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 chengziyue1222/math-model-agent --skill review-model-papergit clone --depth 1 https://github.com/chengziyue1222/math-model-agentWrote 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/chengziyue1222/math-model-agent/review-model-paper)<a href="https://agentmods.dev/skills/chengziyue1222/math-model-agent/review-model-paper"><img src="https://agentmods.dev/badge/skills/chengziyue1222/math-model-agent/review-model-paper/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/chengziyue1222/math-model-agent/review-model-paper"><img src="https://agentmods.dev/badge/skills/chengziyue1222/math-model-agent/review-model-paper.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 | $0.00051 | $0.00492 |
| Opus 5 | $0.00026 | $0.00246 |
| Sonnet 5 | $0.00010 | $0.00098 |
| Haiku 4.5 | $0.00005 | $0.00049 |
Grade A, and why
review-model-paper 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 — 35 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Model Paper
Review in read-only mode unless revision is explicitly requested. First judge the reader-visible paper, then run backend consistency checks.
Workflow
- Collect the problem, manuscript, rendered PDF, result files, figures/tables, validation material, citations and contest template.
- Read
references/paper-quality-review.mdandreferences/review-rubric.md. - Check abstract, problem analysis, unified-core decision, formula explanation, per-question model/solution/result coverage, result reasoning and limitation disclosure.
- Check that figures and tables have different evidentiary roles, are readable on A4 pages, and receive useful surrounding prose.
- Check citations for relevance and verifiability; check appendices for structured, readable code rather than a miniature dump of the full repository.
- Render and inspect layout: title and first-level headings centered; no top-left running header; natural body flow; no orphan headings; references and appendices begin on clean pages.
- Run structural, value-consistency and PDF preflight checks. Keep backend findings separate from the paper's own prose.
- Report findings by severity with direct evidence, impact and the smallest repair. A PASS means the visible paper and its evidence both meet the declared standard, not that it will win a contest.
Guardrails
- Do not claim a citation or numerical result is verified without checking its source.
- Do not use a fixed count of equations, figures or references as a proxy for quality.
- Do not edit, publish or submit the paper without authorization.
Resources
references/paper-quality-review.md— reader-visible quality checklist.references/review-rubric.md— modeling quality rubric.
Executable Contract
For repository-managed competition and audit projects, inspect the shared contract registry with python -m scripts.skill_contracts --skill review-model-paper and run this Skill through the local scripts/execute_skill.py with every contracted input and output role. In rapid, provide a labelled peer-style critique of the available artifact; do not issue a submission-ready or formally verified verdict.
What ships with it
9 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.
- agents/openai.yaml 261 B
- references/paper-quality-review.md 1.0 KB
- references/review-rubric.md 1.4 KB
- references/rule-pack-integration.md 590 B
- scripts/execute_skill.py 232 B runs code
- scripts/finalize_review_package.py 6.6 KB runs code
- scripts/run_competition_review.py 3.6 KB runs code
- scripts/run_paper_check.py 2.4 KB runs code
- scripts/second_pass_content_review.py 5.1 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 · 35 lines · 51 tokens per session scan A 2e76de16ad47
review-model-paper is a skill published in the GitHub repository chengziyue1222/math-model-agent (15 stars, last pushed 1mo ago), licensed MIT. It adds 51 tokens to every session and 492 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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