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.
npx skills add Aperivue/medsci-skills --skill revisegit clone --depth 1 https://github.com/Aperivue/medsci-skillsWrote 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.
[](https://agentmods.dev/skills/aperivue/medsci-skills/revise)<a href="https://agentmods.dev/skills/aperivue/medsci-skills/revise"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/revise/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.
<a href="https://agentmods.dev/skills/aperivue/medsci-skills/revise"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/revise.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00059 | $0.07419 |
| Opus 5 | $0.00030 | $0.03710 |
| Sonnet 5 | $0.00012 | $0.01484 |
| Haiku 4.5 | $0.00006 | $0.00742 |
Grade A, and why
revise 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 11d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 584 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Revision Skill -- Response to Peer Reviewers
Purpose
Parse reviewer decision letters, classify each comment by type, generate a formal Response to Reviewers document, track required manuscript changes, and coordinate with /analyze-stats or /make-figures when new analyses or visuals are needed.
Activation
When the user provides reviewer comments (pasted text, PDF, or file path), or requests revision of a manuscript, this skill activates. Before proceeding, confirm:
- The reviewer decision letter (pasted text or file path)
- The current manuscript file (
paper/main.texorpaper/main.qmd) - The revision round number (default: R1)
- The journal name (affects cover letter format)
Reference Files
- Response-letter voice gallery:
${CLAUDE_SKILL_DIR}/references/r2r_voice.md-- before/after examples, three response skeletons (accept / partial-accept / polite-rebuttal), and a meta-phrase-to-natural conversion table. Read it before drafting the Response to Reviewers document.
Step 1: Parse and Number All Comments
Read the full decision letter. Extract every discrete comment from every reviewer and the editor.
Numbering Convention
E-1, E-2, ... <- Editor comments
R1-1, R1-2, ... <- Reviewer 1 comments
R2-1, R2-2, ... <- Reviewer 2 comments
R3-1, R3-2, ... <- Reviewer 3 (if present)
If a reviewer groups multiple requests in one paragraph, split them into sub-items: R1-3a, R1-3b, R1-3c
Classification
| Type | Symbol | Definition |
|---|---|---|
| MAJOR | [MAJ] |
Requires new experiment, re-analysis, new figure/table, or substantial structural rewrite |
| MINOR | [MIN] |
Requires text revision, clarification, formatting change, or additional citation |
| REBUTTAL | [REB] |
Reviewer is factually incorrect, misunderstood the study, or requests something scientifically unjustified |
Output a classified comment list before generating responses:
E-1 [MIN] Request to shorten abstract
R1-1 [MAJ] Requires subgroup analysis by scanner type
R1-2 [MIN] Clarify exclusion criteria rationale
R1-3 [REB] Claims our sample size is underpowered (we disagree)
R2-1 [MAJ] Requires additional figure showing calibration curve
R2-2 [MIN] Add reference to [Author Year]
What ships with it
11 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.
- references/r2r_voice.md 17 KB
- scripts/_quote_match.py 7.7 KB runs code
- scripts/check_density_complaint.py 8.4 KB runs code
- scripts/check_response_claims.py 13 KB runs code
- scripts/density_complaint_challenge/fixture/decision_letter.md 418 B
- scripts/density_complaint_challenge/fixture/v_prev.md 1.7 KB
- scripts/density_complaint_challenge/fixture/v20_longer.md 3.1 KB
- scripts/density_complaint_challenge/fixture/v21_shorter.md 1.2 KB
- scripts/density_complaint_challenge/verify.sh 3.1 KB runs code
- skill.yml 2.4 KB
- tests/test_response_claims.sh 6.5 KB runs code
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.
- 11d ago First seen · 584 lines · 59 tokens per session scan A 4733786ab5f5
revise is a skill published in the GitHub repository Aperivue/medsci-skills (292 stars, last pushed 3d ago), licensed MIT. It adds 59 tokens to every session and 7,419 once invoked, about $0.0003 per session on Opus 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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