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peer-review skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments. Use for authorized review of scientific manuscripts, protocols, preprints, or research proposals; reporting-guideline selection; claim–evidence checks; methods, statistics, reproducibility, ethics, figure/table, and citation critique; or revision-response planning.

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Install the peer-review skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/peer-review ~/.claude/skills/peer-review
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Peer Review

Support an accountable human reviewer with a rigorous, fair, actionable assessment. Treat every unpublished submission and review as confidential.

Mandatory safety boundary

Before reading or analyzing unpublished content:

  1. Confirm the user is authorized by the publisher, editor, author, or other material owner.
  2. Check the target venue’s review, confidentiality, co-review, retention, and AI/tool policies.
  3. Record conflicts, competence limits, requested scope, and specialist-review needs.
  4. Default to local-only processing.

If authorization is unclear, do not inspect or quote the manuscript. Ask for confirmation or use only the bundled local CLIs, whose reports do not echo manuscript text.

Never:

  • Send unpublished manuscript, supplement, review, or editorial text to an external service without specific publisher/author authorization and venue permission
  • Upload confidential content to a public model, search engine, citation service, grammar tool, plagiarism checker, or image service
  • Reuse content for training, benchmarking, product improvement, or unrelated research
  • Read broad environment state, .env files, API keys, or credentials
  • Call a network, LLM, or image API from bundled tools
  • Invoke another skill or a PDF/image pipeline automatically
  • Impersonate an assigned reviewer, editor, journal, funder, or author
  • Fabricate manuscript details, review findings, citations, analyses, experiments, reproduction, or an editorial outcome
  • Announce a decision that belongs to an editor or panel

Delete local copies and derivatives when policy requires; otherwise retain only what the controlling policy authorizes. Record deletion or retention without copying confidential content into the record.

Read references/ethicalreviewpractice.md before handling confidential material.

Human accountability

Label generated text as a working draft. The accountable human must:

  • Read the complete authorized submission and relevant supplements
  • Verify every factual statement, calculation, citation, and manuscript location
  • Resolve conflicts and disclose assistance as required
  • Rewrite comments in their own expert judgment
  • Submit through the authorized channel

Automated coverage, consistency, or lint results are not peer review and do not establish manuscript merit.

Intake gate

Copy and complete assets/reviewintaketemplate.json, then run:

python3 scripts/validate_review_intake.py completed-intake.json

Proceed only when status is READYFORLOCAL_REVIEW.

The validator blocks:

  • Undocumented authorization
  • Missing human accountability
  • Unassessed or unresolved conflicts
  • Unknown review model or unchecked venue policy
  • Unauthorized AI assistance
  • External service use
  • Data reuse
  • Missing deletion/retention planning

It validates declarations, not their truth.

Review workflow

1. Establish scope and available evidence

Record:

  • Submission type and stage
  • Review question and requested focus
  • Target venue and review model
  • Materials actually available: manuscript, supplements, protocol, registration, analysis plan, data/code statement, prior decision, or response letter
  • Competence areas and limits
  • Missing material that prevents assessment

Do not infer absent content. Use “not reported” or “not available for review.”

2. Orient without deciding

Create a short neutral map:

  • Research question
  • Population or system
  • Design and unit
  • Intervention, exposure, test, or model
  • Comparator/reference
  • Outcomes and timing
  • Principal claims

Do not write an acceptance/rejection recommendation. Identify what evidence would be needed to evaluate each claim.

3. Select reporting guidance

Copy assets/studyprofiletemplate.json and run:

python3 scripts/select_reporting_guidelines.py local-profile.json

For checklist coverage:

python3 scripts/select_reporting_guidelines.py \
  local-profile.json \
  --coverage local-coverage.csv

Use the current base guideline, explanation/elaboration, applicable extensions, and target venue policy. See references/reporting_standards.md.

Critical distinction: reporting completeness is not design quality, risk of bias, validity, or merit. Never convert missing items into an automatic score or publication judgment.

4. Map claims to evidence

Prioritize central, causal, mechanistic, safety, diagnostic, prediction, and generalization claims.

For each claim, record:

  • Location and claim ID
  • Supporting result, figure, table, analysis, or citation IDs
  • Direction, magnitude, population, outcome, timepoint, and uncertainty alignment
  • Limitation or alternative explanation
  • Bounded requested action

Run:

python3 scripts/validate_claim_evidence.py local-claim-matrix.csv

Start from assets/claimevidencematrix_template.csv. The report emits IDs and counts, not claim text.

5. Review methods and statistics

Assess in this order:

  1. Question and target quantity
  2. Design and unit of inference
  3. Sampling, allocation, controls, masking, and timing
  4. Sample-size or precision rationale
  5. Inclusion, exclusion, attrition, and missingness
  6. Analysis–design alignment and assumptions
  7. Multiplicity and prespecification
  8. Effect estimates, uncertainty, denominators, and harms
  9. Interpretation, causality, and generalizability

Use references/commonissues.md and references/statisticalreproducibility.md.

For a structured local audit:

python3 scripts/audit_statistics_reproducibility.py \
  local-statistics-reproducibility.json

Start from assets/statisticalreproducibilitytemplate.json. Request specialist review when a central method exceeds competence; do not hide uncertainty behind a generic critique.

6. Review reproducibility and transparency

Check, as applicable:

  • Protocol, registration, amendments, and analysis-plan consistency
  • Data provenance, exclusions, transformations, and accession IDs
  • Software, package, model, and parameter versions
  • Code, environment, seeds, run instructions, and tests
  • Data, code, materials, and model availability or justified restrictions
  • Domain metadata standards

Do not claim reproduction unless authorized inputs were actually run with documented commands, environment, and outputs.

7. Review ethics and integrity

Check applicable approvals, consent, welfare, privacy, community governance, funding, sponsor role, conflicts, authorship/contribution, registration, biosafety, and dual-use concerns.

Describe observable evidence and uncertainty. Do not accuse authors or investigate them. Route credible concerns through the confidential editor channel under venue policy.

8. Review figures, tables, and citations

For figures and tables, assess:

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