chore(conductor): Archive old track and initialize 4 new Phase 2 MMA tracks

conductor/tracks.md

@@ -20,8 +20,23 @@ This file tracks all major tracks for the project. Each track has its own detail
 
 ---
 
-- [x] **Track: MMA Orchestrator Integration**
+- [ ] **Track: MMA Data Architecture & DAG Engine**
-*Link: [./tracks/mma_orchestrator_integration_20260226/](./tracks/mma_orchestrator_integration_20260226/)*
+*Link: [./tracks/mma_data_architecture_dag_engine/](./tracks/mma_data_architecture_dag_engine/)*
+
+---
+
+- [ ] **Track: Tiered Context Scoping & HITL Approval**
+*Link: [./tracks/tiered_context_scoping_hitl_approval/](./tracks/tiered_context_scoping_hitl_approval/)*
+
+---
+
+- [ ] **Track: MMA Dashboard Visualization Overhaul**
+*Link: [./tracks/mma_dashboard_visualization_overhaul/](./tracks/mma_dashboard_visualization_overhaul/)*
+
+---
+
+- [ ] **Track: Robust Live Simulation Verification**
+*Link: [./tracks/robust_live_simulation_verification/](./tracks/robust_live_simulation_verification/)*
 
 ---
 

conductor/tracks/mma_dashboard_visualization_overhaul/plan.md

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+# Implementation Plan: MMA Dashboard Visualization Overhaul
+
+## Phase 1: Track Browser Panel
+- [ ] Task: Implement a list view in the MMA Dashboard that reads from the `tracks` directory.
+- [ ] Task: Add functionality to select an active track and load its state into the UI.
+- [ ] Task: Display progress bars based on task completion within the active track.
+
+## Phase 2: DAG Visualizer Component
+- [ ] Task: Design the layout for the Task DAG using DearPyGui Node Editor or collapsible Tree Nodes.
+- [ ] Task: Write the data-binding logic to map the backend Python DAG (from Track 1) to the UI visualizer.
+- [ ] Task: Add visual indicators (colors/icons) for Task statuses (Ready, Blocked, Done).
+
+## Phase 3: Live Output Streams
+- [ ] Task: Refactor the AI response handling to support multiple concurrent UI text streams.
+- [ ] Task: Bind the output of Tier 1 (Planning) to a designated "Strategy" text box.
+- [ ] Task: Bind the output of Tier 2 and spawned Tier 3/4 workers to the active Task's detail view in the DAG.

conductor/tracks/mma_dashboard_visualization_overhaul/spec.md

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+# Track Specification: MMA Dashboard Visualization Overhaul
+
+## Overview
+Make the invisible backend operations visible and interactive. The current GUI is too barebones to effectively manage a multi-agent system. This track overhauls the MMA Dashboard to provide real-time insights into tracks, task dependencies, and agent streams.
+
+## Goals
+1.  **Track Browser:** Create a dedicated panel to view all tracks, their statuses, and overall progress metrics.
+2.  **DAG Visualizer:** Implement a visual representation (hierarchical list or node graph) of the current Track's tasks, clearly indicating status and blockers.
+3.  **Live Output Streams:** Ensure the GUI text boxes stream the output of Tier 1 (Planning), Tier 2 (Directing), and Tiers 3/4 (Executing) in real-time, tied precisely to their respective tasks.
+
+## Constraints
+-   Must be built natively using DearPyGui capabilities (e.g., node editor or nested tree nodes).
+-   Must be performant and not block the main rendering loop when updating live text streams.

conductor/tracks/mma_data_architecture_dag_engine/plan.md

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+# Implementation Plan: MMA Data Architecture & DAG Engine
+
+## Phase 1: Track-Scoped State Management
+- [ ] Task: Define the data schema for a Track (Metadata, Discussion History, Task List).
+- [ ] Task: Update `project_manager.py` to create and read from `tracks/<track_id>/state.toml`.
+- [ ] Task: Ensure Tier 2 (Tech Lead) history is securely scoped to the active track's state file.
+
+## Phase 2: Python DAG Engine
+- [ ] Task: Create a `Task` class with `status` (Blocked, Ready, In Progress, Review, Done) and `depends_on` fields.
+- [ ] Task: Implement a topological sorting algorithm to resolve execution order.
+- [ ] Task: Write robust unit tests verifying cycle detection and dependency resolution.
+
+## Phase 3: Execution State Machine
+- [ ] Task: Implement the core loop that evaluates the DAG and identifies "Ready" tasks.
+- [ ] Task: Create configuration settings for "Auto-Queue" vs "Manual Step" execution modes.
+- [ ] Task: Connect the state machine to the backend dispatcher, preparing it for GUI integration.

