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

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.