Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Zaoqu-Liu/ScienceClawnpx agentmods add skills/zaoqu-liu/scienceclaw/peer-reviewWrote 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/zaoqu-liu/scienceclaw/peer-review)<a href="https://agentmods.dev/skills/zaoqu-liu/scienceclaw/peer-review"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/peer-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.
<a href="https://agentmods.dev/skills/zaoqu-liu/scienceclaw/peer-review"><img src="https://agentmods.dev/badge/skills/zaoqu-liu/scienceclaw/peer-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00071 | $0.04713 |
| Opus 5 | $0.00036 | $0.02357 |
| Sonnet 5 | $0.00014 | $0.00943 |
| Haiku 4.5 | $0.00007 | $0.00471 |
Grade A, and why
peer-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 7d 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.
This is a copy
94% identical to peer-review — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 571 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scientific Critical Evaluation and Peer Review
Overview
Peer review is a systematic process for evaluating scientific manuscripts. Assess methodology, statistics, design, reproducibility, ethics, and reporting standards. Apply this skill for manuscript and grant review across disciplines with constructive, rigorous evaluation.
When to Use This Skill
This skill should be used when:
- Conducting peer review of scientific manuscripts for journals
- Evaluating grant proposals and research applications
- Assessing methodology and experimental design rigor
- Reviewing statistical analyses and reporting standards
- Evaluating reproducibility and data availability
- Checking compliance with reporting guidelines (CONSORT, STROBE, PRISMA)
- Providing constructive feedback on scientific writing
Visual Enhancement with Scientific Schematics
When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.
If your document does not already contain schematics or diagrams:
- Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
- Simply describe your desired diagram in natural language
- Nano Banana Pro will automatically generate, review, and refine the schematic
For new documents: Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.
How to generate schematics:
python scripts/generate_schematic.py "your diagram description" -o figures/output.png
The AI will automatically:
- Create publication-quality images with proper formatting
- Review and refine through multiple iterations
- Ensure accessibility (colorblind-friendly, high contrast)
- Save outputs in the figures/ directory
When to add schematics:
- Peer review workflow diagrams
- Evaluation criteria decision trees
- Review process flowcharts
- Methodology assessment frameworks
- Quality assessment visualizations
- Reporting guidelines compliance diagrams
- Any complex concept that benefits from visualization
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.
- 7d ago First seen · 571 lines · 71 tokens per session scan A 37858ec2ae06
peer-review is a skill published in the GitHub repository Zaoqu-Liu/ScienceClaw (60 stars, last pushed 5mo ago), licensed MIT. It adds 71 tokens to every session and 4,713 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to peer-review, differing in 11 lines, and is treated as a copy.
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