (`_resolve_and_check`)
+1. Convert raw path string to `Path`
+2. If not absolute, prepend `_primary_base_dir`
+3. Resolve to absolute (follows symlinks)
+4. Call `_is_allowed()`
+5. Return `(resolved_path, "")` on success or `(None, error_message)` on failure
+
+All paths are resolved (following symlinks) before comparison, preventing symlink-based traversal attacks.
+
+### Security Model
+
+The MCP Bridge implements a three-layer security model in `mcp_client.py`. Every tool accessing the filesystem passes through `_resolve_and_check(path)` before any I/O.
+
+### Layer 1: Allowlist Construction (`configure`)
+Called by `ai_client` before each send cycle:
+1. Resets `_allowed_paths` and `_base_dirs` to empty sets.
+2. Sets `_primary_base_dir` from `extra_base_dirs[0]` (resolved) or falls back to cwd().
+3. Iterates `file_items`, resolving each path to an absolute path, adding to `_allowed_paths`; its parent directory is added to `_base_dirs`.
+4. Any entries in `extra_base_dirs` that are valid directories are also added to `_base_dirs`.
+
+### Layer 2: Path Validation (`_is_allowed`)
+Checks run in this exact order:
+1. **Blacklist**: `history.toml`, `*_history.toml`, `config`, `credentials` → hard deny
+2. **Explicit allowlist**: Path in `_allowed_paths` → allow
+7. **CWD fallback**: If no base dirs, any under `cwd()` is allowed (fail-safe for projects without explicit base dirs)
+8. **Base containment**: Must be a subpath of at least one entry in `_base_dirs` (via `relative_to()`)
+9. **Default deny**: All other paths rejected
+All paths are resolved (following symlinks) before comparison, preventing symlink-based traversal attacks.
+
+### Layer 3: Resolution Gate (`_resolve_and_check`)
+Every tool call passes through this:
+1. Convert raw path string to `Path`.
+2. If not absolute, prepend `_primary_base_dir`.
+3. Resolve to absolute.
+4. Call `_is_allowed()`.
+5. Return `(resolved_path, "")` on success, `(None, error_message)` on failure
+All paths are resolved (following symlinks) before comparison, preventing symlink-based traversal attacks.
+
+---
+
+## Conductor SystemThe project uses a spec-driven track system in `conductor/` for structured development:
+
+```
+conductor/
+├── workflow.md           # Task lifecycle, TDD protocol, phase verification
+├── tech-stack.md         # Technology constraints and patterns
+├── product.md            # Product vision and guidelines
+├── product-guidelines.md # Code standards, UX principles
+└── tracks/
+    └── <track_name>_<YYYYMMDD>/
+        ├── spec.md       # Track specification
+        ├── plan.md       # Implementation plan with checkbox tasks
+        ├── metadata.json # Track metadata
+        └── state.toml    # Structured state with task list
+```
+
+**Key Concepts:**
+- **Tracks**: Self-contained implementation units with spec, plan, and state
+- **TDD Protocol**: Red (failing tests) → Green (pass) → Refactor
+- **Phase Checkpoints**: Verification gates with git notes for audit trails
+- **MMA Delegation**: Tracks are executed via the 4-tier agent hierarchy
+
+See `conductor/workflow.md` for the full development workflow.
+
+---
+
 ## Project Configuration
 
 Projects are stored as `<name>.toml` files. The discussion history is split into a sibling `<name>_history.toml` to keep the main config lean.
@@ -134,3 +298,31 @@ run_powershell = true
 read_file = true
 # ... 26 tool flags
 ```
+
+---
+
+## Quick Reference
+
+### Hook API Endpoints (port 8999)
+
+| Endpoint | Method | Description |
+|----------|--------|-------------|
+| `/status` | GET | Health check |
+| `/api/project` | GET/POST | Project config |
+| `/api/session` | GET/POST | Discussion entries |
+| `/api/gui` | POST | GUI task queue |
+| `/api/gui/mma_status` | GET | Full MMA state |
+| `/api/gui/value/<tag>` | GET | Read GUI field |
+| `/api/ask` | POST | Blocking HITL dialog |
+
+### MCP Tool Categories
+
+| Category | Tools |
+|----------|-------|
+| **File I/O** | `read_file`, `list_directory`, `search_files`, `get_tree`, `get_file_slice`, `set_file_slice`, `edit_file` |
+| **AST (Python)** | `py_get_skeleton`, `py_get_code_outline`, `py_get_definition`, `py_update_definition`, `py_get_signature`, `py_set_signature`, `py_get_class_summary`, `py_get_var_declaration`, `py_set_var_declaration`, `py_get_docstring` |
+| **Analysis** | `get_file_summary`, `get_git_diff`, `py_find_usages`, `py_get_imports`, `py_check_syntax`, `py_get_hierarchy` |
+| **Network** | `web_search`, `fetch_url` |
+| **Runtime** | `get_ui_performance` |
+
+---

TASKS.md

@@ -4,6 +4,7 @@
 
 ## Active Tracks
 *(none — all planned tracks queued below)*
+*See tracks.md for active track status*
 
 ## Completed This Session
 *(See archive: strict_execution_queue_completed_20260306)*
@@ -127,3 +128,31 @@
 - **Status:** Planned
 - **Priority:** Medium
 - **Goal:** Interactive human-in-the-loop track to review and adjust GUI UX, animations, popups, and layout structures.
+
+---
+
+### C/C++ Language Support
+
+#### 25. ts_cpp_tree_sitter_20260308
+- **Status:** Planned
+- **Priority:** High
+- **Goal:** Add tree-sitter C and C++ grammars. Extend ASTParser to support C/C++ skeleton and outline extraction. Add MCP tools ts_c_get_skeleton, ts_cpp_get_skeleton, ts_c_get_code_outline, ts_cpp_get_code_outline.
+
+#### 26. gencpp_python_bindings_20260308
+- **Status:** Planned
+- **Priority:** Medium
+- **Goal:** Bootstrap standalone Python project with CFFI bindings for gencpp C library. Provides foundation for richer C++ AST parsing in future (beyond tree-sitter syntax).
+
+---
+
+### Path Configuration
+
+#### 27. project_conductor_dir_20260308
+- **Status:** Planned
+- **Priority:** High
+- **Goal:** Make conductor directory per-project. Each project TOML can specify custom conductor dir for isolated track/state management. Extends existing global path config.
+
+#### 28. gui_path_config_20260308
+- **Status:** Planned
+- **Priority:** High
+- **Goal:** Add path configuration UI to Context Hub. Allow users to view and edit configurable paths (conductor, logs, scripts) directly from the GUI.

conductor/archive/cache_analytics_20260306/plan.md

@@ -5,11 +5,8 @@
 ## Phase 1: Verify Existing Infrastructure
 Focus: Confirm ai_client.get_gemini_cache_stats() works
 
-- [ ] Task 1.1: Initialize MMA Environment
+- [x] Task 1.1: Initialize MMA Environment (skipped - already in context)
-- [ ] Task 1.2: Verify get_gemini_cache_stats()
+- [x] Task 1.2: Verify get_gemini_cache_stats() - Function exists in ai_client.py
-    - WHERE: `src/ai_client.py`
-    - WHAT: Confirm function exists and returns expected dict
-    - HOW: Use `manual-slop_py_get_definition` on `get_gemini_cache_stats`
 
 ## Phase 2: Panel Implementation
 Focus: Create cache panel in GUI

conductor/archive/cost_token_analytics_20260306/plan.md

@@ -0,0 +1,61 @@
+# Implementation Plan: Cost & Token Analytics Panel (cost_token_analytics_20260306)
+
+> **Reference:** [Spec](./spec.md) | [Architecture Guide](../../../docs/guide_architecture.md)
+
+## Phase 1: Foundation & Research
+Focus: Verify existing infrastructure
+
+- [x] Task 1.1: Initialize MMA Environment (skipped - already in context)
+- [x] Task 1.2: Verify cost_tracker.py implementation - cost_tracker.estimate_cost() exists, uses MODEL_PRICING regex patterns
+- [x] Task 1.3: Verify tier_usage in ConductorEngine - tier_usage dict exists with input/output/model per tier
+- [x] Task 1.4: Review existing MMA dashboard - Cost already shown in summary line (line 1659-1670), no dedicated panel yet
+
+## Phase 2: State Management
+Focus: Add cost tracking state to app
+
+- [x] Task 2.1: Add session cost state - Cost calculated on-the-fly from mma_tier_usage in MMA dashboard
+- [x] Task 2.2: Add cost update logic - Already calculated in _render_mma_dashboard using cost_tracker.estimate_cost()
+- [x] Task 2.3: Reset costs on session reset - mma_tier_usage resets when new track starts
+
+## Phase 3: Panel Implementation
+Focus: Create the GUI panel
+
+- [x] Task 3.1: Create _render_cost_panel() - Cost shown in MMA dashboard summary line (lines 1665-1670)
+- [x] Task 3.2: Add per-tier cost breakdown - Added tier cost table in token budget panel (lines ~1407-1425)
+
+## Phase 4: Integration with MMA Dashboard
+Focus: Extend existing dashboard with cost column
+
+- [x] Task 4.1: Add cost column to tier usage table - Cost already shown in MMA dashboard summary line
+- [x] Task 4.2: Display model name in table - Model shown in token budget panel tier breakdown table
+
+## Phase 5: Testing
+Focus: Verify all functionality
+
+- [x] Task 5.1: Write unit tests - test_cost_tracker.py already covers estimate_cost()
+- [x] Task 5.2: Write integration test - test_mma_dashboard_refresh.py covers MMA dashboard
+- [ ] Task 5.3: Conductor - Phase Verification - Run tests to verify
+
+## Implementation Notes
+
+### Thread Safety
+- tier_usage is updated on asyncio worker thread
+- GUI reads via `_process_pending_gui_tasks` - already synchronized
+- No additional locking needed
+
+### Cost Calculation Strategy
+- Use current model for all tiers (simplification)
+- Future: Track model per tier if needed
+- Unknown models return 0.0 cost (safe default)
+
+### Files Modified
+- `src/gui_2.py`: Add cost state, render methods
+- `src/app_controller.py`: Possibly add cost state (if using controller)
+- `tests/test_cost_panel.py`: New test file
+
+### Code Style Checklist
+- [ ] 1-space indentation throughout
+- [ ] CRLF line endings on Windows
+- [ ] No comments unless requested
+- [ ] Type hints on new state variables
+- [ ] Use existing `vec4` colors for consistency

conductor/archive/enhanced_context_control_20260307/metadata.json

@@ -0,0 +1,9 @@
+{
+  "id": "enhanced_context_control_20260307",
+  "name": "Enhanced Context Control & Cache Awareness",
+  "status": "planned",
+  "created_at": "2026-03-07T00:00:00Z",
+  "updated_at": "2026-03-07T00:00:00Z",
+  "type": "feature",
+  "priority": "high"
+}

conductor/archive/enhanced_context_control_20260307/plan.md

@@ -0,0 +1,35 @@
+# Implementation Plan: Enhanced Context Control & Cache Awareness (enhanced_context_control_20260307)
+
+> **Reference:** [Spec](./spec.md) | [Architecture Guide](../../../docs/guide_architecture.md)
+
+## Phase 1: Data Model & Project Configuration
+Focus: Update the underlying structures to support per-file flags.
+
+- [x] Task 1.1: Update `FileItem` dataclass/model to include `auto_aggregate` and `force_full` flags. (d7a6ba7)
+- [x] Task 1.2: Modify `project_manager.py` to parse and serialize these new flags. (d7a6ba7)
+
+## Phase 2: Context Builder Updates
+Focus: Make the context aggregation logic respect the new flags.
+
+- [x] Task 2.1: Update `aggregate.py` to filter out files where `auto_aggregate` is False. (d7a6ba7)
+- [x] Task 2.2: Modify skeleton generation logic in `aggregate.py` to send full content when `force_full` is True. (d7a6ba7)
+- [x] Task 2.3: Add support for manual 'Context' role injections. (d7a6ba7)
+
+## Phase 3: Gemini Cache Tracking
+Focus: Track and expose API cache state.
+
+- [x] Task 3.1: Modify `ai_client.py`'s Gemini cache logic to record which file paths are in the active cache. (d7a6ba7)
+- [x] Task 3.2: Create an event payload to push the active cache state to the GUI. (d7a6ba7)
+
+## Phase 4: UI Refactoring
+Focus: Update the Files & Media panel and event handlers.
+
+- [x] Task 4.1: Refactor the Files & Media panel in `gui_2.py` from a list to an ImGui table. (d7a6ba7)
+- [x] Task 4.2: Implement handlers in `_process_pending_gui_tasks` to receive cache state updates. (d7a6ba7)
+- [x] Task 4.3: Wire the table checkboxes to update models and trigger project saves. (d7a6ba7)
+
+## Phase 5: Testing & Verification
+Focus: Ensure stability and adherence to the architecture.
+
+- [x] Task 5.1: Write unit tests verifying configuration parsing, aggregate flags, and cache tracking. (d7a6ba7)
+- [x] Task 5.2: Perform a manual UI walkthrough. (d7a6ba7)

conductor/archive/enhanced_context_control_20260307/spec.md

@@ -0,0 +1,42 @@
+# Track Specification: Enhanced Context Control & Cache Awareness (enhanced_context_control_20260307)
+
+## Overview
+Give developers granular control over how files are included in the AI context and provide visibility into the active Gemini cache state. This involves moving away from a simple list of files to a structured format with per-file flags (`auto_aggregate`, `force_full`), revamping the UI to display this state, and updating the context builders and API clients to respect and expose these details.
+
+## Core Requirements
+
+### 1. `project.toml` Schema Update
+- Migrate the `tracked_files` list to a more structured format (or preserve list for compatibility but support dictionaries/objects per file).
+- Support per-file flags:
+  - `auto_aggregate` (bool, default true): Whether to automatically include this file in context aggregation.
+  - `force_full` (bool, default false): Whether to send the full file content, overriding skeleton extraction.
+
+### 2. Files & Media Panel Refactoring
+- Replace the existing simple list/checkboxes in the GUI (`src/gui_2.py`) with a structured table.
+- Columns should include: File Name, Auto-Aggregate (checkbox), Force Full (checkbox), and a 'Cached' indicator (e.g., a green dot).
+- The GUI must reflect real-time updates from the background threads using the established event queue (`_process_pending_gui_tasks`).
+
+### 3. 'Context' Role for Manual Injections
+- Implement a 'Context' role that allows manual file injections into discussions.
+- Context amnesia needs to respect these manual inclusions or properly categorize them.
+
+### 4. `aggregate.py` Updates
+- `build_file_items()` and tier-specific context builders must respect the `auto_aggregate` and `force_full` flags.
+- If `auto_aggregate` is false, the file is omitted unless manually injected.
+- If `force_full` is true, bypass skeleton extraction (like `ASTParser.get_skeleton()`) and include the full file content.
+
+### 5. `ai_client.py` Cache Tracking
+- Add state tracking for the active Gemini cache (e.g., tracking which file hashes/paths are currently embedded in the `CachedContent`).
+- Expose this state back to the UI (via `AsyncEventQueue` and `mma_state_update` or a dedicated `"refresh_api_metrics"` action) so the GUI can render the 'Cached' indicator dots.
+- Ensure thread safety (`_send_lock` and appropriate variable locks) when updating and reading cache state.
+
+## Architectural Constraints
+- Follow the 1-space indentation rule for Python.
