# Implementation Plan: MMA Utilization Refinement

## Phase 1: Skill Segregation and Tier Re-Alignment
- [ ] Task: Refine `mma-tier1-orchestrator` skill to focus exclusively on project/track initialization.
- [ ] Task: Refine `mma-tier2-tech-lead` skill for track execution, ensuring persistent memory across tasks (Disable Context Amnesia).
- [ ] Task: Refine `mma-tier3-worker` and `mma-tier4-qa` skills to be stateless (Enable Context Amnesia) but equipped with full file read/write tools.
- [ ] Task: Conductor - User Manual Verification 'Phase 1' (Protocol in workflow.md)

## Phase 2: AST Skeleton Extraction (Skeleton Views)
- [ ] Task: Enhance `mcp_client.py` with `get_python_skeleton` functionality using `tree-sitter` to extract signatures and docstrings.
- [ ] Task: Update `mma_exec.py` to utilize these skeletons for non-target dependencies when preparing context for Tier 3.
- [ ] Task: Integrate "Interface-level" scrubbed versions into the sub-agent injection logic.
- [ ] Task: Conductor - User Manual Verification 'Phase 2' (Protocol in workflow.md)

## Phase 3: Sub-Agent Observability
- [ ] Task: Implement a dedicated logging mechanism for sub-agents (e.g., `logs/mma_subagents.log`) that captures reasoning and tool output.
- [ ] Task: Ensure sub-agent executions do not pollute the primary Gemini CLI history while remaining visible to the user via the log.
- [ ] Task: Conductor - User Manual Verification 'Phase 3' (Protocol in workflow.md)

## Phase 4: Workflow Optimization and Validation
- [ ] Task: Update `conductor/workflow.md` to formally document the refined tier roles and tool permissions.
- [ ] Task: Conduct a full end-to-end "Dry Run" (Create a dummy track and implement a small feature) to verify the new architecture.
- [ ] Task: Conductor - User Manual Verification 'Phase 4' (Protocol in workflow.md)