Autonomous multi-round research review loop using MiniMax API. Use when you want to use MiniMax instead of Codex MCP for external review. Trigger with "auto review loop minimax" or "minimax review".

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 ](../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: Method 2: curl (Fallback) If MCP is not available, use curl directly: 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: Write this file at the end of every Phase E (after documenting the round). Overwrite each time — only the latest state matters. On completion (positive assessment or max rounds), set "status": "completed" so future invocations don't accidentally resume a finished loop. Workflow Initialization 1. Check for review stage/REVIEW STATE.json (fall back to ./REVIEW STATE.json if not found — legacy path) : If neither path exists: fresh start (normal case) If it exists AND status is "completed" : fresh start (previous loop finished normally) If it exists AND status is "in progress" AND timestamp is older than 24 hours: fresh start (stale state from a killed/abandoned run — delete the file and start over) If it exists AND status is "in progress" AND timestamp is within 24 hours: resume Read the state file to recover round , last score , pending experiments Read review stage/AUTO REVIEW.md to restore full context of prior rounds (fall back to ./AUTO REVIEW.md ) If pending experiments is non empty, check if they have completed (e.g., check screen sessions) Resume from the next round (round = saved round + 1) Log: "Recovered from context compaction. Resuming at Round N." 2. Read project narrative documents, memory files, and any prior review documents 3. Read recent experiment results (check output directories, logs) 4. Identify current weaknesses and open TODOs from prior reviews 5. Initialize round counter = 1 (unless recovered from state file) 6. Create/update review stage/AUTO REVIEW.md with header and timestamp Loop (repeat up to MAX ROUNDS) Phase A: Review Send comprehensive context to the external reviewer. Check MCP availability first , then use appropriate method: If MCP available (Primary): If MCP NOT available (Fallback): Note : Each round is a standalone API call. For round 2+, include the summary of previous reviews and changes in the prompt itself. Phase B: Parse Assessment CRITICAL: Save the FULL raw response from the external reviewer verbatim (store in a variable for Phase E). Do NOT discard or summarize — the raw text is the primary record. Then extract structured fields: Score (numeric 1 10) Verdict ("ready" / "almost" / "not ready") Action items (ranked list of fixes) STOP CONDITION : If score = 6 AND verdict ∈ {"ready", "almost"} (exact match — "not ready" does NOT qualify) → stop loop, document final state. Phase C: Implement Fixes (if not stopping) For each action item (highest priority first): 1. Code changes : Write/modify experiment scripts, model code, analysis scripts 2. Run experiments : Deploy to GPU server via SSH + screen/tmux 3. Analysis : Run evaluation, collect results, update figures/tables 4. Documentation : Update project notes and review document Prioritization rules: Skip fixes requiring excessive compute (flag for manual follow up) Skip fixes requiring external data/models not available Prefer reframing/analysis over new experiments when both address the concern Always implement metric additions (cheap, high impact) Phase D: Wait for Results If experiments were launched: Monitor remote sessions for completion Collect results from output files and logs Phase E: Document Round Append to review stage/AUTO REVIEW.md : Write review stage/REVIEW STATE.json with current round, score, verdict, and any pending experiments. Increment round counter → back to Phase A. Termination When loop ends (positive assessment or max rounds): 1. Update review stage/REVIEW STATE.json with "status": "completed" 2. Write final summary to review stage/AUTO REVIEW.md 3. Update project notes with conclusions 4. If stopped at max rounds without positive assessment: List remaining blockers Estimate effort needed for each Suggest whether to continue manually or pivot Key Rules Large file handling : If the Write tool fails due to file size, immediately retry using Bash ( cat << 'EOF' file ) to write in chunks. Do NOT ask the user for permission — just do it silently. Anti hallucination citations : When adding references, NEVER fabricate BibTeX. Use DBLP → CrossRef → [VERIFY] chain. Do NOT generate BibTeX from memory. Be honest — include negative results and failed experiments Do NOT hide weaknesses to game a positive score Implement fixes BEFORE re reviewing (don't just promise to fix) If an experiment takes 30 minutes, launch it and continue with other fixes while waiting Document EVERYTHING — the review log should be self contained Update project notes after each round, not just at the end For round 2+, always include previous review context in the prompt Prefer MCP tool over curl when available (more reliable) Prompt Template for Round 2+ MCP Method (Primary): curl Fallback: Output Protocols Follow these shared protocols for all output files: [Output Versioning Protocol](../shared references/output versioning.md) — write timestamped file first, then copy to fixed name [Output Manifest Protocol](../shared references/output manifest.md) — log every output to MANIFEST.md [Output Language Protocol](../shared references/output language.md) — respect the project's language setting

auto-review-loop-minimax