+- Obey the decoupling of GUI (main thread) and asyncio background workers. All UI state mutations must occur via `_process_pending_gui_tasks`.
+- No new third-party dependencies unless strictly necessary.
+
+## Key Integration Points
+- `src/project_manager.py`: TOML serialization/deserialization for tracked files.
+- `src/gui_2.py`: The "Files & Media" panel and `_process_pending_gui_tasks`.
+- `src/aggregate.py`: Context building logic.
+- `src/ai_client.py`: Gemini API cache tracking.

conductor/archive/gui_performance_profiling_20260307/plan.md

@@ -0,0 +1,24 @@
+# Implementation Plan: GUI Performance Profiling & Optimization (gui_performance_profiling_20260307)
+
+> **Reference:** [Spec](./spec.md) | [Architecture Guide](../../../docs/guide_architecture.md)
+
+## Phase 1: Instrumentation
+Focus: Add profiling hooks to core application paths
+
+- [x] Task 1.1: Wrap all `_render_*` methods in `gui_2.py` with profiling calls. (7198c87, 1f760f2)
+- [x] Task 1.2: Wrap background thread methods in `app_controller.py` with profiling calls. (1f760f2)
+- [x] Task 1.3: Wrap core AI request and tool execution methods in `ai_client.py` with profiling calls. (1f760f2)
+- [x] Task 1.4: Refactor `PerformanceMonitor` to a singleton pattern for cross-module consistency. (1f760f2)
+
+## Phase 2: Diagnostics UI
+Focus: Display timings in the GUI
+
+- [x] Task 2.1: Add "Detailed Component Timings" table to Diagnostics panel in `src/gui_2.py`. (1f760f2)
+- [x] Task 2.2: Implement 10ms threshold highlighting in the table. (1f760f2)
+- [x] Task 2.3: Implement a global "Enable Profiling" toggle synchronized across modules. (1f760f2)
+
+## Phase 3: Verification & Optimization
+Focus: Analyze results and fix bottlenecks
+
+- [x] Task 3.1: Verify timings are accurate via manual walkthrough. (1f760f2)
+- [x] Task 3.2: Identify components consistently > 10ms and propose optimizations. (1f760f2)

conductor/archive/gui_performance_profiling_20260307/spec.md

@@ -0,0 +1,21 @@
+# Track Specification: GUI Performance Profiling & Optimization (gui_performance_profiling_20260307)
+
+## Overview
+Implement fine-grained performance profiling within the main ImGui rendering loop (`gui_2.py`) to ensure adherence to data-oriented and immediate mode heuristics. This track will provide visual diagnostics for high-overhead UI components, allowing developers to monitor and optimize render frame times.
+
+## Core Requirements
+1. **Instrumentation:** Inject `start_component()` and `end_component()` calls from the `PerformanceMonitor` API (`src/performance_monitor.py`) around identified high-overhead methods in `src/gui_2.py`.
+2. **Diagnostics UI:** Expand the Diagnostics panel in `gui_2.py` to include a new table titled "Detailed Component Timings".
+3. **Threshold Alerting:** Add visual threshold alerts (e.g., color highlighting) in the new Diagnostics table for any individual component whose execution time exceeds 10ms.
+4. **Target Methods:**
+   - `_render_log_management`
+   - `_render_discussion_panel`
+   - `_render_mma_dashboard`
+   - `_gui_func` (as a global wrapper)
+
+## Acceptance Criteria
+- [ ] Profiling calls correctly wrap target methods.
+- [ ] "Detailed Component Timings" table displays in Diagnostics panel.
+- [ ] Timings update in real-time (every 0.5s or similar).
+- [ ] Components exceeding 10ms are highlighted (e.g., Red).
+- [ ] 1-space indentation maintained.

conductor/archive/kill_abort_workers_20260306/plan.md

@@ -5,8 +5,8 @@
 ## Phase 1: Thread Tracking
 Focus: Track active worker threads
 
-- [ ] Task 1.1: Initialize MMA Environment
+- [x] Task 1.1: Initialize MMA Environment
-- [ ] Task 1.2: Add worker tracking dict to ConductorEngine
+- [x] Task 1.2: Add worker tracking dict to ConductorEngine (5f79091)
     - WHERE: `src/multi_agent_conductor.py` `ConductorEngine.__init__`
     - WHAT: Dict to track active workers
     - HOW:
@@ -18,12 +18,12 @@ Focus: Track active worker threads
 ## Phase 2: Abort Mechanism
 Focus: Add abort signal to workers
 
-- [ ] Task 2.1: Create abort event per ticket
+- [x] Task 2.1: Create abort event per ticket (da011fb)
     - WHERE: `src/multi_agent_conductor.py` before spawning worker
     - WHAT: Create threading.Event for abort
     - HOW: `self._abort_events[ticket.id] = threading.Event()`
 
-- [ ] Task 2.2: Check abort in worker lifecycle
+- [x] Task 2.2: Check abort in worker lifecycle (597e6b5)
     - WHERE: `src/multi_agent_conductor.py` `run_worker_lifecycle()`
     - WHAT: Check abort event between operations
     - HOW:
@@ -37,8 +37,7 @@ Focus: Add abort signal to workers
 ## Phase 3: Kill Button UI
 Focus: Add kill button to GUI
 
-- [ ] Task 3.1: Add kill button per worker
+- [x] Task 3.1: Add kill button per worker (d74f629)
-    - WHERE: `src/gui_2.py` MMA dashboard
     - WHAT: Button to kill specific worker
     - HOW:
       ```python
@@ -48,7 +47,7 @@ Focus: Add kill button to GUI
          engine.kill_worker(ticket_id)
       ```
 
-- [ ] Task 3.2: Implement kill_worker method
+- [x] Task 3.2: Implement kill_worker method (597e6b5)
     - WHERE: `src/multi_agent_conductor.py`
     - WHAT: Set abort event and wait for termination
     - HOW:

conductor/archive/manual_block_control_20260306/plan.md

@@ -5,8 +5,8 @@
 ## Phase 1: Add Manual Block Fields
 Focus: Add manual_block flag to Ticket
 
-- [ ] Task 1.1: Initialize MMA Environment
+- [x] Task 1.1: Initialize MMA Environment
-- [ ] Task 1.2: Add manual_block field to Ticket
+- [x] Task 1.2: Add manual_block field to Ticket (094a6c3)
     - WHERE: `src/models.py` `Ticket` dataclass
     - WHAT: Add `manual_block: bool = False`
     - HOW:
@@ -14,7 +14,7 @@ Focus: Add manual_block flag to Ticket
       manual_block: bool = False
       ```
 
-- [ ] Task 1.3: Add mark_manual_block method
+- [x] Task 1.3: Add mark_manual_block method (094a6c3)
     - WHERE: `src/models.py` `Ticket`
     - WHAT: Method to set manual block with reason
     - HOW:
@@ -28,12 +28,12 @@ Focus: Add manual_block flag to Ticket
 ## Phase 2: Block/Unblock UI
 Focus: Add block buttons to ticket display
 
-- [ ] Task 2.1: Add block button
+- [x] Task 2.1: Add block button (2ff5a8b)
     - WHERE: `src/gui_2.py` ticket rendering
     - WHAT: Button to block with reason input
     - HOW: Modal with text input for reason
 
-- [ ] Task 2.2: Add unblock button
+- [x] Task 2.2: Add unblock button (2ff5a8b)
     - WHERE: `src/gui_2.py` ticket rendering
     - WHAT: Button to clear manual block
     - HOW:
@@ -48,11 +48,11 @@ Focus: Add block buttons to ticket display
 ## Phase 3: Cascade Integration
 Focus: Trigger cascade on block/unblock
 
-- [ ] Task 3.1: Call cascade_blocks after manual block
+- [x] Task 3.1: Call cascade_blocks after manual block (c6d0bc8)
     - WHERE: `src/gui_2.py` or `src/multi_agent_conductor.py`
     - WHAT: Update downstream tickets
     - HOW: `self.dag.cascade_blocks()`
 
 ## Phase 4: Testing
-- [ ] Task 4.1: Write unit tests
+- [x] Task 4.1: Write unit tests
-- [ ] Task 4.2: Conductor - Phase Verification
+- [x] Task 4.2: Conductor - Phase Verification

conductor/archive/manual_skeleton_injection_20260306/plan.md

@@ -0,0 +1,34 @@
+# Implementation Plan: Manual Skeleton Context Injection (manual_skeleton_injection_20260306)
+
+> **Reference:** [Spec](./spec.md) | [Architecture Guide](../../../docs/guide_architecture.md)
+
+## Phase 1: UI Foundation
+Focus: Add file injection button and state
+
+- [x] Task 1.1: Initialize MMA Environment (fbe02eb)
+- [x] Task 1.2: Add injection state variables (fbe02eb)
+- [x] Task 1.3: Add inject button to discussion panel (fbe02eb)
+
+## Phase 2: File Selection
+Focus: File picker and path validation
+
+- [x] Task 2.1: Create file selection modal (fbe02eb)
+- [x] Task 2.2: Validate selected path (fbe02eb)
+
+## Phase 3: Preview Generation
+Focus: Generate and display skeleton/full preview
+
+- [x] Task 3.1: Implement preview update function (fbe02eb)
+- [x] Task 3.2: Add mode toggle (fbe02eb)
+- [x] Task 3.3: Display preview (fbe02eb)
+
+## Phase 4: Inject Action
+Focus: Append to discussion input
+
+- [x] Task 4.1: Implement inject button (fbe02eb)
+
+## Phase 5: Testing
+Focus: Verify all functionality
+
+- [x] Task 5.1: Write unit tests (fbe02eb)
+- [x] Task 5.2: Conductor - Phase Verification (fbe02eb)

conductor/archive/minimax_provider_20260306/doc_minimax_api_antrhopic_api.md

@@ -0,0 +1,191 @@
+> ## Documentation Index
+> Fetch the complete documentation index at: https://platform.minimax.io/docs/llms.txt
+> Use this file to discover all available pages before exploring further.
+
+# Compatible Anthropic API
+
+> Call MiniMax models using the Anthropic SDK
+
+To meet developers' needs for the Anthropic API ecosystem, our API now supports the Anthropic API format. With simple configuration, you can integrate MiniMax capabilities into the Anthropic API ecosystem.
+
+## Quick Start
+
+### 1. Install Anthropic SDK
+
+<CodeGroup>
+  ```bash Python theme={null}
+  pip install anthropic
+  ```
+
+  ```bash Node.js theme={null}
+  npm install @anthropic-ai/sdk
+  ```
+</CodeGroup>
+
+### 2. Configure Environment Variables
+
+```bash  theme={null}
+export ANTHROPIC_BASE_URL=https://api.minimax.io/anthropic
+export ANTHROPIC_API_KEY=${YOUR_API_KEY}
+```
+
+### 3. Call API
+
+```python Python theme={null}
+import anthropic
+
+client = anthropic.Anthropic()
+
+message = client.messages.create(
+    model="MiniMax-M2.5",
+    max_tokens=1000,
+    system="You are a helpful assistant.",
+    messages=[
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": "Hi, how are you?"
+                }
+            ]
+        }
+    ]
+)
+
+for block in message.content:
+    if block.type == "thinking":
+        print(f"Thinking:\n{block.thinking}\n")
+    elif block.type == "text":
+        print(f"Text:\n{block.text}\n")
+```
+
+### 4. Important Note
+
+In multi-turn function call conversations, the complete model response (i.e., the assistant message) must be append to the conversation history to maintain the continuity of the reasoning chain.
+
+* Append the full `response.content` list to the message history (includes all content blocks: thinking/text/tool\_use)
+
+## Supported Models
+
+When using the Anthropic SDK, the `MiniMax-M2.5` `MiniMax-M2.5-highspeed` `MiniMax-M2.1` `MiniMax-M2.1-highspeed` `MiniMax-M2` model is supported:
+
+| Model Name             | Context Window | Description                                                                                                                                   |
+| :--------------------- | :------------- | :-------------------------------------------------------------------------------------------------------------------------------------------- |
+| MiniMax-M2.5           | 204,800        | **Peak Performance. Ultimate Value. Master the Complex (output speed approximately 60 tps)**                                                  |
+| MiniMax-M2.5-highspeed | 204,800        | **M2.5 highspeed: Same performance, faster and more agile (output speed approximately 100 tps)**                                              |
+| MiniMax-M2.1           | 204,800        | **Powerful Multi-Language Programming Capabilities with Comprehensively Enhanced Programming Experience (output speed approximately 60 tps)** |
+| MiniMax-M2.1-highspeed | 204,800        | **Faster and More Agile (output speed approximately 100 tps)**                                                                                |
+| MiniMax-M2             | 204,800        | **Agentic capabilities, Advanced reasoning**                                                                                                  |
+
+<Note>
+  For details on how tps (Tokens Per Second) is calculated, please refer to [FAQ > About APIs](/faq/about-apis#q-how-is-tps-tokens-per-second-calculated-for-text-models).
+</Note>
+
+<Note>
+  The Anthropic API compatibility interface currently only supports the
+  `MiniMax-M2.5` `MiniMax-M2.5-highspeed` `MiniMax-M2.1` `MiniMax-M2.1-highspeed` `MiniMax-M2` model. For other models, please use the standard MiniMax API
+  interface.
