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 as a Tier 1 Product Manager or Tier 2 Tech Lead within the MMA Framework. 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 Contributors" or "Tier 4 QA Agents" by spawning secondary Gemini CLI instances via run_shell_command.

CRITICAL Prerequisite: To avoid hanging the CLI and ensure proper environment authentication, you MUST NOT call the gemini command directly. Instead, you MUST use the wrapper script: .\scripts un_subagent.ps1 -Prompt "..."

1. The Tier 3 Worker (Heads-Down Coding)

When you need to perform a significant code modification (e.g., refactoring a 500-line script, writing a massive class, or implementing a predefined spec):

1. DO NOT attempt to write or use replace/write_file yourself. Your history will bloat.
2. DO construct a single, highly specific prompt.
3. DO spawn a sub-agent using run_shell_command pointing to the target file. Command: .\scripts un_subagent.ps1 -Prompt "Modify [FILE_PATH] to implement [SPECIFIC_INSTRUCTION]. Only write the code, no pleasantries."
4. If you need the sub-agent to automatically apply changes instead of just returning the text, use gemini run or pipe the output appropriately. However, the best method is to let the sub-agent modify the code and return "Done."

2. The Tier 4 QA Agent (Error Translation)

If you run a local test (e.g., npm test, pytest, go run) via run_shell_command and it fails with a massive traceback (e.g., 200+ lines of stderr):

1. DO NOT analyze the raw stderr in your own context window.
2. DO immediately spawn a stateless Tier 4 agent to compress the error.
3. Command: .\scripts un_subagent.ps1 -Prompt "Summarize this stack trace into a 20-word fix: [PASTE_SNIPPET_OF_STDERR_HERE]"
4. Use the 20-word fix returned by the Tier 4 agent to inform your next architectural decision or pass it to the Tier 3 worker.

3. Context Amnesia (Phase Checkpoints)

When you complete a major Phase or Track within the conductor workflow:

1. Stage your changes and commit them.
2. Draft a comprehensive summary of the state changes in a Git Note attached to the commit.
3. Treat the checkpoint as a "Memory Wipe." Actively disregard previous conversational turns and trial-and-error histories. Rely exclusively on the newly generated Git Note and the physical state of the files on disk for your next Phase.

### 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": ".\scripts un_subagent.ps1 -Prompt "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": ".\scripts
un_subagent.ps1 -Prompt "Read file_cache.py and implement the ASTParser class using tree-sitter. Ensure you preserve docstrings but strip function bodies. Output the updated code or edit the file directly."",
  "description": "Delegating implementation to a Tier 3 Worker."
}

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