checkpoint!

conductor/product.md

@@ -15,7 +15,7 @@ To serve as an expert-level utility for personal developer use on small projects
   - **Tier 2 (Tech Lead):** Technical oversight and track execution (`/conductor:implement`) using `gemini-2.5-flash`. Maintains persistent context throughout implementation.
   - **Tier 3 (Worker):** Surgical code implementation and TDD using `gemini-2.5-flash` or `deepseek-v3`. Operates statelessly with tool access and dependency skeletons.
   - **Tier 4 (QA):** Error analysis and diagnostics using `gemini-2.5-flash` or `deepseek-v3`. Operates statelessly with tool access.
-  - **MMA Delegation Engine:** Utilizes the `mma-exec` CLI and `mma.ps1` helper to route tasks, ensuring role-scoped context and detailed observability via timestamped sub-agent logs. Supports dynamic ticket creation and dependency resolution via an automated Dispatcher Loop.
+  - **MMA Delegation Engine:** Route tasks, ensuring role-scoped context and detailed observability via timestamped sub-agent logs. Supports dynamic ticket creation and dependency resolution via an automated Dispatcher Loop.
   - **Track-Scoped State Management:** Segregates discussion history and task progress into per-track state files (e.g., `conductor/tracks/<track_id>/state.toml`). This prevents global context pollution and ensures the Tech Lead session is isolated to the specific track's objective.
   - **Native DAG Execution Engine:** Employs a Python-based Directed Acyclic Graph (DAG) engine to manage complex task dependencies, supporting automated topological sorting and robust cycle detection.
   - **Programmable Execution State Machine:** Governing the transition between "Auto-Queue" (autonomous worker spawning) and "Step Mode" (explicit manual approval for each task transition).

conductor/tracks/mma_data_architecture_dag_engine/index.md

@@ -1,7 +0,0 @@
-# MMA Data Architecture & DAG Engine
-
-Restructures manual_slop state and execution into a per-track DAG model.
-
-### Navigation
-- [Specification](./spec.md)
-- [Implementation Plan](./plan.md)

conductor/tracks/mma_data_architecture_dag_engine/metadata.json

@@ -1,6 +0,0 @@
-{
-  "id": "mma_data_architecture_dag_engine",
-  "title": "MMA Data Architecture & DAG Engine",
-  "status": "planned",
-  "created_at": "2026-02-27T19:20:00.000000"
-}

conductor/tracks/mma_data_architecture_dag_engine/plan.md

@@ -1,19 +0,0 @@
-# Implementation Plan: MMA Data Architecture & DAG Engine
-
-## Phase 1: Track-Scoped State Management
-- [x] Task: Define the data schema for a Track (Metadata, Discussion History, Task List). [2efe80e]
-- [x] Task: Update `project_manager.py` to create and read from `tracks/<track_id>/state.toml`. [e1a3712]
-- [x] Task: Ensure Tier 2 (Tech Lead) history is securely scoped to the active track's state file. [b845b89]
-
-## Phase 2: Python DAG Engine
-- [x] Task: Create a `Task` class with `status` (Blocked, Ready, In Progress, Review, Done) and `depends_on` fields. [a3cfeff]
-- [x] Task: Implement a topological sorting algorithm to resolve execution order. [f85ec9d]
-- [x] Task: Write robust unit tests verifying cycle detection and dependency resolution. [f85ec9d]
-
-## Phase 3: Execution State Machine
-- [x] Task: Implement the core loop that evaluates the DAG and identifies "Ready" tasks. [154957f]
-- [x] Task: Create configuration settings for "Auto-Queue" vs "Manual Step" execution modes. [154957f]
-- [x] Task: Connect the state machine to the backend dispatcher, preparing it for GUI integration. [2429b7c]
-
-## Phase: Review Fixes
-- [x] Task: Apply review suggestions [6548ce6]

conductor/tracks/mma_data_architecture_dag_engine/spec.md

@@ -1,34 +0,0 @@
-# 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.
-
-## Context & Origins
-This track was born from the "Human Verification" phase of the initial MMA Orchestrator prototype (`mma_orchestrator_integration_20260226`). We realized that while the backend API plumbing for the hierarchical MMA tiers (Tiers 1-4) was technically functional, the product lacked the necessary state management, UX visualization, and human-in-the-loop security gates to be usable. 
-
-**Key Takeaways from the Prototype Phase:**
-- The Tier 2 (Tech Lead) needs its own track-scoped discussion history, rather than polluting the global project history.
-- Tasks within a track require a DAG (Directed Acyclic Graph) engine to manage complex dependencies and blocking states.
-- The GUI must visualize this DAG and stream the output of individual workers directly to their associated tasks.
-- We must enforce tiered context subsetting so that Tier 3/4 workers don't receive the massive global context blob, and we need a pre-spawn approval modal so the user can intercept, review, and modify worker prompts/contexts before they execute.
-
-**Instructions for the Implementing Agent:**
-As you execute this track, ensure you maintain alignment with the other Phase 2 tracks. If you learn something that impacts the dependent tracks, please append a similar "Context Summary" to their `spec.md` files before concluding your run.
-
-## Execution Order & Dependencies
-This is a multi-track phase. To ensure architectural integrity, these tracks **MUST** be executed in the following strict order:
-1. **[CURRENT] MMA Data Architecture & DAG Engine:** (Builds the state and execution foundation)
-2. **Tiered Context Scoping & HITL Approval:** (Builds the security and context subsetting on top of the state)
-3. **MMA Dashboard Visualization Overhaul:** (Builds the UI to visualize the state and subsets)
-4. **Robust Live Simulation Verification:** (Builds the tests to verify the UI and state)
-
-**Prerequisites for this track:** None. This must be executed FIRST.