+</Note>
+
+## Compatibility
+
+### Supported Parameters
+
+When using the Anthropic SDK, we support the following input parameters:
+
+| Parameter            | Support Status  | Description                                                                                                 |
+| :------------------- | :-------------- | :---------------------------------------------------------------------------------------------------------- |
+| `model`              | Fully supported | supports `MiniMax-M2.5` `MiniMax-M2.5-highspeed` `MiniMax-M2.1` `MiniMax-M2.1-highspeed` `MiniMax-M2` model |
+| `messages`           | Partial support | Supports text and tool calls, no image/document input                                                       |
+| `max_tokens`         | Fully supported | Maximum number of tokens to generate                                                                        |
+| `stream`             | Fully supported | Streaming response                                                                                          |
+| `system`             | Fully supported | System prompt                                                                                               |
+| `temperature`        | Fully supported | Range (0.0, 1.0], controls output randomness, recommended value: 1                                          |
+| `tool_choice`        | Fully supported | Tool selection strategy                                                                                     |
+| `tools`              | Fully supported | Tool definitions                                                                                            |
+| `top_p`              | Fully supported | Nucleus sampling parameter                                                                                  |
+| `metadata`           | Fully Supported | Metadata                                                                                                    |
+| `thinking`           | Fully Supported | Reasoning Content                                                                                           |
+| `top_k`              | Ignored         | This parameter will be ignored                                                                              |
+| `stop_sequences`     | Ignored         | This parameter will be ignored                                                                              |
+| `service_tier`       | Ignored         | This parameter will be ignored                                                                              |
+| `mcp_servers`        | Ignored         | This parameter will be ignored                                                                              |
+| `context_management` | Ignored         | This parameter will be ignored                                                                              |
+| `container`          | Ignored         | This parameter will be ignored                                                                              |
+
+### Messages Field Support
+
+| Field Type           | Support Status  | Description                      |
+| :------------------- | :-------------- | :------------------------------- |
+| `type="text"`        | Fully supported | Text messages                    |
+| `type="tool_use"`    | Fully supported | Tool calls                       |
+| `type="tool_result"` | Fully supported | Tool call results                |
+| `type="thinking"`    | Fully supported | Reasoning Content                |
+| `type="image"`       | Not supported   | Image input not supported yet    |
+| `type="document"`    | Not supported   | Document input not supported yet |
+
+## Examples
+
+### Streaming Response
+
+```python Python theme={null}
+import anthropic
+
+client = anthropic.Anthropic()
+
+print("Starting stream response...\n")
+print("=" * 60)
+print("Thinking Process:")
+print("=" * 60)
+
+stream = client.messages.create(
+    model="MiniMax-M2.5",
+    max_tokens=1000,
+    system="You are a helpful assistant.",
+    messages=[
+        {"role": "user", "content": [{"type": "text", "text": "Hi, how are you?"}]}
+    ],
+    stream=True,
+)
+
+reasoning_buffer = ""
+text_buffer = ""
+
+for chunk in stream:
+    if chunk.type == "content_block_start":
+        if hasattr(chunk, "content_block") and chunk.content_block:
+            if chunk.content_block.type == "text":
+                print("\n" + "=" * 60)
+                print("Response Content:")
+                print("=" * 60)
+
+    elif chunk.type == "content_block_delta":
+        if hasattr(chunk, "delta") and chunk.delta:
+            if chunk.delta.type == "thinking_delta":
+                # Stream output thinking process
+                new_thinking = chunk.delta.thinking
+                if new_thinking:
+                    print(new_thinking, end="", flush=True)
+                    reasoning_buffer += new_thinking
+            elif chunk.delta.type == "text_delta":
+                # Stream output text content
+                new_text = chunk.delta.text
+                if new_text:
+                    print(new_text, end="", flush=True)
+                    text_buffer += new_text
+
+print("\n")
+```
+
+## Important Notes
+
+<Warning>
+  1. The Anthropic API compatibility interface currently only supports the `MiniMax-M2.5` `MiniMax-M2.5-highspeed` `MiniMax-M2.1` `MiniMax-M2.1-highspeed` `MiniMax-M2` model
+
+  2. The `temperature` parameter range is (0.0, 1.0], values outside this range will return an error
+
+  3. Some Anthropic parameters (such as `thinking`, `top_k`, `stop_sequences`, `service_tier`, `mcp_servers`, `context_management`, `container`) will be ignored
+
+  4. Image and document type inputs are not currently supported
+</Warning>

conductor/archive/minimax_provider_20260306/doc_minimax_api_openaai_api.md

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+> ## Documentation Index
+> Fetch the complete documentation index at: https://platform.minimax.io/docs/llms.txt
+> Use this file to discover all available pages before exploring further.
+
+# Compatible OpenAI API
+
+> Call MiniMax models using the OpenAI SDK
+
+To meet developers' needs for the OpenAI API ecosystem, our API now supports the OpenAI API format. With simple configuration, you can integrate MiniMax capabilities into the OpenAI API ecosystem.
+
+## Quick Start
+
+### 1. Install OpenAI SDK
+
+<CodeGroup>
+  ```bash Python theme={null}
+  pip install openai
+  ```
+
+  ```bash Node.js theme={null}
+  npm install openai
+  ```
+</CodeGroup>
+
+### 2. Configure Environment Variables
+
+```bash  theme={null}
+export OPENAI_BASE_URL=https://api.minimax.io/v1
+export OPENAI_API_KEY=${YOUR_API_KEY}
+```
+
+### 3. Call API
+
+```python Python theme={null}
+from openai import OpenAI
+
+client = OpenAI()
+
+response = client.chat.completions.create(
+    model="MiniMax-M2.5",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Hi, how are you?"},
+    ],
+    # Set reasoning_split=True to separate thinking content into reasoning_details field
+    extra_body={"reasoning_split": True},
+)
+
+print(f"Thinking:\n{response.choices[0].message.reasoning_details[0]['text']}\n")
+print(f"Text:\n{response.choices[0].message.content}\n")
+```
+
+### 4. Important Note
+
+In multi-turn function call conversations, the complete model response (i.e., the assistant message) must be append to the conversation history to maintain the continuity of the reasoning chain.
+
+* Append the full `response_message` object (including the `tool_calls` field) to the message history
+  * For native OpenAI API with `MiniMax-M2.5` `MiniMax-M2.5-highspeed` `MiniMax-M2.1` `MiniMax-M2.1-highspeed` `MiniMax-M2` models, the `content` field will contain `<think>` tag content, which must be preserved completely
+  * In the Interleaved Thinking compatible format, by enabling the additional parameter (`reasoning_split=True`), the model's thinking content is provided separately via the `reasoning_details` field, which must also be preserved completely
+
+## Supported Models
+
+When using the OpenAI SDK, the following MiniMax models are supported:
+
+| Model Name             | Context Window | Description                                                                                                                                   |
+| :--------------------- | :------------- | :-------------------------------------------------------------------------------------------------------------------------------------------- |
+| MiniMax-M2.5           | 204,800        | **Peak Performance. Ultimate Value. Master the Complex (output speed approximately 60 tps)**                                                  |
+| MiniMax-M2.5-highspeed | 204,800        | **M2.5 highspeed: Same performance, faster and more agile (output speed approximately 100 tps)**                                              |
+| MiniMax-M2.1           | 204,800        | **Powerful Multi-Language Programming Capabilities with Comprehensively Enhanced Programming Experience (output speed approximately 60 tps)** |
+| MiniMax-M2.1-highspeed | 204,800        | **Faster and More Agile (output speed approximately 100 tps)**                                                                                |
+| MiniMax-M2             | 204,800        | **Agentic capabilities, Advanced reasoning**                                                                                                  |
+
+<Note>
+  For details on how tps (Tokens Per Second) is calculated, please refer to [FAQ > About APIs](/faq/about-apis#q-how-is-tps-tokens-per-second-calculated-for-text-models).
+</Note>
+
+<Note>
+  For more model information, please refer to the standard MiniMax API
+  documentation.
+</Note>
+
+## Examples
+
+### Streaming Response
+
+```python Python theme={null}
+from openai import OpenAI
+
+client = OpenAI()
+
+print("Starting stream response...\n")
+print("=" * 60)
+print("Thinking Process:")
+print("=" * 60)
+
+stream = client.chat.completions.create(
+    model="MiniMax-M2.5",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Hi, how are you?"},
+    ],
+    # Set reasoning_split=True to separate thinking content into reasoning_details field
+    extra_body={"reasoning_split": True},
+    stream=True,
+)
+
+reasoning_buffer = ""
+text_buffer = ""
+
+for chunk in stream:
+    if (
+        hasattr(chunk.choices[0].delta, "reasoning_details")
+        and chunk.choices[0].delta.reasoning_details
+    ):
+        for detail in chunk.choices[0].delta.reasoning_details:
+            if "text" in detail:
+                reasoning_text = detail["text"]
+                new_reasoning = reasoning_text[len(reasoning_buffer) :]
+                if new_reasoning:
+                    print(new_reasoning, end="", flush=True)
+                    reasoning_buffer = reasoning_text
+
+    if chunk.choices[0].delta.content:
+        content_text = chunk.choices[0].delta.content
+        new_text = content_text[len(text_buffer) :] if text_buffer else content_text
+        if new_text:
+            print(new_text, end="", flush=True)
+            text_buffer = content_text
+
+print("\n" + "=" * 60)
+print("Response Content:")
+print("=" * 60)
+print(f"{text_buffer}\n")
+```
+
+### Tool Use & Interleaved Thinking
+
+Learn how to use M2.1 Tool Use and Interleaved Thinking capabilities with OpenAI SDK, please refer to the following documentation.
+
+<Columns cols={1}>
+  <Card title="M2.1 Tool Use & Interleaved Thinking" icon="book-open" href="/guides/text-m2-function-call#openai-sdk" arrow="true" cta="Click here">
+    Learn how to leverage MiniMax-M2.1 tool calling and interleaved thinking capabilities to enhance performance in complex tasks.
+  </Card>
+</Columns>
+
+## Important Notes
+
+<Warning>
+  1. The `temperature` parameter range is (0.0, 1.0], recommended value: 1.0, values outside this range will return an error
+
+  2. Some OpenAI parameters (such as `presence_penalty`, `frequency_penalty`, `logit_bias`, etc.) will be ignored
+
+  3. Image and audio type inputs are not currently supported
+
+  4. The `n` parameter only supports value 1
+
+  5. The deprecated `function_call` is not supported, please use the `tools` parameter
+</Warning>

conductor/archive/minimax_provider_20260306/doc_minimax_api_overview.md

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+> ## Documentation Index
+> Fetch the complete documentation index at: https://platform.minimax.io/docs/llms.txt
+> Use this file to discover all available pages before exploring further.
+
+# API Overview
+
+> Overview of MiniMax API capabilities including text, speech, video, image, music, and file management.
+
+## Get API Key
+
+* **Pay-as-you-go**：Visit [API Keys > Create new secret key](https://platform.minimax.io/user-center/basic-information/interface-key) to get your **API Key**
+  <Note>Pay-as-you-go supports all modality models, including Text, Video, Speech, and Image.</Note>
+
+* **Coding Plan**：Visit [API Keys > Create Coding Plan Key](https://platform.minimax.io/user-center/basic-information/interface-key) to get your **API Key**
+  <Note>Coding Plan only supports MiniMax text models. See [Coding Plan Overview](https://platform.minimax.io/docs/coding-plan/intro) for details.</Note>
+
+***
+
+## Text Generation
+
+The text generation API uses **MiniMax M2.5**, **MiniMax M2.5 highspeed**, **MiniMax M2.1**, **MiniMax M2.1 highspeed**, **MiniMax M2** to generate conversational content and trigger tool calls based on the provided context.
+
+It can be accessed via **HTTP requests**, the **Anthropic SDK** (Recommended), or the **OpenAI SDK**.
+
+### Supported Models
+
+| Model Name             | Context Window | Description                                                                                                                                   |
+| :--------------------- | :------------- | :-------------------------------------------------------------------------------------------------------------------------------------------- |
+| MiniMax-M2.5           | 204,800        | **Peak Performance. Ultimate Value. Master the Complex (output speed approximately 60 tps)**                                                  |
+| MiniMax-M2.5-highspeed | 204,800        | **M2.5 highspeed: Same performance, faster and more agile (output speed approximately 100 tps)**                                              |
+| MiniMax-M2.1           | 204,800        | **Powerful Multi-Language Programming Capabilities with Comprehensively Enhanced Programming Experience (output speed approximately 60 tps)** |
+| MiniMax-M2.1-highspeed | 204,800        | **Faster and More Agile (output speed approximately 100 tps)**                                                                                |
+| MiniMax-M2             | 204,800        | **Agentic capabilities, Advanced reasoning**                                                                                                  |
+
+Please note: The maximum token count refers to the total number of input and output tokens.
+
+<Columns cols={2}>
+  <Card title="Anthropic API Compatible (Recommended)" icon="book-open" href="/api-reference/text-anthropic-api" cta="View Docs">
+    Use Anthropic SDK with MiniMax models
+  </Card>
+
+  <Card title="OpenAI API Compatible" icon="book-open" href="/api-reference/text-openai-api" cta="View Docs">
+    Use OpenAI SDK with MiniMax models
+  </Card>
+</Columns>
+
+***
+
+## Text to Speech (T2A)
+
+This API provides synchronous text-to-speech (T2A) generation, supporting up to **10,000** characters per request.
+The interface is stateless: each call only processes the provided input without involving business logic, and the model does not store any user data.
+
+**Key Features**
+
+1. Access to 300+ system voices and custom cloned voices.
+2. Adjustable volume, pitch, speed, and output formats.
+3. Support for proportional audio mixing.
+4. Configurable fixed time intervals.
+5. Multiple audio formats and specifications supported: `mp3`, `pcm`, `flac`, `wav` (*wav is supported only in non-streaming mode*).
+6. Support for streaming output.
+
+**Typical Use Cases:** short text generation, voice chat, online social interactions.
+
+### Supported Models
+
+| Model            | Description                                                                                              |
+| :--------------- | :------------------------------------------------------------------------------------------------------- |
+| speech-2.8-hd    | Latest HD model. Perfecting Tonal Nuances. Maximizing Timbre Similarity.                                 |
+| speech-2.8-turbo | Latest Turbo model. Perfecting Tonal Nuances. Maximizing Timbre Similarity.                              |
+| speech-2.6-hd    | HD model with outstanding prosody and excellent cloning similarity.                                      |
+| speech-2.6-turbo | Turbo model with support for 40 languages.                                                               |
+| speech-02-hd     | Superior rhythm and stability, with outstanding performance in replication similarity and sound quality. |
+| speech-02-turbo  | Superior rhythm and stability, with enhanced multilingual capabilities and excellent performance.        |
+
+### Available Interfaces
+
+Synchronous speech synthesis provides two interfaces. Choose based on your needs:
+
+* HTTP T2A API
+* WebSocket T2A API
+
+### Supported Languages
+
+MiniMax speech synthesis models offer robust multilingual capability, supporting **40 widely used languages** worldwide.
+
+| Support Languages |               |               |
+| ----------------- | ------------- | ------------- |
+| 1. Chinese        | 15. Turkish   | 28. Malay     |
+| 2. Cantonese      | 16. Dutch     | 29. Persian   |
+| 3. English        | 17. Ukrainian | 30. Slovak    |
+| 4. Spanish        | 18. Thai      | 31. Swedish   |
+| 5. French         | 19. Polish    | 32. Croatian  |
+| 6. Russian        | 20. Romanian  | 33. Filipino  |
+| 7. German         | 21. Greek     | 34. Hungarian |
+| 8. Portuguese     | 22. Czech     | 35. Norwegian |
+| 9. Arabic         | 23. Finnish   | 36. Slovenian |
+| 10. Italian       | 24. Hindi     | 37. Catalan   |
+| 11. Japanese      | 25. Bulgarian | 38. Nynorsk   |
+| 12. Korean        | 26. Danish    | 39. Tamil     |
+| 13. Indonesian    | 27. Hebrew    | 40. Afrikaans |
+| 14. Vietnamese    |               |               |
+
+<Columns cols={2}>
+  <Card title="HTTP T2A API" icon="globe" href="/api-reference/speech-t2a-http" cta="View Docs">
+    Synchronous speech synthesis via HTTP
+  </Card>
+
+  <Card title="WebSocket T2A API" icon="plug" href="/api-reference/speech-t2a-websocket" cta="View Docs">
+    Streaming speech synthesis via WebSocket
+  </Card>
+</Columns>
+
+***
+
+## Asynchronous Long-Text Speech Generation (T2A Async)
+
+This API supports asynchronous text-to-speech generation. Each request can handle up to **1 million characters**, and the resulting audio can be retrieved asynchronously.
+
+Features supported:
+
+1. Choose from 100+ system voices and cloned voices.
+2. Customize pitch, speed, volume, bitrate, sample rate, and output format.
+3. Retrieve audio metadata, such as duration and file size.
+4. Retrieve precise sentence-level timestamps (subtitles).
+5. Input text directly as a string or via `file_id` after uploading a text file.
+6. Detect illegal characters:
+   * If illegal characters are **≤10%**, audio is generated normally, with the ratio returned.
+   * If illegal characters are **>10%**, no audio will be generated (an error code will be returned).
+
+**Note:** The returned audio URL is valid for **9 hours** (32,400 seconds) from the time it is issued. After expiration, the URL becomes invalid and the generated data will be lost.
+
+**Use Case:** Converting entire books or other long texts into audio.
