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.

manualslop_layout.ini

@@ -78,8 +78,8 @@ Collapsed=0
 DockId=0x0000000F,2
 
 [Window][Theme]
-Pos=0,21
-Size=639,824
+Pos=0,17
+Size=393,824
 Collapsed=0
 DockId=0x00000005,1
 
@@ -89,14 +89,14 @@ Size=900,700
 Collapsed=0
 
 [Window][Diagnostics]
-Pos=641,21
-Size=1092,908
+Pos=395,17
+Size=734,545
 Collapsed=0
 DockId=0x00000010,0
 
 [Window][Context Hub]
-Pos=0,21
-Size=639,824
+Pos=0,17
+Size=393,824
 Collapsed=0
 DockId=0x00000005,0
 
@@ -107,26 +107,26 @@ Collapsed=0
 DockId=0x0000000D,0
 
 [Window][Discussion Hub]
-Pos=1735,21
-Size=783,1586
+Pos=1131,17
+Size=549,1183
 Collapsed=0
 DockId=0x00000004,0
 
 [Window][Operations Hub]
-Pos=641,21
-Size=1092,908
+Pos=395,17
+Size=734,545
 Collapsed=0
 DockId=0x00000010,1
 
 [Window][Files & Media]
-Pos=0,847
-Size=639,760
+Pos=0,843
+Size=393,357
 Collapsed=0
 DockId=0x00000006,1
 
 [Window][AI Settings]
-Pos=0,847
-Size=639,760
+Pos=0,843
+Size=393,357
 Collapsed=0
 DockId=0x00000006,0
 
@@ -136,14 +136,14 @@ Size=416,325
 Collapsed=0
 
 [Window][MMA Dashboard]
-Pos=641,931
-Size=1092,676
+Pos=395,564
+Size=734,636
 Collapsed=0
 DockId=0x00000011,0
 
 [Window][Log Management]
-Pos=1735,21
-Size=783,1586
+Pos=1131,17
+Size=549,1183
 Collapsed=0
 DockId=0x00000004,1
 
@@ -167,20 +167,20 @@ Column 6  Weight=1.0079
 DockNode              ID=0x00000008 Pos=3125,170 Size=593,1157 Split=Y
   DockNode            ID=0x00000009 Parent=0x00000008 SizeRef=1029,147 Selected=0x0469CA7A
   DockNode            ID=0x0000000A Parent=0x00000008 SizeRef=1029,145 Selected=0xDF822E02
-DockSpace             ID=0xAFC85805 Window=0x079D3A04 Pos=0,21 Size=2518,1586 Split=Y
+DockSpace             ID=0xAFC85805 Window=0x079D3A04 Pos=0,17 Size=1680,1183 Split=Y
   DockNode            ID=0x0000000C Parent=0xAFC85805 SizeRef=1362,1041 Split=X Selected=0x5D11106F
-    DockNode          ID=0x00000003 Parent=0x0000000C SizeRef=1733,1183 Split=X
+    DockNode          ID=0x00000003 Parent=0x0000000C SizeRef=1129,1183 Split=X
       DockNode        ID=0x0000000B Parent=0x00000003 SizeRef=404,1186 Split=Y Selected=0xF4139CA2
         DockNode      ID=0x00000002 Parent=0x0000000B SizeRef=1029,1119 Split=X Selected=0xF4139CA2
-          DockNode    ID=0x00000007 Parent=0x00000002 SizeRef=639,858 Split=Y Selected=0x8CA2375C
+          DockNode    ID=0x00000007 Parent=0x00000002 SizeRef=393,858 Split=Y Selected=0x8CA2375C
             DockNode  ID=0x00000005 Parent=0x00000007 SizeRef=295,824 Selected=0xF4139CA2
             DockNode  ID=0x00000006 Parent=0x00000007 SizeRef=295,724 CentralNode=1 Selected=0x7BD57D6A
-          DockNode    ID=0x0000000E Parent=0x00000002 SizeRef=1092,858 Split=Y Selected=0x418C7449
-            DockNode  ID=0x00000010 Parent=0x0000000E SizeRef=868,887 Selected=0x418C7449
-            DockNode  ID=0x00000011 Parent=0x0000000E SizeRef=868,661 Selected=0x3AEC3498
+          DockNode    ID=0x0000000E Parent=0x00000002 SizeRef=734,858 Split=Y Selected=0x418C7449
+            DockNode  ID=0x00000010 Parent=0x0000000E SizeRef=868,545 Selected=0xB4CBF21A
+            DockNode  ID=0x00000011 Parent=0x0000000E SizeRef=868,636 Selected=0x3AEC3498
         DockNode      ID=0x00000001 Parent=0x0000000B SizeRef=1029,775 Selected=0x8B4EBFA6
       DockNode        ID=0x0000000D Parent=0x00000003 SizeRef=435,1186 Selected=0x363E93D6
-    DockNode          ID=0x00000004 Parent=0x0000000C SizeRef=783,1183 Selected=0x6F2B5B04
+    DockNode          ID=0x00000004 Parent=0x0000000C SizeRef=549,1183 Selected=0x6F2B5B04
   DockNode            ID=0x0000000F Parent=0xAFC85805 SizeRef=1362,451 Selected=0xDD6419BC
 
 ;;;<<<Layout_655921752_Default>>>;;;

reproduce_issue.py

@@ -0,0 +1,36 @@
+import pytest
+from models import Ticket
+from dag_engine import TrackDAG, ExecutionEngine
+
+def test_auto_queue_and_step_mode():
+    t1 = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker")
+    t2 = Ticket(id="T2", description="Task 2", status="todo", assigned_to="worker", step_mode=True)
+    
+    dag = TrackDAG([t1, t2])
+    # Expectation: ExecutionEngine takes auto_queue parameter
+    try:
+        engine = ExecutionEngine(dag, auto_queue=True)
+    except TypeError:
+        pytest.fail("ExecutionEngine does not accept auto_queue parameter")
+
+    # Tick 1: T1 should be 'in-progress' because auto_queue=True
+    # T2 should remain 'todo' because step_mode=True
+    engine.tick()
+    
+    assert t1.status == "in_progress"
+    assert t2.status == "todo"
+    
+    # Approve T2
+    try:
+        engine.approve_task("T2")
+    except AttributeError:
+        pytest.fail("ExecutionEngine does not have approve_task method")
+        
+    assert t2.status == "in_progress"
+
+if __name__ == "__main__":
+    try:
+        test_auto_queue_and_step_mode()
+        print("Test passed (unexpectedly)")
+    except Exception as e:
+        print(f"Test failed as expected: {e}")

tests/temp_project_history.toml

@@ -18,7 +18,7 @@ history = [
 
 [discussions.AutoDisc]
 git_commit = ""
-last_updated = "2026-02-27T19:23:11"
+last_updated = "2026-02-27T19:27:19"
 history = [
     "@2026-02-27T19:08:37\nSystem:\n[PERFORMANCE ALERT] Frame time high: 62.2ms. Please consider optimizing recent changes or reducing load.",
 ]