self-review

self-review is a skill for Claude Code from Aperivue/medsci-skills. It costs 50 tokens per session (12,165 once invoked), scanned A, original, MIT.

A manuscript pre-submission check that reads a medical research paper from a reviewer's perspective. It looks for likely major and minor criticisms across ten areas.

In plain words
What is it for?
Use it to create an actionable list of expected reviewer comments and suggested fixes. Optional modes can apply suitable fixes, return structured JSON, or gather feedback from several specialist reviewers.
Why use it?
It helps authors find weaknesses before sending a paper to a journal, when fixing problems is easier. It separates issues that can be fixed from problems requiring new data or a different study design.

Skill for Claude Code

Written for Claude Code: ${CLAUDE_SKILL_DIR} variable. Also seen: mentions Codex.

Part of the medsci-review plugin — 3 skills shipped together

Good fit Use it to create an actionable list of expected reviewer comments and suggested fixes. Optional modes can apply suitable fixes, return structured JSON, or gather feedback from several specialist reviewers.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aperivue/medsci-skills/self-review
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Aperivue/medsci-skills --skill self-review
Clone the repo
git clone --depth 1 https://github.com/Aperivue/medsci-skills

Made for: Claude Code.

Or install medsci-review, the plugin that ships this one along with the rest of its 3 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for self-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/aperivue/medsci-skills/self-review/github.svg)](https://agentmods.dev/skills/aperivue/medsci-skills/self-review)
Your own site
<a href="https://agentmods.dev/skills/aperivue/medsci-skills/self-review"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/self-review/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for self-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/aperivue/medsci-skills/self-review"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/self-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 12,165 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00050 $0.12165
Opus 5.5 $0.00020 $0.04866
Sonnet 5.5 $0.00010 $0.02433
Haiku 4.5 $0.00005 $0.01217

Measured 20d ago against content hash dca8eb084221, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

self-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 20d ago.

The scan reads SKILL.md. This mod also ships 7 executable files (scripts/_frontmatter.py, scripts/_prose.py, scripts/_qc_findings.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/self-review/SKILL.md · 732 lines

How it starts

The opening of the file, as written. The whole thing — 732 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Self-Review Skill

Check the user's own manuscript before submission and produce an actionable list of anticipated reviewer comments, each with a specific fix — not a written review.

Optional Flags

  • --fix: after the report, apply fixes for every issue with fixable_by_ai: true, editing the manuscript in place, then report a diff summary. Never fix fixable_by_ai: false issues (missing data, design flaws). Maximum 2 fix-and-re-review iterations; if the score is still below threshold after the second, stop and report what remains as structural (inside /write-paper this routes to Phase 7.4a Audit Recovery).
  • --json: also emit the structured JSON block (Phase 3c). Default when called from /write-paper Phase 7.
  • --panel: run the multi-agent panel review (Phase 2.6) — domain-expert reviewers in parallel plus an editor synthesis — instead of the single-pass review. Opt-in and off by default, because it spawns N reviewer agents + 1 editor and costs several times more tokens; reserve it for a high-stakes final pass on a top-tier target. Do not combine with --fix: a panel diagnoses and prioritizes; run --fix as a separate pass once the author has triaged the panel's findings.

Severity Framing

  • Fatal: a design flaw existing data cannot fix (data leakage that invalidates all results, no reference standard, label–feature circularity). Submission would likely be rejected. Reserve Fatal for true design-level problems.
  • Fixable: significant but addressable with existing data (missing calibration, unclear exclusion criteria, absent CIs, incomplete reporting). Most issues are Fixable.

Two Objectives: the Floor and the Ceiling

The floor (categories A–K and the gates of Phases 2.5–2.5f) minimizes rejection-for-cause — fabricated citations, numbers that do not reconcile, overclaims, missing checklist items, leakage — and much of it works by adding a hedge, caveat, disclosure or audit trail. Iterated, that over-hardens a manuscript: every finding is correct, yet the whole reads as a defensive audit (over-hedged, audit trail in the body, Abstract buried under caveats, the strongest robustness result hidden in Limitations). The ceiling pass (category L / Phase 2.5g) runs after the floor gates, reads the accurate manuscript as a whole, and recommends only SUBTRACTION — REMOVE, MOVE, or TIGHTEN. It is advisory, never blocks, and cannot relax a floor gate. Report its findings as their own block (Phase 3), not folded into the "add this" comments. Phase 2.5i then reads the floor + ceiling state to declare the loop done, including a zero-edit PASS.

Read the full file on GitHub · 732 lines

Files

What ships with it

60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 20d ago Changed · +1 lines dca8eb084221
  2. 21d ago Changed · +14 lines 52d153796a72
  3. 22d ago Changed · +33 lines 9a2c9026547c
  4. 25d ago Changed · -203 lines · -4 tokens per session 5e21d1aa3a9f
  5. 26d ago Changed 4c6d0e2889d3
  6. 1mo ago Changed · -2 lines 69f70f778ebf
  7. 1mo ago First seen · 889 lines · 54 tokens per session scan A 00453644f660

Subscribe to this mod's changes

self-review is a skill published in the GitHub repository Aperivue/medsci-skills (349 stars, last pushed yesterday), licensed MIT. It adds 50 tokens to every session and 12,165 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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