skills/skills-codex/research-review/SKILL.md
Get a deep critical review of research from GPT using a secondary Codex agent. Use when user says "review my research", "help me review", "get external review", or wants critical feedback on research ideas, papers, or experimental results.
npx skillsauth add wanshuiyin/Auto-claude-code-research-in-sleep research-reviewInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Codex assurance: the fresh base reviewer is same-family. Record
review_independence: same-familyandacceptance_status: provisionalin traces and deliverables. A Claude/Gemini overlay may record cross-family accepted; an unavailable reviewer is BLOCKED, never a fabricated PASS.
Get a multi-round critical review of research work from an external LLM with maximum reasoning depth.
gpt-5.6-sol — Model used via a secondary Codex agent, reasoning effort ultra (deep-audit tier). Must be an OpenAI model (e.g., gpt-5.6-sol, gpt-5.5, o3)codex — Default: Codex ultra reviewer (deep-audit tier). Use --reviewer: oracle-pro only when explicitly requested; if Oracle is unavailable, warn and fall back to Codex at this skill's declared tier (ultra). Same-family note: this default reviewer is a second Codex/GPT agent — valid for Type-A completeness/drive review, but not a cross-family Type-B verdict; install a skills-codex-claude-review / skills-codex-gemini-review overlay for a cross-family acquittal (see shared-references/reviewer-routing.md).spawn_agent and send_input when the user has explicitly allowed delegation or subagents.Before calling the external reviewer, compile a comprehensive briefing:
Send a detailed prompt with ultra reasoning:
spawn_agent:
model: gpt-5.6-sol
reasoning_effort: ultra
message: |
[Full research context + specific questions]
Please act as a senior ML reviewer (NeurIPS/ICML level). Start from the
assumption that the work is broken somewhere — your job is to find where.
Be adversarial. Trust nothing the author tells you — verify everything
yourself. Identify:
1. Logical gaps or unjustified claims
2. Missing experiments that would strengthen the story
3. Narrative weaknesses
4. Whether the contribution is sufficient for a top venue
Please be brutally honest.
Use send_input with the returned agent id to continue the conversation:
send_input:
target: [saved reviewer id from Step 2]
message: |
Please continue the review using the revised materials below.
Revised files:
- /absolute/path/to/file1
- /absolute/path/to/file2
Focus on unresolved weaknesses and whether the revision actually fixed them.
For each round:
Key follow-up patterns:
Stop iterating when:
Save the full interaction and conclusions to a review document in the project root:
Update project memory/notes with key review conclusions.
If — composed: <canonical-report-path> is explicitly present, fold consensus,
claims matrix, TODOs, and trace links into that report instead of writing a
standalone review document. Without the directive, write the standalone review
as documented; never infer composed mode from an existing file. — standalone
always wins. See
output-composition.md.
Save a trace for every spawn_agent, send_input, or oracle-pro review call following ../shared-references/review-tracing.md. Record the reviewer route, saved agent id, prompt summary, raw response path, decisions, and action items. This preserves the Claude mainline Review Tracing semantics while using Codex-native reviewer calls.
model: gpt-5.6-sol + reasoning_effort: ultra for reviews (deep-audit tier; capability fallback per reviewer-routing.md, never below xhigh)"I'm going to present a complete ML research project for your critical review. Please act as a senior ML reviewer (NeurIPS/ICML level)..."
"Please design the minimal additional experiment package that gives the highest acceptance lift per GPU week. Our compute: [describe]. Be very specific about configurations."
"Please turn this into a concrete paper outline with section-by-section claims and figure plan."
"Please give me a results-to-claims matrix: what claim is allowed under each possible outcome of experiments X and Y?"
"Please write a mock NeurIPS review with: Summary, Strengths, Weaknesses, Questions for Authors, Score, Confidence, and What Would Move Toward Accept."
development
Search GitHub Issues and Discussions for software errors, version compatibility problems, and exact error-string matches. Use for debugging and discovery only; results are not paper-citation evidence.
development
Search GitHub Issues and Discussions for software errors, version compatibility problems, and exact error-string matches. Use for debugging and discovery only; results are not paper-citation evidence.
testing
Run the Anti-Autoresearch integrity-forensics sweep (span-anchored evidence ledger → GPT auditors propose findings → deterministic rules-only adjudicator) against a paper via a SHA-pinned thin launcher — then convert the verdict into a typed policy gate (BLOCK/WARN/NO_NEW_BLOCKER) and an append-only obligations ledger. Use when user says "integrity forensics", "forensic audit this paper", "投稿前自查诚信", "审这篇论文的诚信", or says "anti-autoresearch" when the upstream repo's own skills are not installed. Also invoked by /paper-writing (submission self-forensics, default ON), /peer-review (forensic appendix), /resubmit-pipeline.
testing
Privileged applier that LANDS meta-optimize / corpus-audit patches the user approved — the ONLY skill permitted to mutate the skill corpus from a self-modification proposal, with cross-model jury and human approval at landing. Use when the user says "meta apply", "/meta-apply", "land the staged patches", "应用优化", after a /meta-optimize run.