peer-review
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.
npx skills add K-Dense-AI/scientific-agent-skills --skill peer-review --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 Treat every unpublished submission and review as confidential. ## Mandatory safety boundary Before reading or analyzing unpublished content: 1.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 Never: - Send unpublished manuscript, supplement, review, or editorial text to an external service without specific publisher/author authorization and venue permission - Upload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, LL
- Mandatory safety boundary
- Human accountability
- Intake gate
- Review workflow
- 1. Establish scope and available evidence
- 2. Orient without deciding
- 3. Select reporting guidance
- 4. Map claims to evidence
- 5. Review methods and statistics
- 6. Review reproducibility and transparency
- 7. Review ethics and integrity
- 8. Review figures, tables, and citations
- 9. Draft actionable comments
- 10. Keep channels separate
python3 scripts/validate_review_intake.py completed-intake.json python3 scripts/select_reporting_guidelines.py local-profile.json python3 scripts/select_reporting_guidelines.py \ local-profile.json \ python3 scripts/validate_claim_evidence.py local-claim-matrix.csv python3 scripts/audit_statistics_reproducibility.py \ local-statistics-reproducibility.json python3 scripts/audit_citations.py local-manuscript.md local-references.csv python3 scripts/generate_review_scaffold.py \ completed-intake.json \
What does the peer-review skill do?
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.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill peer-review --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.