# Skill: MMA Tiered Orchestrator

## Description
This skill enforces the 4-Tier Hierarchical Multi-Model Architecture (MMA) directly within the Gemini CLI using Token Firewalling and sub-agent task delegation. It teaches the CLI how to act as a Tier 1/2 Orchestrator, dispatching stateless tasks to cheaper models using shell commands, thereby preventing massive error traces or heavy coding contexts from polluting the primary prompt context.

<instructions>
# 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\run_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\run_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\run_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.
</instructions>

<examples>
### 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\\run_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):**
```json
{
  "command": ".\\scripts\\run_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."
}
```
</examples>

<triggers>
- 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.
</triggers>