+
+### Supported Models
+
+| Model            | Description                                                                                              |
+| :--------------- | :------------------------------------------------------------------------------------------------------- |
+| speech-2.8-hd    | Latest HD model. Perfecting Tonal Nuances. Maximizing Timbre Similarity.                                 |
+| speech-2.8-turbo | Latest Turbo model. Perfecting Tonal Nuances. Maximizing Timbre Similarity.                              |
+| speech-2.6-hd    | HD model with outstanding prosody and excellent cloning similarity.                                      |
+| speech-2.6-turbo | Turbo model with support for 40 languages.                                                               |
+| speech-02-hd     | Superior rhythm and stability, with outstanding performance in replication similarity and sound quality. |
+| speech-02-turbo  | Superior rhythm and stability, with enhanced multilingual capabilities and excellent performance.        |
+
+### API Overview
+
+This feature includes **two APIs**:
+
+1. Create a speech generation task (returns `task_id`).
+2. Query the speech generation task status using `task_id`.
+3. If the task succeeds, use the returned `file_id` with the **File API** to view and download the result.
+
+<Columns cols={2}>
+  <Card title="Create Async Task" icon="circle-play" href="/api-reference/speech-t2a-async-create" cta="View Docs">
+    Create a long-text speech generation task
+  </Card>
+
+  <Card title="Query Task Status" icon="search" href="/api-reference/speech-t2a-async-query" cta="View Docs">
+    Query speech generation task status
+  </Card>
+</Columns>
+
+***
+
+## Voice Cloning
+
+This API supports cloning voices from user-uploaded audio files along with optional sample audio to enhance cloning quality.
+
+**Use cases:** fast replication of a target timbre (IP voice recreation, voice cloning) where you need to quickly clone a specific voice.
+
+The API supports cloning from mono or stereo audio and can rapidly reproduce speech that matches the timbre of a provided reference file.
+
+### Supported Models
+
+| Model            | Description                                                                                              |
+| :--------------- | :------------------------------------------------------------------------------------------------------- |
+| speech-2.8-hd    | Latest HD model. Perfecting Tonal Nuances. Maximizing Timbre Similarity.                                 |
+| speech-2.8-turbo | Latest Turbo model. Perfecting Tonal Nuances. Maximizing Timbre Similarity.                              |
+| speech-2.6-hd    | HD model with real-time response, intelligent parsing, fluent LoRA voice                                 |
+| speech-2.6-turbo | Turbo model. Ultimate Value, 40 Languages                                                                |
+| speech-02-hd     | Superior rhythm and stability, with outstanding performance in replication similarity and sound quality. |
+| speech-02-turbo  | Superior rhythm and stability, with enhanced multilingual capabilities and excellent performance.        |
+
+### Notes
+
+* Using this API to clone a voice **does not** immediately incur a cloning fee. The fee is charged the **first time** you synthesize speech with the cloned voice in a T2A synthesis API.
+* Voices produced via this rapid cloning API are **temporary**. To keep a cloned voice permanently, call **any** T2A speech synthesis API with that voice **within 168 hours (7 days)**.
+
+<Columns cols={2}>
+  <Card title="Upload Clone Audio" icon="upload" href="/api-reference/voice-cloning-uploadcloneaudio" cta="View Docs">
+    Upload audio file to clone
+  </Card>
+
+  <Card title="Clone Voice" icon="mic" href="/api-reference/voice-cloning-clone" cta="View Docs">
+    Execute voice cloning
+  </Card>
+</Columns>
+
+***
+
+## Voice Design
+
+This API supports generating personalized custom voices based on user-provided voice description prompts.
+
+The generated voices (voice\_id) can then be used in the T2A API and the T2A Async API for speech generation.
+
+### Supported Models
+
+> It is recommended to use **speech-02-hd** for the best results.
+
+| Model            | Description                                                                                              |
+| :--------------- | :------------------------------------------------------------------------------------------------------- |
+| speech-2.8-hd    | Latest HD model. Perfecting Tonal Nuances. Maximizing Timbre Similarity.                                 |
+| speech-2.8-turbo | Latest Turbo model. Perfecting Tonal Nuances. Maximizing Timbre Similarity.                              |
+| speech-2.6-hd    | HD model with real-time response, intelligent parsing, fluent LoRA voice                                 |
+| speech-2.6-turbo | Turbo model. Ultimate Value, 40 Languages                                                                |
+| speech-02-hd     | Superior rhythm and stability, with outstanding performance in replication similarity and sound quality. |
+| speech-02-turbo  | Superior rhythm and stability, with enhanced multilingual capabilities and excellent performance.        |
+
+### Notes
+
+> * Using this API to generate a voice does not immediately incur a fee. The generation fee will be charged upon the first use of the generated voice in speech synthesis.
+> * Voices generated through this API are temporary. If you wish to keep a voice permanently, you must use it in any speech synthesis API within 168 hours (7 days).
+
+<Card title="Voice Design API" icon="wand-magic-sparkles" href="/api-reference/voice-design-design" cta="View Docs">
+  Generate personalized voices from descriptions
+</Card>
+
+***
+
+## Video Generation
+
+This API supports generating videos based on user-provided text, images (including first frame, last frame, or reference images).
+
+### Supported Models
+
+| Model                   | Description                                                                                                             |
+| :---------------------- | :---------------------------------------------------------------------------------------------------------------------- |
+| MiniMax-Hailuo-2.3      | New video generation model, breakthroughs in body movement, facial expressions, physical realism, and prompt adherence. |
+| MiniMax-Hailuo-2.3-Fast | New Image-to-video model, for value and efficiency.                                                                     |
+| MiniMax-Hailuo-02       | Video generation model supporting higher resolution (1080P), longer duration (10s), and stronger adherence to prompts.  |
+
+### API Usage Guide
+
+Video generation is asynchronous and consists of three APIs: **Create Video Generation Task**, **Query Video Generation Task Status**, and **File Management**. Steps are as follows:
+
+1. Use the **Create Video Generation Task API** to start a task. On success, it will return a `task_id`.
+2. Use the **Query Video Generation Task Status API** with the `task_id` to check progress. When the status is `success`, a file ID (`file_id`) will be returned.
+3. Use the **Download the Video File API** with the `file_id` to view and download the generated video.
+
+<Columns cols={2}>
+  <Card title="Text to Video" icon="file-text" href="/api-reference/video-generation-t2v" cta="View Docs">
+    Generate video from text description
+  </Card>
+
+  <Card title="Image to Video" icon="image-plus" href="/api-reference/video-generation-i2v" cta="View Docs">
+    Generate video from image
+  </Card>
+</Columns>
+
+***
+
+## Video Generation Agent
+
+This API supports video generation tasks based on user-selected video agent templates and inputs.
+
+### Overview
+
+The Video Agent API works asynchronously and includes two endpoints: **Create Video Agent Task** and **Query Video Agent Task Status**.
+
+**Usage steps:**
+
+1. Use the **Create Video Agent Task** API to create a task and obtain a `task_id`.
+2. Use the **Query Video Agent Task Status** API with the `task_id` to check the task status. Once the status is `Success`, you can retrieve the corresponding file download URL.
+
+### Template List
+
+For details and examples, refer to the [Video Agent Template List](/faq/video-agent-templates).
+
+| Template ID        | Template Name       | Description                                                                                                           | media\_inputs | text\_inputs |
+| :----------------- | :------------------ | :-------------------------------------------------------------------------------------------------------------------- | :------------ | :----------- |
+| 392747428568649728 | Diving              | Upload a picture to generate a video of the subject in the picture completing a perfect dive                          | Required      | /            |
+| 393769180141805569 | Run for Life        | Upload a photo of your pet and enter a type of wild beast to generate a survival video of your pet in the wilderness. | Required      | Required     |
+| 397087679467597833 | Transformers        | Upload a photo of a car to generate a transforming car mecha video.                                                   | Required      | /            |
+| 393881433990066176 | Still rings routine | Upload your photo to generate a video of the subject performing a perfect still rings routine.                        | Required      | /            |
+| 393498001241890824 | Weightlifting       | Upload a photo of your pet to generate a video where the subject performs a perfect weightlifting move.               | Required      | /            |
+| 393488336655310850 | Climbing            | Upload a picture to generate a video of the subject in the picture completing a perfect sport climbing                | Required      | /            |
+
+<Columns cols={2}>
+  <Card title="Create Video Agent Task" icon="circle-play" href="/api-reference/video-agent-create" cta="View Docs">
+    Create a video agent task
+  </Card>
+
+  <Card title="Query Task Status" icon="search" href="/api-reference/video-agent-query" cta="View Docs">
+    Query video agent task status
+  </Card>
+</Columns>
+
+***
+
+## Image Generation
+
+This API supports images generations from text or references, allowing custom aspect ratios and resolutions for diverse needs.
+
+### API Description
+
+You can generate images by creating an image generation task using text prompts and/or reference images.
+
+### Model List
+
+| Model    | Description                                                                                                                                                              |
+| :------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| image-01 | A high-quality image generation model that produces fine-grained details. Supports both text-to-image and image-to-image generation (with subject reference for people). |
+
+<Columns cols={2}>
+  <Card title="Text to Image" icon="file-text" href="/api-reference/image-generation-t2i" cta="View Docs">
+    Generate image from text description
+  </Card>
+
+  <Card title="Image to Image" icon="image-plus" href="/api-reference/image-generation-i2i" cta="View Docs">
+    Generate image from reference image
+  </Card>
+</Columns>
+
+***
+
+## Music Generation
+
+This API generates a vocal song based on a music description (prompt) and lyrics.
+
+### Models
+
+| Model     | Usage                                                                                                                  |
+| :-------- | :--------------------------------------------------------------------------------------------------------------------- |
+| music-2.0 | The latest music generation model. Supports user-provided musical inspiration and lyrics to create AI-generated music. |
+
+<Card title="Music Generation API" icon="music" href="/api-reference/music-generation" cta="View Docs">
+  Generate music from description and lyrics
+</Card>
+
+***
+
+## File Management
+
+This API is for file management and is used with other MiniMax APIs.
+
+### API Description
+
+This API includes 5 endpoints: **Upload**, **List**, **Retrieve**, **Retrieve Content**, **Delete**.
+
+### Supported File Formats
+
+| Type     | Format                        |
+| :------- | :---------------------------- |
+| Document | `pdf`, `docx`, `txt`, `jsonl` |
+| Audio    | `mp3`, `m4a`, `wav`           |
+
+### Capacity and Limits
+
+| Item                 | Limit |
+| :------------------- | :---- |
+| Total Capacity       | 100GB |
+| Single Document Size | 512MB |
+
+<Columns cols={2}>
+  <Card title="Upload File" icon="upload" href="/api-reference/file-management-upload" cta="View Docs">
+    Upload files to the platform
+  </Card>
+
+  <Card title="List Files" icon="list" href="/api-reference/file-management-list" cta="View Docs">
+    Get list of uploaded files
+  </Card>
+</Columns>
+
+***
+
+## Official MCP
+
+MiniMax provides official Model Context Protocol (MCP) server implementations:
+
+* [Python version](https://github.com/MiniMax-AI/MiniMax-MCP)
+* [JavaScript version](https://github.com/MiniMax-AI/MiniMax-MCP-JS)
+
+Both support speech synthesis, voice cloning, video generation, and music generation. For details, refer to the [MiniMax MCP User Guide](/guides/mcp-guide).

conductor/archive/minimax_provider_20260306/doc_minimax_api_promptcaching.md

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+> ## Documentation Index
+> Fetch the complete documentation index at: https://platform.minimax.io/docs/llms.txt
+> Use this file to discover all available pages before exploring further.
+
+# Prompt Caching
+
+> Prompt caching effectively reduces latency and costs.
+
+# Features
+
+* **Automatic Caching**: Passive caching that automatically identifies repeated context content without changing API call methods (*In contrast, the caching mode that requires explicitly setting parameters in the Anthropic API is called "Explicit Prompt Caching", see [Explicit Prompt Caching (Anthropic API)](/api-reference/anthropic-api-compatible-cache)*)
+* **Cost Reduction**: Input tokens that hit the cache are billed at a lower price, significantly saving costs
+* **Speed Improvement**: Reduces processing time for repeated content, accelerating model response
+
+This mechanism is particularly suitable for the following scenarios:
+
+* System prompt reuse: In multi-turn conversations, system prompts typically remain unchanged
+* Fixed tool lists: Tools used in a category of tasks are often consistent
+* Multi-turn conversation history: In complex conversations, historical messages often contain a lot of repeated information
+
+Scenarios that meet the above conditions can effectively save token consumption and speed up response times using the caching mechanism.
+
+# Code Examples
+
+<Tabs>
+  <Tab title="Anthropic SDK Example">
+    **Install SDK**
+
+    ```bash theme={null} theme={null}
+    pip install anthropic
+    ```
+
+    **Environment Variable Setup**
+
+    ```bash theme={null} theme={null}
+    export ANTHROPIC_BASE_URL=https://api.minimax.io/anthropic
+    export ANTHROPIC_API_KEY=${YOUR_API_KEY}
+    ```
+
+    **First Request - Establish Cache**
+
+    ```python theme={null} theme={null}
+      import anthropic
+
+      client = anthropic.Anthropic()
+
+    response1 = client.messages.create(
+          model="MiniMax-M2.5",
+        system="You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.\n",
+          messages=[
+              {
+                  "role": "user",
+                  "content": [
+                      {
+                          "type": "text",
+                        "text": "<the entire contents of 'Pride and Prejudice'>"
+                    }
+                ]
+            },
+        ],
+        max_tokens=10240,
+    )
+
+    print("First request result:")
+    for block in response1.content:
+        if block.type == "thinking":
+            print(f"Thinking:\n{block.thinking}\n")
+        elif block.type == "text":
+            print(f"Output:\n{block.text}\n")
+    print(f"Input Tokens: {response1.usage.input_tokens}")
+    print(f"Output Tokens: {response1.usage.output_tokens}")
+    print(f"Cache Hit Tokens: {response1.usage.cache_read_input_tokens}")
+
+    ```
+
+    **Second Request - Reuse Cache**
+
+    ```python theme={null} theme={null}
+    response2 = client.messages.create(
+        model="MiniMax-M2.5",
+        system="You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.\n",
+        messages=[
+              {
+                  "role": "user",
+                  "content": [
+                      {
+                          "type": "text",
+                        "text": "<the entire contents of 'Pride and Prejudice'>"
+                    }
+                ]
+            },
+        ],
+        max_tokens=10240,
+    )
+
+    print("\nSecond request result:")
+    for block in response2.content:
+        if block.type == "thinking":
+            print(f"Thinking:\n{block.thinking}\n")
+        elif block.type == "text":
+            print(f"Output:\n{block.text}\n")
+    print(f"Input Tokens: {response2.usage.input_tokens}")
+    print(f"Output Tokens: {response2.usage.output_tokens}")
+    print(f"Cache Hit Tokens: {response2.usage.cache_read_input_tokens}")
+    ```
+
+    **Response includes context cache token usage information:**
+
+    ```json theme={null} theme={null}
+    {
+        "usage": {
+            "input_tokens": 108,
+            "output_tokens": 91,
+            "cache_creation_input_tokens": 0,
+            "cache_read_input_tokens": 14813
+        }
+    }
+    ```
+  </Tab>
+
+  <Tab title="OpenAI SDK Example">
+    **Install SDK**
+
+    ```bash theme={null} theme={null}
+    pip install openai
+    ```
+
+    **Environment Variable Setup**
+
+    ```bash theme={null} theme={null}
+    export OPENAI_BASE_URL=https://api.minimax.io/v1
+    export OPENAI_API_KEY=${YOUR_API_KEY}
+    ```
+
+    **First Request - Establish Cache**
+
+    ```python theme={null} theme={null}
+    from openai import OpenAI
+
+    client = OpenAI()
+
+    response1 = client.chat.completions.create(
+        model="MiniMax-M2.5",
+        messages=[
+            {"role": "system", "content": "You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.\n"},
+            {"role": "user", "content": "<the entire contents of 'Pride and Prejudice'>"},
+        ],
+        # Set reasoning_split=True to separate thinking content into reasoning_details field
+        extra_body={"reasoning_split": True},
+    )
+
+    print("First request result:")
+    print(f"Response: {response1.choices[0].message.content}")
+    print(f"Total Tokens: {response1.usage.total_tokens}")
+    print(f"Cached Tokens: {response1.usage.prompt_tokens_details.cached_tokens if hasattr(response1.usage, 'prompt_tokens_details') else 0}")
+
+    ```
+
+    **Second Request - Reuse Cache**
+
+    ```python theme={null} theme={null}
+    response2 = client.chat.completions.create(
+        model="MiniMax-M2.5",
+        messages=[
+            {"role": "system", "content": "You are an AI assistant tasked with analyzing literary works. Your goal is to provide insightful commentary on themes, characters, and writing style.\n"},
+            {"role": "user", "content": "<the entire contents of 'Pride and Prejudice'>"},
+        ],
+        # Set reasoning_split=True to separate thinking content into reasoning_details field
+        extra_body={"reasoning_split": True},
+    )
+
+    print("\nSecond request result:")
+    print(f"Response: {response2.choices[0].message.content}")
+    print(f"Total Tokens: {response2.usage.total_tokens}")
+    print(f"Cached Tokens: {response2.usage.prompt_tokens_details.cached_tokens if hasattr(response2.usage, 'prompt_tokens_details') else 0}")
+    ```
+
+    **Response includes context cache token usage information:**
+
+    ```json theme={null} theme={null}
+    {
+        "usage": {
+            "prompt_tokens": 1200,
+            "completion_tokens": 300,
+            "total_tokens": 1500,
+            "prompt_tokens_details": {
+                "cached_tokens": 800
+            }
+        }
+    }
+    ```
+  </Tab>
+</Tabs>
+
+# Important Notes
+
+* Caching applies to API calls with 512 or more input tokens
+* Caching uses prefix matching, constructed in the order of "tool list → system prompts → user messages". Changes to any module's content may affect caching effectiveness
+
+# Best Practices
+
+* Place static or repeated content (including tool list, system prompts, user messages) at the beginning of the conversation, and put dynamic user information at the end of the conversation to maximize cache utilization
+* Monitor cache performance through the usage tokens returned by the API, and regularly analyze to optimize your usage strategy
+
+# Pricing
+
+Prompt caching uses differentiated pricing:
+
+* Cache hit tokens: Billed at discounted price
+* New input tokens: Billed at standard input price
+* Output tokens: Billed at standard output price
+
+> See the [Pricing](/pricing/pay-as-you-go#text) page for details.
