Auto Review Loop (MiniMax Version): Autonomous Research Improvement

🔒 Do not wrap this skill in /loop, /schedule, or CronCreate. Like /auto-review-loop, it already loops internally (review → fix → re-review), feeding each round's prior-round summary into the next review prompt (the backend is a stateless per-round API call, not a shared thread). An external timer re-enters from the top each tick, dropping that accumulated context and firing the verdict on wall-clock time instead of on artifact change, zero new signal, full token cost. Schedule the external wait that precedes it, not the verdict. See shared-references/external-cadence.md.

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 AND verdict ∈ {"ready", "almost"}, both must hold, matching the operative STOP CONDITION below. Verdict vocabulary is {"ready", "almost", "not ready"}. (Earlier wording used or and a stale verdict set; the AND form is authoritative.)
• REVIEW_DOC: review-stage/AUTO_REVIEW.md (cumulative log) (fall back to ./AUTO_REVIEW.md for legacy projects)
• REVIEWER_MODEL = MiniMax-M3, 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-M3"
  system: "You are a senior machine learning researcher..."

Method 2: curl (Fallback)

If MCP is not available, use curl directly: