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 beita6969/ScienceClaw --skill peer-reviewgit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/peer-review)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/peer-review"><img src="https://agentmods.dev/badge/skills/beita6969/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/beita6969/scienceclaw/peer-review"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/peer-review.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 | $0.00047 | $0.00701 |
| Opus 5 | $0.00023 | $0.00351 |
| Sonnet 5 | $0.00009 | $0.00140 |
| Haiku 4.5 | $0.00005 | $0.00070 |
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 9d 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Trigger
Activate this skill when the user mentions:
- Peer review, manuscript review, referee report
- Reviewer comments, constructive criticism, revision feedback
- Journal submission evaluation, editorial assessment
- Methodological critique, statistical review
- Paper strengths and weaknesses analysis
- Response to reviewers, rebuttal letter
Step-by-Step Methodology
- Initial assessment - Read the full manuscript. Identify the research question, study design, key findings, and claimed conclusions. Assess whether the paper is within scope for the target journal.
- Novelty and significance evaluation - Determine the contribution relative to existing literature. Is the advance incremental or substantial? Are similar results already published? Check for proper citation of prior work.
- Methods evaluation - Assess study design appropriateness for the research question. Check for adequate controls, sample sizes, blinding, and randomization. Verify that methods are described with sufficient detail for replication.
- Statistical review - Verify appropriate statistical tests for data type and design. Check for multiple comparison corrections. Assess effect sizes (not just p-values). Look for signs of p-hacking or selective reporting. Verify that assumptions of statistical tests are met.
- Results assessment - Check that results directly address the stated aims. Verify figures and tables are accurate, well-labeled, and consistent with text. Look for cherry-picking or over-interpretation of data.
- Discussion evaluation - Assess whether conclusions are supported by the data. Check for appropriate caveats and limitations. Evaluate whether alternative interpretations are considered.
- Generate structured review - Organize feedback into: (a) Summary of the paper, (b) Major concerns (issues that must be addressed), (c) Minor concerns (suggestions for improvement), (d) Optional comments (stylistic or presentational). Be specific, constructive, and provide actionable suggestions.
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.
- 9d ago First seen · 52 lines · 47 tokens per session scan A 7201ad368359
peer-review is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 47 tokens to every session and 701 once invoked, about $0.0002 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 2026-09-03.
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