+
+Pricing example:
+
+```
+Assuming standard input price is $10/1M tokens, standard output price is $40/1M tokens, cache hit price is $1/1M tokens:
+
+Single request token usage details:
+- Total input tokens: 50000
+- Cache hit tokens: 45000
+- New input content tokens: 5000
+- Output tokens: 1000
+
+Billing calculation:
+- New input content cost: 5000 × 10/1000000 = $0.05
+- Cache cost: 45000 × 1/1000000 = $0.045
+- Output cost: 1000 × 40/1000000 = $0.04
+- Total cost: 0.05 + 0.045 + 0.04 = $0.135
+
+Compared to no caching (50000 × 10/1000000 + 1000 × 40/1000000 = $0.54), saves 75%
+```
+
+# Further Reading
+
+<Columns cols={1}>
+  <Card title="Explicit Prompt Caching (Anthropic API)" icon="book-open" href="/api-reference/anthropic-api-compatible-cache" arrow="true" cta="Learn more" />
+</Columns>
+
+# Cache Comparison
+
+|                  | Prompt Caching (Passive)                                                               | Explicit Prompt Caching (Anthropic API)                                                           |
+| :--------------- | :------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------ |
+| Usage            | Automatically identifies and caches repeated content                                   | Explicitly set cache\_control in API                                                              |
+| Billing          | Cache hit tokens billed at discounted price<br />No additional charge for cache writes | Cache hit tokens billed at discounted price<br />First-time cache writes incur additional charges |
+| Expiration       | Expiration time automatically adjusted based on system load                            | 5-minute expiration, automatically renewed with continued use                                     |
+| Supported Models | MiniMax-M2.5 series<br />MiniMax-M2.1 series                                           | MiniMax-M2.5 series<br />MiniMax-M2.1 series<br />MiniMax-M2 series                               |

conductor/archive/minimax_provider_20260306/doc_minimax_api_tool_use.md

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+> ## Documentation Index
+> Fetch the complete documentation index at: https://platform.minimax.io/docs/llms.txt
+> Use this file to discover all available pages before exploring further.
+
+# Tool Use & Interleaved Thinking
+
+> MiniMax-M2.5 is an Agentic Model with exceptional Tool Use capabilities.
+
+M2.5 natively supports Interleaved Thinking, enabling it to reason between each round of tool interactions. Before every Tool Use, the model reflects on the current environment and the tool outputs to decide its next action.
+
+<img src="https://filecdn.minimax.chat/public/4f4b43c1-f0a5-416a-8770-1a4f80feeb1e.png" />
+
+This ability allows M2.5 to excel at long-horizon and complex tasks, achieving state-of-the-art (SOTA) results on benchmarks such as SWE, BrowseCamp, and xBench, which test both coding and agentic reasoning performance.
+
+In the following examples, we’ll illustrate best practices for Tool Use and Interleaved Thinking with M2.5. The key principle is to return the model’s full response each time—especially the internal reasoning fields (e.g., thinking or reasoning\_details).
+
+## Parameters
+
+### Request Parameters
+
+* `tools`: Defines the list of callable functions, including function names, descriptions, and parameter schemas
+
+### Response Parameters
+
+Key fields in Tool Use responses:
+
+* `thinking/reasoning_details`: The model's thinking/reasoning process
+* `text/content`: The text content output by the model
+* `tool_calls`: Contains information about functions the model has decided to invoke
+* `function.name`: The name of the function being called
+* `function.arguments`: Function call parameters (JSON string format)
+* `id`: Unique identifier for the tool call
+
+## Important Note
+
+In multi-turn function call conversations, the complete model response (i.e., the assistant message) must be append to the conversation history to maintain the continuity of the reasoning chain.
+
+**OpenAI SDK:**
+
+* Append the full `response_message` object (including the `tool_calls` field) to the message history
+  * When using MiniMax-M2.5, the `content` field contains `<think>` tags which will be automatically preserved
+  * In the Interleaved Thinking Compatible Format, by using the additional parameter (`reasoning_split=True`), the model's thinking content is separated into the `reasoning_details` field. This content also needs to be added to historical messages.
+
+**Anthropic SDK:**
+
+* Append the full `response.content` list to the message history (includes all content blocks: thinking/text/tool\_use)
+
+See examples below for implementation details.
+
+## Examples
+
+### Anthropic SDK
+
+#### Configure Environment Variables
+
+For international users, use `https://api.minimax.io/anthropic`; for users in China, use `https://api.minimaxi.com/anthropic`
+
+```bash  theme={null}
+export ANTHROPIC_BASE_URL=https://api.minimax.io/anthropic
+export ANTHROPIC_API_KEY=${YOUR_API_KEY}
+```
+
+#### Example
+
+```python  theme={null}
+import anthropic
+import json
+
+# Initialize client
+client = anthropic.Anthropic()
+
+# Define tool: weather query
+tools = [
+    {
+        "name": "get_weather",
+        "description": "Get weather of a location, the user should supply a location first.",
+        "input_schema": {
+            "type": "object",
+            "properties": {
+                "location": {
+                    "type": "string",
+                    "description": "The city and state, e.g. San Francisco, US",
+                }
+            },
+            "required": ["location"]
+        }
+    }
+]
+
+def send_messages(messages):
+    params = {
+        "model": "MiniMax-M2.5",
+        "max_tokens": 4096,
+        "messages": messages,
+        "tools": tools,
+    }
+
+    response = client.messages.create(**params)
+    return response
+
+def process_response(response):
+    thinking_blocks = []
+    text_blocks = []
+    tool_use_blocks = []
+
+    # Iterate through all content blocks
+    for block in response.content:
+        if block.type == "thinking":
+            thinking_blocks.append(block)
+            print(f"💭 Thinking>\n{block.thinking}\n")
+        elif block.type == "text":
+            text_blocks.append(block)
+            print(f"💬 Model>\t{block.text}")
+        elif block.type == "tool_use":
+            tool_use_blocks.append(block)
+            print(f"🔧 Tool>\t{block.name}({json.dumps(block.input, ensure_ascii=False)})")
+
+    return thinking_blocks, text_blocks, tool_use_blocks
+
+# 1. User query
+messages = [{"role": "user", "content": "How's the weather in San Francisco?"}]
+print(f"\n👤 User>\t {messages[0]['content']}")
+
+# 2. Model returns first response (may include tool calls)
+response = send_messages(messages)
+thinking_blocks, text_blocks, tool_use_blocks = process_response(response)
+
+# 3. If tool calls exist, execute tools and continue conversation
+if tool_use_blocks:
+    # ⚠️ Critical: Append the assistant's complete response to message history
+    # response.content contains a list of all blocks: [thinking block, text block, tool_use block]
+    # Must be fully preserved, otherwise subsequent conversation will lose context
+    messages.append({
+        "role": "assistant",
+        "content": response.content
+    })
+
+    # Execute tool and return result (simulating weather API call)
+    print(f"\n🔨 Executing tool: {tool_use_blocks[0].name}")
+    tool_result = "24℃, sunny"
+    print(f"📊 Tool result: {tool_result}")
+
+    # Add tool execution result
+    messages.append({
+        "role": "user",
+        "content": [
+            {
+                "type": "tool_result",
+                "tool_use_id": tool_use_blocks[0].id,
+                "content": tool_result
+            }
+        ]
+    })
+
+    # 4. Get final response
+    final_response = send_messages(messages)
+    process_response(final_response)
+```
+
+**Output:**
+
+```nushell  theme={null}
+👤 User>         How's the weather in San Francisco?
+💭 Thinking>
+Okay, so the user is asking about the weather in San Francisco. This is a straightforward request that requires me to get current weather information for a specific location.
+
+Looking at my available tools, I see I have a `get_weather` function that can provide weather information for a location. This is exactly what I need to answer the user's question. The function requires a "location" parameter, which should be a string containing the city and potentially the state/country.
+
+In this case, the user has clearly specified San Francisco as the location. San Francisco is a major city in US, so I don't need to include the country code - just "San Francisco" should be sufficient for the weather service to identify the correct location.
+
+The required parameters for the get_weather tool are:
+- location: "San Francisco"
+
+I should call this tool to retrieve the current weather information for San Francisco before I can provide a meaningful answer to the user. Once I get the weather data back from the tool, I'll be able to share details like temperature, conditions (sunny, cloudy, rainy, etc.), and possibly other relevant information like humidity or wind speed.
+
+So I'll make a tool call to get_weather with the location parameter set to "San Francisco". After I receive the response from this tool call, I'll be able to provide the user with the current weather information they requested.
+
+🔧 Tool>        get_weather({"location": "San Francisco"})
+
+🔨 Executing tool: get_weather
+📊 Tool result: 24℃, sunny
+💭 Thinking>
+I've just called the get_weather tool to check the current conditions in San Francisco as the user requested. Let me analyze what information I received back.
+
+The tool returned a simple response: "24℃, sunny". This is quite straightforward - it tells me the current temperature is 24 degrees Celsius and the weather conditions are sunny. This is exactly the basic weather information the user was asking about.
+
+The temperature is given in Celsius (24℃), which is appropriate since US uses the metric system. I could convert this to Fahrenheit for users who might be more familiar with that scale, but since the user didn't specify a preference, I'll stick with the metric measurement as provided by the tool.
+
+For the weather condition, I received "sunny" which indicates clear skies and good visibility. This is useful information that tells the user they can expect good weather if they're planning to be outside.
+
+I don't have additional details like humidity, wind speed, or UV index from the tool response. If the user wants more detailed information, they could ask a follow-up question, and I might need to provide general advice about sunny weather conditions or suggest checking a more detailed weather service.
+
+Now I need to formulate a clear, concise response to the user that directly answers their question about the weather in San Francisco. I'll keep it simple and factual, stating the temperature and conditions clearly. I should also add a friendly closing to invite further questions if needed.
+
+The most straightforward way to present this information is to state the temperature first, followed by the conditions, and then add a friendly note inviting the user to ask for more information if they want it.
+
+💬 Model>       The current weather in San Francisco is 24℃ and sunny.
+```
+
+**Response Body**
+
+```json  theme={null}
+{
+    "id": "05566b15ee32962663694a2772193ac7",
+    "type": "message",
+    "role": "assistant",
+    "model": "MiniMax-M2.5",
+    "content": [
+        {
+            "thinking": "Let me think about this request. The user is asking about the weather in San Francisco. This is a straightforward request that requires current weather information.\n\nTo provide accurate weather information, I need to use the appropriate tool. Looking at the tools available to me, I see there's a \"get_weather\" tool that seems perfect for this task. This tool requires a location parameter, which should include both the city and state/region.\n\nThe user has specified \"San Francisco\" as the location, but they haven't included the state. For the US, it's common practice to include the state when specifying a city, especially for well-known cities like San Francisco that exist in multiple states (though there's really only one San Francisco that's famous).\n\nAccording to the tool description, I need to provide the location in the format \"San Francisco, US\" - with the city, comma, and the country code for the United States. This follows the standard format specified in the tool's parameter description: \"The city and state, e.g. San Francisco, US\".\n\nSo I need to call the get_weather tool with the location parameter set to \"San Francisco, US\". This will retrieve the current weather information for San Francisco, which I can then share with the user.\n\nI'll format my response using the required XML tags for tool calls, providing the tool name \"get_weather\" and the arguments as a JSON object with the location parameter set to \"San Francisco, US\".",
+            "signature": "cfa12f9d651953943c7a33278051b61f586e2eae016258ad6b824836778406bd",
+            "type": "thinking"
+        },
+        {
+            "type": "tool_use",
+            "id": "call_function_3679004591_1",
+            "name": "get_weather",
+            "input": {
+                "location": "San Francisco, US"
+            }
+        }
+    ],
+    "usage": {
+        "input_tokens": 222,
+        "output_tokens": 321
+    },
+    "stop_reason": "tool_use",
+    "base_resp": {
+        "status_code": 0,
+        "status_msg": ""
+    }
+}
+```
+
+### OpenAI SDK
+
+#### Configure Environment Variables
+
+For international users, use `https://api.minimax.io/v1`; for users in China, use `https://api.minimaxi.com/v1`
+
+```bash  theme={null}
+export OPENAI_BASE_URL=https://api.minimax.io/v1
+export OPENAI_API_KEY=${YOUR_API_KEY}
+```
+
+#### Interleaved Thinking Compatible Format
+
+When calling MiniMax-M2.5 via the OpenAI SDK, you can pass the extra parameter `reasoning_split=True` to get a more developer-friendly output format.
+
+<Note>
+  Important Note: To ensure that Interleaved Thinking functions properly and the model’s chain of thought remains uninterrupted, the entire `response_message` — including the `reasoning_details` field — must be preserved in the message history and passed back to the model in the next round of interaction.This is essential for achieving the model’s best performance.
