auto-review-loop-minimax

Auto Review Loop (MiniMax Version): Autonomous Research Improvement

Autonomously iterate: review → implement fixes → re-review, until the external reviewer gives a positive assessment or MAX_ROUNDS is reached.

Context: $ARGUMENTS

Constants

• MAX_ROUNDS = 4
• POSITIVE_THRESHOLD: score >= 6/10, or verdict contains "accept", "sufficient", "ready for submission"
• REVIEW_DOC: review-stage/AUTO_REVIEW.md (cumulative log) (fall back to ./AUTO_REVIEW.md for legacy projects)
• REVIEWER_MODEL = MiniMax-M2.7 — Model used via MiniMax API

API Configuration

This skill uses MiniMax API for external review. Two methods are supported:

Method 1: MCP Tool (Primary)

If mcp__minimax-chat__minimax_chat is available, use it:

mcp__minimax-chat__minimax_chat:
  prompt: |
    [Review prompt content]
  model: "MiniMax-M2.7"
  system: "You are a senior machine learning researcher..."

Method 2: curl (Fallback)

If MCP is not available, use curl directly:

curl -s "https://api.minimax.io/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $MINIMAX_API_KEY" \
  -d '{
    "model": "MiniMax-M2.7",
    "messages": [
      {"role": "system", "content": "You are a senior ML researcher..."},
      {"role": "user", "content": "[Review prompt]"}
    ],
    "max_tokens": 4096
  }'

API Key: Read from ~/.claude/settings.json under env.MINIMAX_API_KEY, or from environment variable.

Why MiniMax instead of Codex MCP? Codex CLI uses OpenAI's Responses API (/v1/responses) which is not supported by third-party providers. See: https://github.com/openai/codex/discussions/7782

State Persistence (Compact Recovery)

Long-running loops may hit the context window limit, triggering automatic compaction. To survive this, persist state to review-stage/REVIEW_STATE.json after each round:

{
  "round": 2,
  "status": "in_progress",
  "last_score": 5.0,
  "last_verdict": "not ready",
  "pending_experiments": ["screen_name_1"],
  "timestamp": "2026-03-13T21:00:00"
}