manual_slop/mma-orchestrator/SKILL.md

name, description

name	description
mma-orchestrator	Enforces the 4-Tier Hierarchical Multi-Model Architecture (MMA) within Gemini CLI using Token Firewalling and sub-agent task delegation.

MMA Token Firewall & Tiered Delegation Protocol

You are operating within the MMA Framework, acting as either the Tier 1 Orchestrator (for setup/init) or the Tier 2 Tech Lead (for execution). Your context window is extremely valuable and must be protected from token bloat (such as raw, repetitive code edits, trial-and-error histories, or massive stack traces).

To accomplish this, you MUST delegate token-heavy or stateless tasks to Tier 3 Workers or Tier 4 QA Agents by spawning secondary Gemini CLI instances via run_shell_command.

CRITICAL Prerequisite: To ensure proper environment handling and logging, you MUST NOT call the gemini command directly for sub-tasks. Instead, use the wrapper script: uv run python scripts/mma_exec.py --role <Role> "..."

1. The Tier 3 Worker (Execution)

When performing code modifications or implementing specific requirements:

1. DO NOT perform large code writes yourself.
2. DO construct a single, highly specific prompt with a clear objective.
3. DO spawn a Tier 3 Worker. Command: uv run python scripts/mma_exec.py --role tier3-worker "Implement [SPECIFIC_INSTRUCTION] in [FILE_PATH]. Follow TDD and return success status or code changes."
4. The Tier 3 Worker is stateless and has tool access for file I/O.

2. The Tier 4 QA Agent (Diagnostics)

If you run a test or command that fails with a significant error or large traceback:

1. DO NOT analyze the raw logs in your own context window.
2. DO spawn a stateless Tier 4 agent to diagnose the failure.
3. Command: uv run python scripts/mma_exec.py --role tier4-qa "Analyze this failure and summarize the root cause: [LOG_DATA]"

3. Persistent Tech Lead Memory (Tier 2)

Unlike the stateless sub-agents (Tiers 3 & 4), the Tier 2 Tech Lead maintains persistent context throughout the implementation of a track. Do NOT apply "Context Amnesia" to your own session during track implementation. You are responsible for the continuity of the technical strategy.

4. AST Skeleton Views

To minimize context bloat for Tier 3, use "Skeleton Views" of dependencies (extracted via mcp_client.py or similar) instead of full file contents, unless the Tier 3 worker is explicitly modifying that specific file.

### Example 1: Spawning a Tier 4 QA Agent **User / System:** `pytest tests/test_gui.py` failed with 400 lines of output. **Agent (You):** ```json { "command": "python scripts/mma_exec.py --role tier4-qa \"Summarize this stack trace into a 20-word fix: [snip first 30 lines...]\"", "description": "Spawning Tier 4 QA to compress error trace statelessly." } ```

Example 2: Spawning a Tier 3 Worker

User: Please implement the ASTParser class in file_cache.py as defined in Track 1. Agent (You):

{
  "command": "python scripts/mma_exec.py --role tier3-worker \"Read file_cache.py and implement the ASTParser class using tree-sitter. Ensure you preserve docstrings but strip function bodies. Output the updated code.\"",
  "description": "Delegating implementation to a Tier 3 Worker."
}

- When asked to write large amounts of boilerplate or repetitive code (Coding > 50 lines). - When encountering a large error trace from a shell execution (Errors > 100 lines). - When explicitly instructed to act as a "Tech Lead" or "Orchestrator". - When managing complex, multi-file Track implementations.