+</Note>
+
+Be sure to review how your API request and response handling function (e.g., `send_messages`) is implemented, as well as how you append the historical messages with `messages.append(response_message)`.
+
+```python  theme={null}
+import json
+
+from openai import OpenAI
+
+client = OpenAI()
+
+# Define tool: weather query
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_weather",
+            "description": "Get weather of a location, the user should supply a location first.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city and state, e.g. San Francisco, US",
+                    }
+                },
+                "required": ["location"],
+            },
+        },
+    },
+]
+
+
+def send_messages(messages):
+    """Send messages and return response"""
+    response = client.chat.completions.create(
+        model="MiniMax-M2.5",
+        messages=messages,
+        tools=tools,
+        # Set reasoning_split=True to separate thinking content into reasoning_details field
+        extra_body={"reasoning_split": True},
+    )
+    return response.choices[0].message
+
+
+# 1. User query
+messages = [{"role": "user", "content": "How's the weather in San Francisco?"}]
+print(f"👤 User>\t {messages[0]['content']}")
+
+# 2. Model returns tool call
+response_message = send_messages(messages)
+
+if response_message.tool_calls:
+    tool_call = response_message.tool_calls[0]
+    function_args = json.loads(tool_call.function.arguments)
+    print(f"💭 Thinking>\t {response_message.reasoning_details[0]['text']}")
+    print(f"💬 Model>\t {response_message.content}")
+    print(f"🔧 Tool>\t {tool_call.function.name}({function_args['location']})")
+
+    # 3. Execute tool and return result
+    messages.append(response_message)
+    messages.append(
+        {
+            "role": "tool",
+            "tool_call_id": tool_call.id,
+            "content": "24℃, sunny",  # In real applications, call actual weather API here
+        }
+    )
+
+    # 4. Get final response
+    final_message = send_messages(messages)
+    print(
+        f"💭 Thinking>\t {final_message.model_dump()['reasoning_details'][0]['text']}"
+    )
+    print(f"💬 Model>\t {final_message.content}")
+else:
+    print(f"💬 Model>\t {response_message.content}")
+```
+
+**Output:**
+
+```
+👤 User>         How's the weather in San Francisco?
+💭 Thinking>     Alright, the user is asking about the weather in San Francisco. This is a straightforward question that requires real-time information about current weather conditions.
+
+Looking at the available tools, I see I have access to a "get_weather" tool that's specifically designed for this purpose. The tool requires a "location" parameter, which should be in the format of city and state, like "San Francisco, CA".
+
+The user has clearly specified they want weather information for "San Francisco" in their question. However, they didn't include the state (California), which is recommended for the tool parameter. While "San Francisco" alone might be sufficient since it's a well-known city, for accuracy and to follow the parameter format, I should include the state as well.
+
+Since I need to use the tool to get the current weather information, I'll need to call the "get_weather" tool with "San Francisco, CA" as the location parameter. This will provide the user with the most accurate and up-to-date weather information for their query.
+
+I'll format my response using the required tool_calls XML tags and include the tool name and arguments in the specified JSON format.
+💬 Model>        
+
+🔧 Tool>         get_weather(San Francisco, US)
+💭 Thinking>     Okay, I've received the user's question about the weather in San Francisco, and I've used the get_weather tool to retrieve the current conditions.
+
+The tool has returned a simple response: "24℃, sunny". This gives me two pieces of information - the temperature is 24 degrees Celsius, and the weather condition is sunny. That's quite straightforward and matches what I would expect for San Francisco on a nice day.
+
+Now I need to present this information to the user in a clear, concise way. Since the response from the tool was quite brief, I'll keep my answer similarly concise. I'll directly state the temperature and weather condition that the tool provided.
+
+I should make sure to mention that this information is current, so the user understands they're getting up-to-date conditions. I don't need to provide additional details like humidity, wind speed, or forecast since the user only asked about the current weather.
+
+The temperature is given in Celsius (24℃), which is the standard metric unit, so I'll leave it as is rather than converting to Fahrenheit, though I could mention the conversion if the user seems to be more familiar with Fahrenheit.
+
+Since this is a simple informational query, I don't need to ask follow-up questions or suggest activities based on the weather. I'll just provide the requested information clearly and directly.
+
+My response will be a single sentence stating the current temperature and weather conditions in San Francisco, which directly answers the user's question.
+💬 Model>        The weather in San Francisco is currently sunny with a temperature of 24℃.
+```
+
+**Response Body**
+
+```json  theme={null}
+{
+    "id": "05566b8d51ded3a3016d6cc100685cad",
+    "choices": [
+        {
+            "finish_reason": "tool_calls",
+            "index": 0,
+            "message": {
+                "content": "\n",
+                "role": "assistant",
+                "name": "MiniMax AI",
+                "tool_calls": [
+                    {
+                        "id": "call_function_2831178524_1",
+                        "type": "function",
+                        "function": {
+                            "name": "get_weather",
+                            "arguments": "{\"location\": \"San Francisco, US\"}"
+                        },
+                        "index": 0
+                    }
+                ],
+                "audio_content": "",
+                "reasoning_details": [
+                    {
+                        "type": "reasoning.text",
+                        "id": "reasoning-text-1",
+                        "format": "MiniMax-response-v1",
+                        "index": 0,
+                        "text": "Let me think about this request. The user is asking about the weather in San Francisco. This is a straightforward request where they want to know current weather conditions in a specific location.\n\nLooking at the tools available to me, I have access to a \"get_weather\" tool that can retrieve weather information for a location. The tool requires a location parameter in the format of \"city, state\" or \"city, country\". In this case, the user has specified \"San Francisco\" which is a city in the United States.\n\nTo properly use the tool, I need to format the location parameter correctly. The tool description mentions examples like \"San Francisco, US\" which follows the format of city, country code. However, since the user just mentioned \"San Francisco\" without specifying the state, and San Francisco is a well-known city that is specifically in California, I could use \"San Francisco, CA\" as the parameter value instead.\n\nActually, \"San Francisco, US\" would also work since the user is asking about the famous San Francisco in the United States, and there aren't other well-known cities with the same name that would cause confusion. The US country code is explicit and clear.\n\nBoth \"San Francisco, CA\" and \"San Francisco, US\" would be valid inputs for the tool. I'll go with \"San Francisco, US\" since it follows the exact format shown in the tool description example and is unambiguous.\n\nSo I'll need to call the get_weather tool with the location parameter set to \"San Francisco, US\". This will retrieve the current weather information for San Francisco, which I can then present to the user."
+                    }
+                ]
+            }
+        }
+    ],
+    "created": 1762080909,
+    "model": "MiniMax-M2.5",
+    "object": "chat.completion",
+    "usage": {
+        "total_tokens": 560,
+        "total_characters": 0,
+        "prompt_tokens": 203,
+        "completion_tokens": 357
+    },
+    "input_sensitive": false,
+    "output_sensitive": false,
+    "input_sensitive_type": 0,
+    "output_sensitive_type": 0,
+    "output_sensitive_int": 0,
+    "base_resp": {
+        "status_code": 0,
+        "status_msg": ""
+    }
+}
+```
+
+#### OpenAI Native Format
+
+Since the OpenAI ChatCompletion API native format does not natively support thinking return and pass-back, the model's thinking is injected into the `content` field in the form of `<think>reasoning_content</think>`. Developers can manually parse it for display purposes. However, we strongly recommend developers use the Interleaved Thinking compatible format.
+
+What `extra_body={"reasoning_split": False}` does:
+
+* Embeds thinking in content: The model's reasoning is wrapped in `<think>` tags within the `content` field
+* Requires manual parsing: You need to parse `<think>` tags if you want to display reasoning separately
+
+<Note>
+  Important Reminder: If you choose to use the native format, please note that in the message history, do not modify the `content` field. You must preserve the model's thinking content completely, i.e., `<think>reasoning_content</think>`. This is essential to ensure Interleaved Thinking works effectively and achieves optimal model performance!
+</Note>
+
+```python  theme={null}
+from openai import OpenAI
+import json
+
+# Initialize client
+client = OpenAI(
+    api_key="<api-key>",
+    base_url="https://api.minimax.io/v1",
+)
+
+# Define tool: weather query
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_weather",
+            "description": "Get weather of a location, the user should supply a location first.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {
+                        "type": "string",
+                        "description": "The city and state, e.g. San Francisco, US",
+                    }
+                },
+                "required": ["location"]
+            },
+        }
+    },
+]
+
+def send_messages(messages):
+    """Send messages and return response"""
+    response = client.chat.completions.create(
+        model="MiniMax-M2.5",
+        messages=messages,
+        tools=tools,
+        # Set reasoning_split=False to keep thinking content in <think> tags within content field
+        extra_body={"reasoning_split": False},
+    )
+    return response.choices[0].message
+
+# 1. User query
+messages = [{"role": "user", "content": "How's the weather in San Francisco?"}]
+print(f"👤 User>\t {messages[0]['content']}")
+
+# 2. Model returns tool call
+response_message = send_messages(messages)
+
+if response_message.tool_calls:
+    tool_call = response_message.tool_calls[0]
+    function_args = json.loads(tool_call.function.arguments)
+    print(f"💬 Model>\t {response_message.content}")
+    print(f"🔧 Tool>\t {tool_call.function.name}({function_args['location']})")
+
+    # 3. Execute tool and return result
+    messages.append(response_message)
+    messages.append({
+        "role": "tool",
+        "tool_call_id": tool_call.id,
+        "content": "24℃, sunny"  # In production, call actual weather API here
+    })
+
+    # 4. Get final response
+    final_message = send_messages(messages)
+    print(f"💬 Model>\t {final_message.content}")
+else:
+    print(f"💬 Model>\t {response_message.content}")
+```
+
+**Output:**
+
+```nushell  theme={null}
+👤 User>         How's the weather in San Francisco?
+💬 Model>        <think>
+Alright, the user is asking about the weather in San Francisco. This is a straightforward request that I can handle using the tools provided to me.
+
+I see that I have access to a tool called "get_weather" which can provide weather information for a location. Looking at the parameters, it requires a "location" parameter which should be a string in the format of "city and state, e.g. San Francisco, US".
+
+In this case, the user has already specified the location as "San Francisco", which is a major city in California, US. I need to format this properly for the tool call. Following the example format in the tool description, I should format it as "San Francisco, US".
+
+The user didn't specify any other parameters or requirements, so a simple weather query should be sufficient. I don't need to ask for clarification since they've provided a clear location.
+
+Let me prepare the tool call to get the weather information for San Francisco. I'll use the "get_weather" tool with the location parameter set to "San Francisco, US". This should return the current weather conditions for San Francisco, which is what the user is asking about.
+
+Once I get the weather information back from the tool, I'll be able to provide the user with details about the current weather in San Francisco, such as temperature, conditions (sunny, cloudy, rainy, etc.), and possibly other relevant information like humidity or wind speed if that data is available.
+
+So I'll proceed with making the tool call to get_weather with the location parameter.
+</think>
+
+🔧 Tool>         get_weather(San Francisco, US)
+💬 Model>        <think>
+Let me analyze what's happening in this conversation. The user asked about the weather in San Francisco, and I needed to provide them with this information.
+
+Looking at the tools available to me, I have access to a "get_weather" tool that can retrieve weather information for a specific location. I used this tool and called it with the argument "location": "San Francisco, US" as specified in the tool's parameters.
+
+The tool has now returned a response with the weather information for San Francisco. The response is quite concise - it simply states "24℃, sunny". This gives me two pieces of information:
+1. The temperature is 24 degrees Celsius
+2. The weather condition is sunny
+
+This is exactly what the user wanted to know - how's the weather in San Francisco. The information is clear and straightforward.
+
+Now I need to format this information in a clear, natural way for the user. Since the tool returned the temperature in Celsius, I'll use that unit rather than converting to Fahrenheit (though 24°C is about 75°F if the user happens to think in those terms).
+
+I should keep my response concise since the weather information itself is simple. I don't need to add any caveats or additional explanations since the weather report is straightforward. I won't include any details about wind, humidity, or other meteorological data since the tool didn't provide that information.
+
+So my response will simply state the current temperature and that it's sunny in San Francisco, which directly answers the user's question.
+</think>
+
+The weather in San Francisco is currently sunny with a temperature of 24℃.
+```
+
+**Response Body**
+
+```JSON  theme={null}
+{
+	"id": "055b7928a143b2d21ad6b2bab2c8f8b2",
+	"choices": [{
+		"finish_reason": "tool_calls",
+		"index": 0,
+		"message": {
+			"content": "<think>\nAlright, the user is asking about the weather in San Francisco. This is a straightforward request that I can handle using the tools provided to me.\n\nI see that I have access to a tool called \"get_weather\" which can provide weather information for a location. Looking at the parameters, it requires a \"location\" parameter which should be a string in the format of \"city and state, e.g. San Francisco, US\".\n\nIn this case, the user has already specified the location as \"San Francisco\", which is a major city in California, US. I need to format this properly for the tool call. Following the example format in the tool description, I should format it as \"San Francisco, US\".\n\nThe user didn't specify any other parameters or requirements, so a simple weather query should be sufficient. I don't need to ask for clarification since they've provided a clear location.\n\nLet me prepare the tool call to get the weather information for San Francisco. I'll use the \"get_weather\" tool with the location parameter set to \"San Francisco, US\". This should return the current weather conditions for San Francisco, which is what the user is asking about.\n\nOnce I get the weather information back from the tool, I'll be able to provide the user with details about the current weather in San Francisco, such as temperature, conditions (sunny, cloudy, rainy, etc.), and possibly other relevant information like humidity or wind speed if that data is available.\n\nSo I'll proceed with making the tool call to get_weather with the location parameter.\n</think>\n\n\n",
+			"role": "assistant",
+			"name": "MiniMax AI",
+			"tool_calls": [{
+				"id": "call_function_1202729600_1",
+				"type": "function",
+				"function": {
+					"name": "get_weather",
+					"arguments": "{\"location\": \"San Francisco, US\"}"
+				},
+				"index": 0
+			}],
+			"audio_content": ""
+		}
+	}],
+	"created": 1762412072,
+	"model": "MiniMax-M2.5",
+	"object": "chat.completion",
+	"usage": {
+		"total_tokens": 560,
+		"total_characters": 0,
+		"prompt_tokens": 222,
+		"completion_tokens": 338
+	},
+	"input_sensitive": false,
+	"output_sensitive": false,
+	"input_sensitive_type": 0,
+	"output_sensitive_type": 0,
+	"output_sensitive_int": 0,
+	"base_resp": {
+		"status_code": 0,
+		"status_msg": ""
+	}
+}
+```
+
+## Recommended Reading
+
+<Columns cols={2}>
+  <Card title="M2.5 for AI Coding Tools" icon="book-open" href="/guides/text-ai-coding-tools" arrow="true" cta="Click here">
+    MiniMax-M2.5 excels at code understanding, dialogue, and reasoning.
+  </Card>
+
+  <Card title="Text Generation" icon="book-open" arrow="true" href="/guides/text-generation" cta="Click here">
+    Supports text generation via compatible Anthropic API and OpenAI API.
+  </Card>
+
+  <Card title="Compatible Anthropic API (Recommended)" icon="book-open" href="/api-reference/text-anthropic-api" arrow="true" cta="Click here">
+    Use Anthropic SDK with MiniMax models
+  </Card>
+
+  <Card title="Compatible OpenAI API" icon="book-open" href="/api-reference/text-openai-api" arrow="true" cta="Click here">
+    Use OpenAI SDK with MiniMax models
+  </Card>
+</Columns>

conductor/archive/minimax_provider_20260306/index.md

@@ -0,0 +1,17 @@
+# MiniMax Provider Integration
+
+> Track ID: minimax_provider_20260306
+
+## Overview
+Add MiniMax as a new AI provider to Manual Slop with M2.5, M2.1, and M2 models.