conductor/tracks/mma_data_architecture_dag_engine/spec.md

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+# Track Specification: MMA Data Architecture & DAG Engine
+
+## Overview
+Restructure how `manual_slop` stores and executes work. The current implementation relies on global state and linear execution, which does not support the complexity of multi-agent, task-based workflows. This track establishes a robust, data-oriented foundation using track-scoped state and a native Python Directed Acyclic Graph (DAG) engine.
+
+## Goals
+1.  **Track-Scoped State:** Transition away from a single global `manual_slop_history.toml` to a per-track state structure (e.g., `tracks/<track_id>/state.toml`) to manage specific discussion history and context.
+2.  **Task DAG Engine:** Implement a topological sorter and DAG execution engine in Python to manage dependencies between tasks.
+3.  **Execution State Machine:** Build an internal state machine that governs whether the DAG auto-advances or waits for manual user intervention to spawn the next worker.
+
+## Constraints
+-   Must integrate seamlessly with the existing project TOML structure.
+-   Avoid external complex dependencies (like Steve Yegge's Beads) for now; rely on standard Python libraries or lightweight custom implementations to ensure tight coupling with the DearPyGui stack.

conductor/tracks/robust_live_simulation_verification/plan.md

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+# Implementation Plan: Robust Live Simulation Verification
+
+## Phase 1: Framework Foundation
+- [ ] Task: Create `tests/visual_sim_mma_v2.py` based on existing simulation patterns.
+- [ ] Task: Implement helper methods in `ApiHookClient` for querying specific DearPyGui item states (e.g., `get_text_value`, `get_node_status`).
+
+## Phase 2: Epic & Track Verification
+- [ ] Task: Write the simulation routine to trigger a new Epic and verify the Track Browser updates correctly.
+- [ ] Task: Verify that selecting a newly generated track successfully loads its initial (empty) state into the DAG visualizer.
+
+## Phase 3: DAG & Spawn Interception Verification
+- [ ] Task: Simulate the "Start Track" action and verify the DAG visualizer populates with tasks.
+- [ ] Task: Simulate the Auto-Queue advancing to a "Ready" task.
+- [ ] Task: Verify the "Approve Worker Spawn" modal appears with the correct prompt and context.
+- [ ] Task: Simulate clicking "Approve" and verify the worker's simulated output streams into the correct task detail view.

conductor/tracks/robust_live_simulation_verification/spec.md

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+# Track Specification: Robust Live Simulation Verification
+
+## Overview
+Establish a robust, visual simulation framework to prevent regressions in the complex GUI and asynchronous orchestration layers. This track replaces manual human verification with an automated script that clicks through the GUI and verifies the rendered state.
+
+## Goals
+1.  **Simulation Framework Setup:** Build a dedicated test script (`tests/visual_sim_mma_v2.py`) utilizing `ApiHookClient` to control the live GUI.
+2.  **Simulate Epic Planning:** Automate the clicking of "New Epic", inputting a prompt, and verifying the expected Tier 1 tracks appear in the UI.
+3.  **Simulate Execution & Spawning:** Automate the selection of a track, the generation of the DAG, and the interaction with the HITL Approval modal.
+
+## Constraints
+-   Must run against a live instance of the application using `--enable-test-hooks`.
+-   Must fail loudly if the visual state (e.g., rendered DAG nodes, text box contents) does not match expectations.

conductor/tracks/tiered_context_scoping_hitl_approval/plan.md

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+# Implementation Plan: Tiered Context Scoping & HITL Approval
+
+## Phase 1: Context Subsetting
+- [ ] Task: Refactor `aggregate.py` to support targeted context builds (e.g., `build_tier1_context`, `build_tier3_context`).
+- [ ] Task: Integrate AST skeleton extraction into the standard Tier 3 context build.
+- [ ] Task: Update the project state to track which files are assigned to which tier.
+
+## Phase 2: The Spawn Interceptor
+- [ ] Task: Create a signaling mechanism in `multi_agent_conductor.py` to emit a "Worker Spawn Requested" event.
+- [ ] Task: Implement the interception logic that pauses the async dispatcher until a signal is received from the GUI.
+
+## Phase 3: Approval UX Modal
+- [ ] Task: Design the "Approve Worker Spawn" modal in DearPyGui.
+- [ ] Task: Populate the modal with the target role, the exact prompt, and a read-only view of the specific file context.
+- [ ] Task: Wire the "Approve", "Modify", and "Reject" buttons to resume or cancel the intercepted spawn.

conductor/tracks/tiered_context_scoping_hitl_approval/spec.md

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+# Track Specification: Tiered Context Scoping & HITL Approval
+
+## Overview
+Provide the user with absolute visual control over what the AI sees at every level of the hierarchy. Currently, the system builds a single massive context blob. This track introduces context subsetting based on the target tier and implements a Human-in-the-Loop (HITL) approval gate before any Tier 3/4 worker is spawned.
+
+## Goals
+1.  **Context Subsetting:** Modify the aggregation logic so that Tier 1, Tier 2, and Tier 3/4 receive distinct, optimized context packages.
+2.  **Spawn Interceptor:** Implement a `BeforeSpawn` hook mechanism within the application to pause execution right before a sub-agent is launched.
+3.  **Approval UX:** Design and implement a GUI modal that displays the exact prompt and context intended for the worker, allowing the user to Approve, Modify, or Reject the spawn.
+
+## Constraints
+-   Must adhere to the project's security and transparency mandates.
+-   The interceptor must be reliable and not cause the main event loop to hang indefinitely.