Research review
Skill wanshuiyin/Auto-claude-code-research-in-sleep/skills/skills-codex/research-review
ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.
npx -y skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill research-reviewAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
What its author says it does
Copied from the file, not written here
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.
SKILL.md
6.5 KB, as published. Nobody here has run it
Research Review via a secondary Codex agent (ultra reasoning)
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.
Constants
- REVIEWER_MODEL =
gpt-5.6-sol— Model used via a secondary Codex agent, reasoning effortultra(deep-audit tier). Must be an OpenAI model (e.g.,gpt-5.6-sol,gpt-5.5,o3) - REVIEWER_BACKEND =
codex— Default: Codex ultra reviewer (deep-audit tier). Use--reviewer: oracle-proonly 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 askills-codex-claude-review/skills-codex-gemini-reviewoverlay for a cross-family acquittal (seeshared-references/reviewer-routing.md).
Context: $ARGUMENTS
Prerequisites
- Use
spawn_agentandsend_inputwhen the user has explicitly allowed delegation or subagents. - If delegation is not allowed, run the same review loop locally and preserve the same deliverable structure.
Workflow
Step 1: Gather Research Context
Before calling the external reviewer, compile a comprehensive briefing:
- Read project narrative documents (e.g., STORY.md, README.md, paper drafts)
- Read any memory/notes files for key findings and experiment history
- Identify: core claims, methodology, key results, known weaknesses
Step 2: Initial Review (Round 1)
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.
Step 3: Iterative Dialogue (Rounds 2-N)
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:
- Respond to criticisms with evidence/counterarguments
- Ask targeted follow-ups on the most actionable points
- Request specific deliverables: experiment designs, paper outlines, claims matrices
Key follow-up patterns:
- "If we reframe X as Y, does that change your assessment?"
- "What's the minimum experiment to satisfy concern Z?"
- "Please design the minimal additional experiment package (highest acceptance lift per GPU week)"
- "Please write a mock NeurIPS/ICML review with scores"
- "Give me a results-to-claims matrix for possible experimental outcomes"
Step 4: Convergence
Stop iterating when:
- Both sides agree on the core claims and their evidence requirements
- A concrete experiment plan is established
- The narrative structure is settled
Step 5: Document Everything
Save the full interaction and conclusions to a review document in the project root:
- Round-by-round summary of criticisms and responses
- Final consensus on claims, narrative, and experiments
- Claims matrix (what claims are allowed under each possible outcome)
- Prioritized TODO list with estimated compute costs
- Paper outline if discussed
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.
Step 6: Review Tracing
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.
Key Rules
- ALWAYS use
model: gpt-5.6-sol+reasoning_effort: ultrafor reviews (deep-audit tier; capability fallback perreviewer-routing.md, never belowxhigh) - Send comprehensive context in Round 1 — the external model cannot read your files
- Be honest about weaknesses — hiding them leads to worse feedback
- Push back on criticisms you disagree with, but accept valid ones
- Focus on ACTIONABLE feedback — "what experiment would fix this?"
- Document the agent id for potential future resumption
- The review document should be self-contained (readable without the conversation)
Prompt Templates
For initial review:
"I'm going to present a complete ML research project for your critical review. Please act as a senior ML reviewer (NeurIPS/ICML level)..."
For experiment design:
"Please design the minimal additional experiment package that gives the highest acceptance lift per GPU week. Our compute: [describe]. Be very specific about configurations."
For paper structure:
"Please turn this into a concrete paper outline with section-by-section claims and figure plan."
For claims matrix:
"Please give me a results-to-claims matrix: what claim is allowed under each possible outcome of experiments X and Y?"
For mock review:
"Please write a mock NeurIPS review with: Summary, Strengths, Weaknesses, Questions for Authors, Score, Confidence, and What Would Move Toward Accept."