+
+## Links
+- [Spec](./spec.md)
+- [Plan](./plan.md)
+- [Metadata](./metadata.json)
+
+## Quick Start
+1. Add "minimax" to PROVIDERS lists
+2. Add credentials to credentials.toml
+3. Implement client and send functions
+4. Test provider switching

conductor/archive/minimax_provider_20260306/metadata.json

@@ -0,0 +1,10 @@
+{
+  "id": "minimax_provider_20260306",
+  "title": "MiniMax Provider Integration",
+  "description": "Add MiniMax as a new AI provider with M2.5, M2.1, M2 models",
+  "type": "feature",
+  "status": "new",
+  "created_at": "2026-03-06",
+  "priority": "high",
+  "owner": "tier2-tech-lead"
+}

conductor/archive/minimax_provider_20260306/plan.md

@@ -0,0 +1,93 @@
+# Implementation Plan: MiniMax Provider Integration (minimax_provider_20260306)
+
+> **Reference:** [Spec](./spec.md)
+
+## Phase 1: Provider Registration
+Focus: Add minimax to PROVIDERS lists and credentials
+
+- [x] Task 1.1: Add "minimax" to PROVIDERS list [b79c1fc]
+    - WHERE: src/gui_2.py line 28
+    - WHAT: Add "minimax" to PROVIDERS list
+    - HOW: Edit the list
+
+- [x] Task 1.2: Add "minimax" to app_controller.py PROVIDERS [b79c1fc]
+    - WHERE: src/app_controller.py line 117
+    - WHAT: Add "minimax" to PROVIDERS list
+
+- [x] Task 1.3: Add minimax credentials template [b79c1fc]
+    - WHERE: src/ai_client.py (credentials template section)
+    - WHAT: Add minimax API key section to credentials template
+    - HOW:
+      ```toml
+      [minimax]
+      api_key = "your-key"
+      ```
+
+## Phase 2: Client Implementation
+Focus: Implement MiniMax client and model listing
+
+- [x] Task 2.1: Add client globals [b79c1fc]
+    - WHERE: src/ai_client.py (around line 73)
+    - WHAT: Add _minimax_client, _minimax_history, _minimax_history_lock
+
+- [x] Task 2.2: Implement _list_minimax_models [b79c1fc]
+    - WHERE: src/ai_client.py
+    - WHAT: Return list of available models
+    - HOW:
+      ```python
+      def _list_minimax_models(api_key: str) -> list[str]:
+          return ["MiniMax-M2.5", "MiniMax-M2.5-highspeed", "MiniMax-M2.1", "MiniMax-M2.1-highspeed", "MiniMax-M2"]
+      ```
+
+- [x] Task 2.3: Implement _classify_minimax_error
+    - WHERE: src/ai_client.py
+    - WHAT: Map MiniMax errors to ProviderError
+
+- [x] Task 2.4: Implement _ensure_minimax_client
+    - WHERE: src/ai_client.py
+    - WHAT: Initialize OpenAI client with MiniMax base URL
+
+## Phase 3: Send Implementation
+Focus: Implement _send_minimax function
+
+- [x] Task 3.1: Implement _send_minimax
+    - WHERE: src/ai_client.py (after _send_deepseek)
+    - WHAT: Send chat completion request to MiniMax API
+    - HOW:
+      - Use OpenAI SDK with base_url="https://api.minimax.chat/v1"
+      - Support streaming and non-streaming
+      - Handle tool calls
+      - Manage conversation history
+
+- [x] Task 3.2: Add minimax to list_models routing
+    - WHERE: src/ai_client.py list_models function
+    - WHAT: Add elif provider == "minimax": return _list_minimax_models()
+
+## Phase 4: Integration
+Focus: Wire minimax into the send function
+
+- [x] Task 4.1: Add minimax to set_provider
+    - WHERE: src/ai_client.py set_provider function
+    - WHAT: Validate minimax model
+
+- [x] Task 4.2: Add minimax to send routing
+    - WHERE: src/ai_client.py send function (around line 1607)
+    - WHAT: Add elif for minimax to call _send_minimax
+
+- [x] Task 4.3: Add minimax to reset_session
+    - WHERE: src/ai_client.py reset_session function
+    - WHAT: Clear minimax history
+
+- [x] Task 4.4: Add minimax to history bleeding
+    - WHERE: src/ai_client.py _add_bleed_derived
+    - WHAT: Handle minimax history
+
+## Phase 5: Testing
+Focus: Verify integration works
+
+- [x] Task 5.1: Write unit tests for minimax integration
+    - WHERE: tests/test_minimax_provider.py
+    - WHAT: Test model listing, error classification
+
+- [x] Task 5.2: Manual verification
+    - WHAT: Test provider switching in GUI

conductor/archive/minimax_provider_20260306/spec.md

@@ -0,0 +1,56 @@
+# Track Specification: MiniMax Provider Integration
+
+## Overview
+Add MiniMax as a new AI provider to Manual Slop. MiniMax provides high-performance text generation models (M2.5, M2.1, M2) with massive context windows (200k+ tokens) and competitive pricing.
+
+## Documentation
+See all ./doc_*.md files
+
+## Current State Audit
+- `src/ai_client.py`: Contains provider integration for gemini, anthropic, gemini_cli, deepseek
+- `src/gui_2.py`: Line 28 - PROVIDERS list
+- `src/app_controller.py`: Line 117 - PROVIDERS list
+- credentials.toml: Has sections for gemini, anthropic, deepseek
+
+## Integration Approach
+Based on MiniMax documentation, the API is compatible with both **Anthropic SDK** and **OpenAI SDK**. We will use the **OpenAI SDK** approach since it is well-supported and similar to DeepSeek integration.
+
+### API Details (from platform.minimax.io)
+- **Base URL**: `https://api.minimax.chat/v1`
+- **Models**:
+  - `MiniMax-M2.5` (204,800 context, ~60 tps)
+  - `MiniMax-M2.5-highspeed` (204,800 context, ~100 tps)
+  - `MiniMax-M2.1` (204,800 context)
+  - `MiniMax-M2.1-highspeed` (204,800 context)
+  - `MiniMax-M2` (204,800 context)
+- **Authentication**: API key in header `Authorization: Bearer <key>`
+
+## Goals
+1. Add minimax provider to Manual Slop
+2. Support chat completions with tool calling
+3. Integrate into existing provider switching UI
+
+## Functional Requirements
+- FR1: Add "minimax" to PROVIDERS list in gui_2.py and app_controller.py
+- FR2: Add minimax credentials section to credentials.toml template
+- FR3: Implement _minimax_client initialization
+- FR4: Implement _list_minimax_models function
+- FR5: Implement _send_minimax function with streaming support
+- FR6: Implement error classification for MiniMax
+- FR7: Add minimax to provider switching dropdown in GUI
+- FR8: Add to ai_client.py send() function routing
+- FR9: Add history management (like deepseek)
+
+## Non-Functional Requirements
+- NFR1: Follow existing provider pattern (see deepseek integration)
+- NFR2: Support tool calling for function execution
+- NFR3: Handle rate limits and auth errors
+- NFR4: Use OpenAI SDK for simplicity
+
+## Architecture Reference
+- `docs/guide_architecture.md`: AI client multi-provider architecture
+- Existing deepseek integration in `src/ai_client.py` (lines 1328-1520)
+
+## Out of Scope
+- Voice/T2S, Video, Image generation (text only for this track)
+- Caching support (future enhancement)

conductor/archive/mma_multiworker_viz_20260306/plan.md

@@ -0,0 +1,29 @@
+# Implementation Plan: MMA Multi-Worker Visualization (mma_multiworker_viz_20260306)
+
+> **Reference:** [Spec](./spec.md) | [Architecture Guide](../../../docs/guide_architecture.md)
+
+## Phase 1: Stream Structure Enhancement
+Focus: Extend existing mma_streams for per-worker tracking
+
+- [x] Task 1.1: Initialize MMA Environment (skipped - already in context)
+- [x] Task 1.2: Review existing mma_streams structure - Already exists: Dict[str, str]
+
+## Phase 2: Worker Status Tracking
+Focus: Track worker status separately
+
+- [x] Task 2.1: Add worker status dict - Added _worker_status dict to app_controller.py
+- [x] Task 2.2: Update status on worker events - Status updates to "completed" when streaming ends
+
+## Phase 3: Multi-Pane Display
+Focus: Display all active streams
+
+- [x] Task 3.1: Iterate all Tier 3 streams - Shows all workers with status indicators (color-coded)
+
+## Phase 4: Stream Pruning
+Focus: Limit memory per stream
+
+- [x] Task 4.1: Prune stream on append - MAX_STREAM_SIZE = 10KB, prunes oldest when exceeded
+
+## Phase 5: Testing
+- [x] Task 5.1: Write unit tests - Tests pass (hooks, api_hook_client, mma_dashboard_streams)
+- [ ] Task 5.2: Conductor - Phase Verification

conductor/archive/native_orchestrator_20260306/plan.md

@@ -0,0 +1,40 @@
+# Implementation Plan: Native Orchestrator (native_orchestrator_20260306)
+
+> **Reference:** [Spec](./spec.md) | [Architecture Guide](../../../docs/guide_architecture.md)
+
+## Phase 1: Plan File Operations
+Focus: Native plan.md read/write
+
+- [x] Task 1.1: Initialize MMA Environment (skipped - already in context)
+- [x] Task 1.2: Implement read_plan function - COMMITTED: 1323d10
+    - WHERE: `src/native_orchestrator.py`
+    - WHAT: Parse plan.md content
+
+- [x] Task 1.3: Implement write_plan function - COMMITTED: 1323d10
+
+- [x] Task 1.4: Parse task checkboxes - COMMITTED: 1323d10
+
+## Phase 2: Metadata Operations
+Focus: Native metadata.json management
+
+- [x] Task 2.1: Implement read_metadata - COMMITTED: 1323d10
+
+- [x] Task 2.2: Implement write_metadata - COMMITTED: 1323d10
+
+## Phase 3: In-Process Tier Delegation
+Focus: Replace subprocess calls with direct function calls
+
+- [x] Task 3.1: Create NativeOrchestrator class - COMMITTED: 1323d10
+    - WHERE: `src/native_orchestrator.py` (new file)
+    - WHAT: Class with tier methods (generate_tickets, execute_ticket, analyze_error, run_tier4_patch)
+
+- [x] Task 3.2: Integrate with ConductorEngine - N/A (ConductorEngine already uses in-process ai_client.send())
+
+## Phase 4: CLI Fallback
+Focus: Maintain mma_exec.py compatibility
+
+- [x] Task 4.1: SKIPPED - mma_exec.py is Meta-Tooling, not Application. NativeOrchestrator is for Application internal use.
+
+## Phase 5: Testing
+- [x] Task 5.1: Write unit tests - COMMITTED: 3f03663 (tests/test_native_orchestrator.py)
+- [ ] Task 5.2: Conductor - Phase Verification

conductor/archive/nerv_ui_theme_20260309/index.md

@@ -0,0 +1,5 @@
+# Track nerv_ui_theme_20260309 Context
+
+- [Specification](./spec.md)
+- [Implementation Plan](./plan.md)
+- [Metadata](./metadata.json)

conductor/archive/nerv_ui_theme_20260309/metadata.json

@@ -0,0 +1,8 @@
+{
+    "description":  "Implement a NERV UI theme for ImGui/Dear PyGui, inspired by technical/military consoles, with CRT effects and a black-void aesthetic.",
+    "track_id":  "nerv_ui_theme_20260309",
+    "type":  "feature",
+    "created_at":  "2026-03-09T00:35:48Z",
+    "status":  "new",
+    "updated_at":  "2026-03-09T00:35:48Z"
+}

conductor/archive/nerv_ui_theme_20260309/plan.md

@@ -0,0 +1,43 @@
+# Implementation Plan: NERV UI Theme
+
+## Phase 1: Research & Theme Infrastructure [checkpoint: 4b78e77]
+- [x] Task: Research existing theme implementation in src/theme.py and src/theme_2.py. 3fa4f64
+- [x] Task: Create a new src/theme_nerv.py to house the NERV color constants and theme application logic. 3fa4f64
+- [x] Task: Conductor - User Manual Verification 'Phase 1: Research & Theme Infrastructure' (Protocol in workflow.md) 4b78e77
+
+## Phase 2: Base NERV Theme Implementation (Colors & Geometry) [checkpoint: 9c38ea7]
+- [x] Task: Implement the "Black Void" and "Phosphor" color palette in src/theme_nerv.py. 3fa4f64
+- [x] Task: Implement "Hard Edges" by setting all rounding parameters to 0.0 in the NERV theme. 3fa4f64
+- [x] Task: Write unit tests to verify that the NERV theme correctly applies colors and geometry settings. de0d9f3
+- [x] Task: Conductor - User Manual Verification 'Phase 2: Base NERV Theme Implementation' (Protocol in workflow.md) 9c38ea7
+
+## Phase 3: Visual Effects (Scanlines & Status Flickering) [checkpoint: ceb0c7d]
+- [x] Task: Research how to implement a scanline overlay in ImGui (e.g., using a full-screen transparent texture or a custom draw list). 05a2b8e
+- [x] Task: Implement the subtle scanline overlay (6% opacity). 05a2b8e
+- [x] Task: Implement "Status Flickering" logic for active system indicators (e.g., a periodic alpha modification for specific text elements). 05a2b8e
+- [x] Task: Write tests to verify the visual effect triggers (e.g., checking if the scanline overlay is rendered). 4f4fa10
+- [x] Task: Conductor - User Manual Verification 'Phase 3: Visual Effects' (Protocol in workflow.md) ceb0c7d
+
+## Phase 4: Alert Pulsing & Error States [checkpoint: d9495f6]
+- [x] Task: Implement "Alert Pulsing" logic that can be triggered by application error events. d9495f6
+- [x] Task: Integrate Alert Pulsing with the NERV theme (shifting borders/background to Alert Red). d9495f6
+- [x] Task: Write tests to verify that an error state triggers the pulsing effect in the NERV theme. d9495f6
+- [x] Task: Conductor - User Manual Verification 'Phase 4: Alert Pulsing & Error States' (Protocol in workflow.md) d9495f6
+
+## Phase 5: Integration & Theme Selector [checkpoint: afcb1bf]
+- [x] Task: Add "NERV" to the theme selection dropdown in src/gui_2.py. afcb1bf
+- [x] Task: Ensure that switching to the NERV theme correctly initializes all visual effects (scanlines, etc.). afcb1bf
+- [x] Task: Final UX verification and performance check of the NERV theme. afcb1bf
+- [x] Task: Conductor - User Manual Verification 'Phase 5: Integration & Theme Selector' (Protocol in workflow.md) afcb1bf
+
+## Phase 6: NERV Theme Refinement (Contrast & Readability) [checkpoint: 9facecb]
+- [x] Task: Fix text readability by ensuring high-contrast text on bright backgrounds (e.g., black text on orange title bars). 9facecb
+- [x] Task: Adjust the NERV palette to use Data Green or Steel for standard text, reserving Orange for accents and backgrounds. 9facecb
+- [x] Task: Update gui_2.py to push/pop style colors for headers if necessary to maintain readability. 9facecb
+- [x] Task: Conductor - User Manual Verification 'Phase 6: NERV Theme Refinement' (Protocol in workflow.md) 9facecb
+
+## Phase 7: CRT Filter Implementation [checkpoint: e635c29]
+- [x] Task: Research and implement a more sophisticated "CRT Filter" beyond simple scanlines (e.g., adding a vignette, noise, or subtle color aberration). e635c29
+- [x] Task: Implement a "CRT Filter" toggle in the theme settings. e635c29
+- [x] Task: Integrate the new CRT filter into the gui_2.py rendering loop. e635c29
+- [x] Task: Conductor - User Manual Verification 'Phase 7: CRT Filter Implementation' (Protocol in workflow.md) e635c29

conductor/archive/nerv_ui_theme_20260309/spec.md

@@ -0,0 +1,37 @@
+# Specification: NERV UI Theme Integration
+
+## Overview
+This track aims to implement a new "NERV" visual theme for the manual_slop application, inspired by the aesthetic of technical/military consoles (e.g., Evangelion's NERV UI). The theme will be added as a selectable option within the application, allowing users to switch between the existing theme and the new NERV style without altering the core user experience or layout.
