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wanshuiyin/Auto-claude-code-research-in-sleep/skills/skills-codex/research-review/SKILL.md

research-review

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.

Source repository stars
14,225
Declared platforms
1
Static risk flags
0
Last source update
2026-08-04
Source checked
2026-08-04

Decision brief

What it does—and where it fits

Codex assurance: the fresh base reviewer is same-family. Record reviewindependence: same-family and acceptancestatus: provisional in traces and deliverables. A Claude/Gemini overlay may record cross-family accepted; an unavailable reviewer is BLOCKED, never a fabricated PASS.

Best for

  • Use when user says "review my research", "help me review", "get external review", or wants critical feedback on research ideas, papers, or experimental results.

Not for

  • Tasks that require unconfirmed production actions or broad system permissions.
  • Environments where the pinned source and install steps cannot be inspected.

Compatibility matrix

Platform support, with evidence labels

PlatformStatusEvidenceWhat to check
CodexDeclaredSource recordInstall path and trigger
Claude CodeNot declaredNo explicit evidencePortability before use
CursorNot declaredNo explicit evidencePortability before use
Gemini CLINot declaredNo explicit evidencePortability before use
Open the compatibility checker

Installation

Inspect first. Install second.

The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

Source-detected install commandSource
npx skills add https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep --skill "skills/skills-codex/research-review"
Safe inspection promptEditorial

Inspect the Agent Skill "research-review" from https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/blob/a5fcc6970f08d45f6a2100abef4d5d234a1cef25/skills/skills-codex/research-review/SKILL.md at commit a5fcc6970f08d45f6a2100abef4d5d234a1cef25. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

Workflow

What the source asks the agent to do

  1. 01

    Workflow

    Before calling the external reviewer, compile a comprehensive briefing: 1. Read project narrative documents (e.g., STORY.md, README.md, paper drafts) 2. Read any memory/notes files for key findings and experiment history 3. Identify: core claims, methodology, key results, known…

    Read project narrative documents (e.g., STORY.md, README.md, paper drafts)Read any memory/notes files for key findings and experiment historyIdentify: core claims, methodology, key results, known weaknesses
  2. 02

    Step 1: Gather Research Context

    Before calling the external reviewer, compile a comprehensive briefing: 1. Read project narrative documents (e.g., STORY.md, README.md, paper drafts) 2. Read any memory/notes files for key findings and experiment history 3. Identify: core claims, methodology, key results, known…

    Read project narrative documents (e.g., STORY.md, README.md, paper drafts)Read any memory/notes files for key findings and experiment historyIdentify: core claims, methodology, key results, known weaknesses
  3. 03

    Step 2: Initial Review (Round 1)

    Send a detailed prompt with ultra reasoning:

    Send a detailed prompt with ultra reasoning:
  4. 04

    Step 3: Iterative Dialogue (Rounds 2-N)

    Use sendinput with the returned agent id to continue the conversation:

    Respond to criticisms with evidence/counterargumentsAsk targeted follow-ups on the most actionable pointsRequest specific deliverables: experiment designs, paper outlines, claims matrices
  5. 05

    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

    Both sides agree on the core claims and their evidence requirementsA concrete experiment plan is establishedThe narrative structure is settled

Permission review

Static risk signals and limitations

No configured static risk pattern was detected

This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.

Evidence record

Why each signal appears

EvidenceSourceComputedTestedEditorial
SignalValueEvidence typeMeaning
Quality score86/100ComputedDocumentation, specificity, maintenance, and trust rules
Repository stars14,225SourceRepository attention, not individual Skill quality
Compatibility1 platformsSourceDeclared in the catalog source record
Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

Pinned source

Provenance and original SKILL.md

Repository
wanshuiyin/Auto-claude-code-research-in-sleep
Skill path
skills/skills-codex/research-review/SKILL.md
Commit
a5fcc6970f08d45f6a2100abef4d5d234a1cef25
License
MIT
Collected
2026-08-04
Default branch
main
View the original SKILL.md

Research Review via a secondary Codex agent (ultra reasoning)

Codex assurance: the fresh base reviewer is same-family. Record review_independence: same-family and acceptance_status: provisional in 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 effort ultra (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-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).

Context: $ARGUMENTS

Prerequisites

  • Use spawn_agent and send_input when 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:

  1. Read project narrative documents (e.g., STORY.md, README.md, paper drafts)
  2. Read any memory/notes files for key findings and experiment history
  3. 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:

  1. Respond to criticisms with evidence/counterarguments
  2. Ask targeted follow-ups on the most actionable points
  3. 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: ultra for reviews (deep-audit tier; capability fallback per reviewer-routing.md, never below xhigh)
  • 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."

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