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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".
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep --skill auto-review-loop-minimax명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
SOC 직업 분류 기준
Search and analyze research papers, find related work, summarize key ideas. Use when user says "find papers", "related work", "literature review", "what does this paper say", or needs to understand academic papers.
Persistent research knowledge base that accumulates papers, ideas, experiments, claims, and their relationships across the entire research lifecycle. Inspired by Karpathy's LLM Wiki pattern. Use when user says "知识库", "research wiki", "add paper", "wiki query", "查知识库", or wants to build/query a persistent field map.
Search and analyze research papers, find related work, summarize key ideas. Use when user says "find papers", "related work", "literature review", "what does this paper say", or needs to understand academic papers.
SKILL.md 표시 중
| name | auto-review-loop-minimax |
| description | 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". |
| argument-hint | [topic-or-scope] |
| allowed-tools | Bash(*), Read, Grep, Glob, Write, Edit, Skill |
🔒 Do not wrap this skill in
/loop,/schedule, orCronCreate. 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. Seeshared-references/external-cadence.md.
Autonomously iterate: review → implement fixes → re-review, until the external reviewer gives a positive assessment or MAX_ROUNDS is reached.
orANDreview-stage/AUTO_REVIEW.md (cumulative log) (fall back to ./AUTO_REVIEW.md for legacy projects)MiniMax-M3 — Model used via MiniMax APIThis skill uses MiniMax API for external review. Two methods are supported:
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..."
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-M3",
"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
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"
}
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.
review-stage/REVIEW_STATE.json (fall back to ./REVIEW_STATE.json if not found — legacy path):
status is "completed": fresh start (previous loop finished normally)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)status is "in_progress" AND timestamp is within 24 hours: resume
round, last_score, pending_experimentsreview-stage/AUTO_REVIEW.md to restore full context of prior rounds (fall back to ./AUTO_REVIEW.md)pending_experiments is non-empty, check if they have completed (e.g., check screen sessions)review-stage/AUTO_REVIEW.md with header and timestampSend comprehensive context to the external reviewer.
Check MCP availability first, then use appropriate method:
If MCP available (Primary):
Use mcp__minimax-chat__minimax_chat tool with:
- system: "You are a senior machine learning researcher serving as a reviewer for top-tier conferences like NeurIPS, ICML, and ICLR. Provide rigorous, constructive feedback."
- prompt: [Full review prompt with context]
- model: "MiniMax-M3"
If MCP NOT available (Fallback):
curl -s "https://api.minimax.io/v1/chat/completions" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $MINIMAX_API_KEY" \
-d '{
"model": "MiniMax-M3",
"messages": [
{
"role": "system",
"content": "You are a senior machine learning researcher serving as a reviewer for top-tier conferences like NeurIPS, ICML, and ICLR. Provide rigorous, constructive feedback."
},
{
"role": "user",
"content": "[Round N/MAX_ROUNDS of autonomous review loop]\n\n[Full research context: claims, methods, results, known weaknesses]\n[Changes since last round, if any]\n[For round 2+: Summary of previous review feedback and what was addressed]\n\nPlease act as a senior ML reviewer (NeurIPS/ICML level).\n\n1. Score this work 1-10 for a top venue\n2. List remaining critical weaknesses (ranked by severity)\n3. For each weakness, specify the MINIMUM fix (experiment, analysis, or reframing)\n4. State clearly: is this READY for submission? Yes/No/Almost\n\nBe brutally honest. If the work is ready, say so clearly."
}
],
"max_tokens": 4096
}'
Note: Each round is a standalone API call. For round 2+, include the summary of previous reviews and changes in the prompt itself.
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:
STOP CONDITION: If score >= 6 AND verdict ∈ {"ready", "almost"} (exact match — "not ready" does NOT qualify) → stop loop, document final state.
For each action item (highest priority first):
Prioritization rules:
If experiments were launched:
Append to review-stage/AUTO_REVIEW.md:
## Round N (timestamp)
### Assessment (Summary)
- Score: X/10
- Verdict: [ready/almost/not ready]
- Key criticisms: [bullet list]
### Reviewer Raw Response
<details>
<summary>Click to expand full reviewer response</summary>
[Paste the COMPLETE raw response from the external reviewer here — verbatim, unedited.
This is the authoritative record. Do NOT truncate or paraphrase.]
</details>
### Actions Taken
- [what was implemented/changed]
### Results
- [experiment outcomes, if any]
### Status
- [continuing to round N+1 / stopping]
Write review-stage/REVIEW_STATE.json with current round, score, verdict, and any pending experiments.
Increment round counter → back to Phase A.
When loop ends (positive assessment or max rounds):
review-stage/REVIEW_STATE.json with "status": "completed"review-stage/AUTO_REVIEW.mdLarge 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)
MCP Method (Primary):
mcp__minimax-chat__minimax_chat:
model: "MiniMax-M3"
system: "You are a senior machine learning researcher serving as a reviewer for top-tier conferences like NeurIPS, ICML, and ICLR. Provide rigorous, constructive feedback."
prompt: |
[Round N/MAX_ROUNDS of autonomous review loop]
## Previous Review Summary (Round N-1)
- Previous Score: X/10
- Previous Verdict: [ready/almost/not ready]
- Previous Key Weaknesses: [list]
## Changes Since Last Review
1. [Action 1]: [result]
2. [Action 2]: [result]
3. [Action 3]: [result]
## Updated Results
[paste updated metrics/tables]
## Current Research Context
[brief summary of claims, methods, current state]
Please re-score and re-assess:
1. Score this work 1-10 for a top venue
2. List remaining critical weaknesses (ranked by severity)
3. For each weakness, specify the MINIMUM fix
4. State clearly: is this READY for submission? Yes/No/Almost
Be brutally honest. If the work is ready, say so clearly.
curl Fallback:
curl -s "https://api.minimax.io/v1/chat/completions" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $MINIMAX_API_KEY" \
-d '{
"model": "MiniMax-M3",
"messages": [
{
"role": "system",
"content": "You are a senior machine learning researcher serving as a reviewer for top-tier conferences like NeurIPS, ICML, and ICLR. Provide rigorous, constructive feedback."
},
{
"role": "user",
"content": "[Round N/MAX_ROUNDS of autonomous review loop]\n\n## Previous Review Summary (Round N-1)\n- Previous Score: X/10\n- Previous Verdict: [ready/almost/not ready]\n- Previous Key Weaknesses: [list]\n\n## Changes Since Last Review\n1. [Action 1]: [result]\n2. [Action 2]: [result]\n3. [Action 3]: [result]\n\n## Updated Results\n[paste updated metrics/tables]\n\n## Current Research Context\n[brief summary of claims, methods, current state]\n\nPlease re-score and re-assess:\n1. Score this work 1-10 for a top venue\n2. List remaining critical weaknesses (ranked by severity)\n3. For each weakness, specify the MINIMUM fix\n4. State clearly: is this READY for submission? Yes/No/Almost\n\nBe brutally honest. If the work is ready, say so clearly."
}
],
"max_tokens": 4096
}'
Follow these shared protocols for all output files:
- Output Versioning Protocol — write timestamped file first, then copy to fixed name
- Output Manifest Protocol — log every output to MANIFEST.md
- Output Language Protocol — respect the project's language setting