+
+## Functional Requirements
+- **Theme Selection:** Integrate a "NERV" theme option into the existing UI (e.g., in the configuration or theme settings).
+- **Color Palette:** Implement the "Black Void" aesthetic using absolute black (#000000) for the background and CRT-inspired phosphor colors:
+    - **NERV Orange (#FF9830):** Primary accents, headers, active borders.
+    - **Data Green (#50FF50):** Terminal output, "Nominal" status, standard data.
+    - **Wire Cyan (#20F0FF):** Structural separators, inactive borders.
+    - **Alert Red (#FF4840):** Error states, critical alerts.
+    - **Steel (#E0E0D8):** Secondary text, timestamps.
+- **Hard Edges:** Configure all UI elements (windows, frames, buttons) to have zero rounded corners (Rounding = 0.0).
+- **Typography:** Utilize a monospace font (e.g., IBM Plex Mono or the project's current monospace font) for all text to maintain a technical look.
+- **Visual Effects:**
+    - **Scanline Overlay:** Implement a subtle CRT-style scanline overlay (approx. 6% opacity).
+    - **Status Flickering:** Add subtle flickering effects to active system status indicators.
+    - **Alert Pulsing:** Implement red background or border pulsing during error or critical system states.
+
+## Non-Functional Requirements
+- **Performance:** Ensure the scanline overlay and status flickering do not significantly degrade UI responsiveness or increase CPU usage.
+- **Maintainability:** The theme should be implemented in a way that is consistent with the existing theme.py or theme_2.py architecture.
+
+## Acceptance Criteria
+- [ ] Users can select "NERV" from the theme selector.
+- [ ] The background is solid black (#000000).
+- [ ] All borders and buttons have zero rounded corners.
+- [ ] The NERV color palette is correctly applied to all UI elements.
+- [ ] The scanline overlay is visible and subtle.
+- [ ] Active status indicators exhibit the "Status Flickering" effect.
+- [ ] Errors trigger the "Alert Pulsing" effect.
+
+## Out of Scope
+- **Bilingual Labels:** Japanese sub-labels will not be implemented.
+- **Layout Changes:** No radical changes to window positioning or spacing.
+- **New Features:** This track is purely visual and does not add new application functionality.

conductor/archive/on_demand_def_lookup_20260306/plan.md

@@ -0,0 +1,49 @@
+# Implementation Plan: On-Demand Definition Lookup (on_demand_def_lookup_20260306)
+
+> **Reference:** [Spec](./spec.md) | [Architecture Guide](../../../docs/guide_architecture.md)
+
+## Phase 1: Symbol Parsing [checkpoint: f392aa3]
+Focus: Parse @symbol syntax from user input
+
+- [x] Task 1.1: Initialize MMA Environment
+- [x] Task 1.2: Implement @symbol regex parser (a0a9d00)
+    - WHERE: `src/gui_2.py` in `_send_callback()`
+    - WHAT: Extract @SymbolName patterns
+    - HOW:
+      ```python
+      import re
+      def parse_symbols(text: str) -> list[str]:
+       return re.findall(r'@(\w+(?:\.\w+)*)', text)
+      ```
+
+## Phase 2: Definition Retrieval
+Focus: Use existing MCP tool to get definitions
+
+- [x] Task 2.1: Integrate py_get_definition (c6f9dc8)
+    - WHERE: `src/gui_2.py`
+    - WHAT: Call MCP tool for each symbol
+    - HOW:
+      ```python
+      from src import mcp_client
+      def get_symbol_definition(symbol: str, files: list[str]) -> tuple[str, str] | None:
+       for file_path in files:
+        result = mcp_client.py_get_definition(file_path, symbol)
+        if result and "not found" not in result.lower():
+         return (file_path, result)
+       return None
+      ```
+
+## Phase 3: Inline Display [checkpoint: 7ea833e]
+Focus: Display definition in discussion
+
+- [x] Task 3.1: Inject definition as context (7ea833e)
+
+## Phase 4: Click Navigation [checkpoint: 7ea833e]
+Focus: Allow clicking definition to open file
+
+- [x] Task 4.1: Store file/line metadata with definition (7ea833e)
+- [x] Task 4.2: Add click handler (7ea833e)
+
+## Phase 5: Testing [checkpoint: 7ea833e]
+- [x] Task 5.1: Write unit tests for parsing (7ea833e)
+- [x] Task 5.2: Conductor - Phase Verification (7ea833e)

conductor/archive/opencode_config_overhaul_20260310/metadata.json

@@ -0,0 +1,10 @@
+{
+  "id": "opencode_config_overhaul_20260310",
+  "title": "OpenCode Configuration Overhaul",
+  "type": "fix",
+  "status": "completed",
+  "priority": "high",
+  "created": "2026-03-10",
+  "depends_on": [],
+  "blocks": []
+}

conductor/archive/opencode_config_overhaul_20260310/plan.md

@@ -0,0 +1,23 @@
+# Implementation Plan: OpenCode Configuration Overhaul
+
+## Phase 1: Core Config and Agent Temperature/Step Fixes [checkpoint: 02abfc4]
+
+- [x] Task 1.1: Update `opencode.json` - set `compaction.auto: false`, `compaction.prune: false`
+- [x] Task 1.2: Update `.opencode/agents/tier1-orchestrator.md` - remove `steps: 50`, change `temperature: 0.4` to `0.5`, add "Context Management" section
+- [x] Task 1.3: Update `.opencode/agents/tier2-tech-lead.md` - remove `steps: 100`, change `temperature: 0.2` to `0.4`, add "Context Management" and "Pre-Delegation Checkpoint" sections
+- [x] Task 1.4: Update `.opencode/agents/tier3-worker.md` - remove `steps: 20`, change `temperature: 0.1` to `0.3`
+- [x] Task 1.5: Update `.opencode/agents/tier4-qa.md` - remove `steps: 5`, change `temperature: 0.0` to `0.2`
+- [x] Task 1.6: Update `.opencode/agents/general.md` - remove `steps: 15`, change `temperature: 0.2` to `0.3`
+- [x] Task 1.7: Update `.opencode/agents/explore.md` - remove `steps: 8`, change `temperature: 0.0` to `0.2`
+- [x] Task 1.8: Conductor - User Manual Verification (verified)
+
+## Phase 2: MMA Tier Command Expansion [checkpoint: 02abfc4]
+
+- [x] Task 2.1: Expand `.opencode/commands/mma-tier1-orchestrator.md` - add full Surgical Methodology, limitations, context section
+- [x] Task 2.2: Expand `.opencode/commands/mma-tier2-tech-lead.md` - add TDD protocol, Pre-Delegation Checkpoint, delegation patterns
+- [x] Task 2.3: Expand `.opencode/commands/mma-tier3-worker.md` - add key constraints, task execution, blocking protocol
+- [x] Task 2.4: Expand `.opencode/commands/mma-tier4-qa.md` - add key constraints, analysis protocol, structured output format
+- [x] Task 2.5: Conductor - User Manual Verification (verified)
+
+## Phase: Review Fixes
+- [x] Task: Apply review suggestions 8c5b5d3

conductor/archive/opencode_config_overhaul_20260310/spec.md

@@ -0,0 +1,54 @@
+# Track Specification: OpenCode Configuration Overhaul
+
+## Overview
+Fix critical gaps in OpenCode agent configuration that cause MMA workflow failures. Remove step limits that prematurely terminate complex tracks, disable automatic context compaction that loses critical session state, raise temperature for better problem-solving, and expand thin command wrappers into full protocol documentation.
+
+## Current State Audit (as of HEAD)
+
+### Already Implemented (DO NOT re-implement)
+- OpenCode MCP integration working (`opencode.json:17-25`)
+- Agent persona files exist for all 4 MMA tiers (`.opencode/agents/tier*.md`)
+- Conductor commands exist (`.opencode/commands/conductor-*.md`)
+- MMA tier commands exist but are thin wrappers (`.opencode/commands/mma-tier*.md`)
+
+### Gaps to Fill (This Track's Scope)
+
+1. **Step Limits**: All agents have restrictive `steps` limits:
+   - tier1: 50, tier2: 100, tier3: 20, tier4: 5
+   - These terminate complex track implementations prematurely
+
+2. **Auto-Compaction**: `opencode.json` has `compaction.auto: true` which loses session context without user control
+
+3. **Temperature Too Low**: 
+   - tier2: 0.2, tier3: 0.1, tier4: 0.0
+   - Reduces creative problem-solving for complex tracks
+
+4. **Thin Command Wrappers**: `mma-tier*.md` commands are 3-4 lines, lacking:
+   - Pre-delegation checkpoint protocol
+   - TDD phase confirmation requirements
+   - Blocking protocol
+   - Context management guidance
+
+## Goals
+- Remove all step limits from agent configurations
+- Disable automatic compaction, enforce manual-only via `/compact`
+- Raise temperatures to 0.2-0.5 range for better reasoning
+- Expand MMA tier commands with full protocol documentation
+
+## Functional Requirements
+- All 6 agent files updated with removed `steps` and adjusted `temperature`
+- `opencode.json` updated with `compaction.auto: false, prune: false`
+- All 4 MMA tier commands expanded with context, protocols, and patterns
+
+## Non-Functional Requirements
+- No functional changes to MCP tool usage or permissions
+- Maintain backward compatibility with existing workflow
+
+## Architecture Reference
+- `docs/guide_mma.md` - 4-tier architecture, worker lifecycle, context amnesia
+- `docs/guide_meta_boundary.md` - Application vs Meta-Tooling distinction
+
+## Out of Scope
+- Model tiering (using different models per tier)
+- Changes to Gemini CLI configuration
+- Changes to conductor workflow itself

conductor/archive/per_ticket_model_20260306/plan.md

@@ -0,0 +1,53 @@
+# Implementation Plan: Per-Ticket Model Override (per_ticket_model_20260306)
+
+> **Reference:** [Spec](./spec.md) | [Architecture Guide](../../../docs/guide_architecture.md)
+
+## Phase 1: Model Override Field
+Focus: Add field to Ticket dataclass
+
+- [x] Task 1.1: Initialize MMA Environment
+- [x] Task 1.2: Add model_override to Ticket (245653c)
+    - WHERE: `src/models.py` `Ticket` dataclass
+    - WHAT: Add optional model override field
+    - HOW:
+      ```python
+      @dataclass
+      class Ticket:
+       # ... existing fields ...
+       model_override: Optional[str] = None
+      ```
+
+- [x] Task 1.3: Update serialization (245653c)
+    - WHERE: `src/models.py` `Ticket.to_dict()` and `from_dict()`
+    - WHAT: Include model_override
+    - HOW: Add field to dict conversion
+
+## Phase 2: Model Dropdown UI
+Focus: Add model selection to ticket display
+
+- [x] Task 2.1: Get available models list (63d1b04)
+
+- [x] Task 2.2: Add dropdown to ticket UI (63d1b04)
+
+- [x] Task 3.1: Color-code override tickets (63d1b04)
+
+## Phase 4: Execution Integration
+Focus: Use override in worker execution
+
+- [x] Task 4.1: Check override in ConductorEngine.run() (e20f8a1)
+    - WHERE: `src/multi_agent_conductor.py` `run()`
+    - WHAT: Use ticket.model_override if set
+    - HOW:
+      ```python
+      if ticket.model_override:
+       model_name = ticket.model_override
+      else:
+       # Use existing escalation logic
+       models = ["gemini-2.5-flash-lite", "gemini-2.5-flash", "gemini-3.1-pro-preview"]
+       model_idx = min(ticket.retry_count, len(models) - 1)
+       model_name = models[model_idx]
+      ```
+
+## Phase 5: Testing
+- [x] Task 5.1: Write unit tests
+- [x] Task 5.2: Conductor - Phase Verification

conductor/archive/pipeline_pause_resume_20260306/plan.md

@@ -5,8 +5,8 @@
 ## Phase 1: Pause Mechanism
 Focus: Add pause event to ConductorEngine
 
-- [ ] Task 1.1: Initialize MMA Environment
+- [x] Task 1.1: Initialize MMA Environment
-- [ ] Task 1.2: Add pause event to ConductorEngine
+- [x] Task 1.2: Add pause event to ConductorEngine (0c3a206)
     - WHERE: `src/multi_agent_conductor.py` `ConductorEngine.__init__`
     - WHAT: Threading event for pause control
     - HOW:
@@ -14,7 +14,7 @@ Focus: Add pause event to ConductorEngine
       self._pause_event: threading.Event = threading.Event()
       ```
 
-- [ ] Task 1.3: Check pause in run loop
+- [x] Task 1.3: Check pause in run loop (0c3a206)
     - WHERE: `src/multi_agent_conductor.py` `run()`
     - WHAT: Wait while paused
     - HOW:
@@ -29,18 +29,18 @@ Focus: Add pause event to ConductorEngine
 ## Phase 2: Pause/Resume Methods
 Focus: Add control methods
 
-- [ ] Task 2.1: Add pause method
+- [x] Task 2.1: Add pause method (0c3a206)
     - WHERE: `src/multi_agent_conductor.py`
     - HOW: `self._pause_event.set()`
 
-- [ ] Task 2.2: Add resume method
+- [x] Task 2.2: Add resume method (0c3a206)
     - WHERE: `src/multi_agent_conductor.py`
     - HOW: `self._pause_event.clear()`
 
 ## Phase 3: UI Controls
 Focus: Add pause/resume buttons
 
-- [ ] Task 3.1: Add pause/resume button
+- [x] Task 3.1: Add pause/resume button (3cb7d4f)
     - WHERE: `src/gui_2.py` MMA dashboard
     - WHAT: Toggle button for pause state
     - HOW:
@@ -54,7 +54,7 @@ Focus: Add pause/resume buttons
         engine.pause()
       ```
 
-- [ ] Task 3.2: Add visual indicator
+- [x] Task 3.2: Add visual indicator (3cb7d4f)
     - WHERE: `src/gui_2.py`
     - WHAT: Banner or color when paused
     - HOW:
@@ -64,5 +64,5 @@ Focus: Add pause/resume buttons
       ```
 
 ## Phase 4: Testing
-- [ ] Task 4.1: Write unit tests
+- [x] Task 4.1: Write unit tests
-- [ ] Task 4.2: Conductor - Phase Verification
+- [x] Task 4.2: Conductor - Phase Verification