feat(conductor): Consolidate MMA implementation into single multi-phase track and draft Agent Skill

MMA_Support/mma_tiered_orchestrator_skill.md

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+# 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`.
+
+## 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:* `gemini ask "Modify [FILE_PATH] to implement [SPECIFIC_INSTRUCTION]. Only write the code, no pleasantries." --model gemini-1.5-flash`
+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:* `gemini ask "Summarize this stack trace into a 20-word fix: [PASTE_SNIPPET_OF_STDERR_HERE]" --model gemini-1.5-flash`
+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": "gemini ask "Summarize this stack trace into a 20-word fix: [snip first 30 lines...]" --model gemini-1.5-flash",
+  "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": "gemini ask "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." --model gemini-1.5-flash",
+  "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>