review-model-paper

review-model-paper is a skill for Codex from chengziyue1222/math-model-agent. It costs 51 tokens per session (1,399 once invoked), scanned A, original, MIT.

A read-only review workflow for mathematical-modeling papers, covering reasoning, evidence, citations, figures, tables, and final document layout.

In plain words
What is it for?
Use it for pre-submission audits, revision plans, claim checks, and inspection of LaTeX, Typst, Markdown, PDFs, figures, tables, and appendices.
Why use it?
It finds unsupported claims, inconsistent results, weak explanations, citation problems, and rendering issues before submission.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: mentions Codex.

Good fit Use it for pre-submission audits, revision plans, claim checks, and inspection of LaTeX, Typst, Markdown, PDFs, figures, tables, and appendices.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/chengziyue1222/math-model-agent/review-model-paper
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 chengziyue1222/math-model-agent --skill review-model-paper
Clone the repo
git clone --depth 1 https://github.com/chengziyue1222/math-model-agent

Made for: Codex.

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 review-model-paper

README.md
[![agentmods](https://agentmods.dev/badge/skills/chengziyue1222/math-model-agent/review-model-paper/github.svg)](https://agentmods.dev/skills/chengziyue1222/math-model-agent/review-model-paper)
Your own site
<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.

agentmods 80×15 button for review-model-paper

Your own site · 80×15
<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>
Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,399 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.
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 $0.00051 $0.01399
Opus 5.5 $0.00020 $0.00560
Sonnet 5.5 $0.00010 $0.00280
Haiku 4.5 $0.00005 $0.00140

Measured 3d ago against content hash 1a51ec416cdb, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 3d ago.

The scan reads SKILL.md. This mod also ships 7 executable files (scripts/execute_skill.py, scripts/finalize_review_package.py, scripts/resolve_paper_venue.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/review-model-paper/SKILL.md · 56 lines

How it starts

The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Review Model Paper

Report mathematical evidence, figure expression and page-layout defects separately in the existing review record. After a repair, inspect the regenerated dependent outputs and their current hashes; keep unresolved uncertainty and unviewed pages pending.

For practical matrix/calendar/composition figures or a failed stage gate, read references/practical-figure-review.md. Check source numbers, units, composition denominators, size/height/angle encoding, missing states and final insertion. Return the earliest affected stage; release requires passing review, preflight and current evidence, without fixed counts of equations, figures or citations.

Review in read-only mode unless revision is explicitly requested. First judge the reader-visible paper, then run backend consistency checks.

Workflow

  1. Collect the problem, manuscript, selected paper delivery (PDF by default; PDF or DOCX for competition, both for audit), result files, figures/tables, validation material, citations, support archive/manifest and contest template.
  2. Read references/paper-quality-review.md and references/review-rubric.md. For CUMCM 2026, also read references/cumcm-2026-preflight.md and label each finding as official compliance or quality advice.
  3. Check abstract, problem analysis, unified-core decision, formula explanation, per-question model/solution/result coverage, result reasoning and limitation disclosure.
  4. Check that figures and tables have different evidentiary roles, are readable on A4 pages, and receive useful surrounding prose.
  5. Check citations for relevance and verifiability. Require the appendix to list every support-package file and contain every complete self-authored runnable source file; third-party libraries belong in a versioned dependency list with installation instructions, not as copied source or binaries.
  6. Render and inspect layout against the selected venue/template. For CUMCM, the abstract is electronic page 1, the restatement follows with continuous numbering, and no contents page exists. References and appendices must read cleanly; the body obeys the resolved venue page limit. Treat typography and heading alignment as local quality defaults unless explicitly mandated.
  7. Keep ordinary review read-only. For a final anonymity preflight, use a user-supplied private identity dictionary outside the support tree or explicit --identity-term values when available. Do not create or populate an identity file during review. If no private dictionary is supplied, still run generic identity-label, path, filename and metadata checks and report the personalized term scan as not run rather than failing the whole review.
  8. Run structural, value-consistency, resolved venue package-size/anonymity, path and PDF/DOCX metadata checks. Verify the support manifest exactly matches ZIP/RAR entries and rerun it from a clean extracted temporary directory. For CUMCM 2026, require the AI declaration before references and, when AI was used, the exact support file AI工具使用详情.pdf. Reconcile disclosure against records rather than guessing from writing style. RAR review requires rarfile plus a detected extraction backend and records that backend in the report. Any missing local import/data/config/font/plot dependency, confirmed identity leak or absolute user path blocks readiness.
  9. 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. Use references/evidence-review.md for claim/source pointers, statistical ambiguity, figure semantics and a revision closure check. Separate confirmed defects, unresolved evidence gaps and optional aesthetic preferences.

Read the full file on GitHub · 56 lines

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. 3d ago Changed · +18 lines 1a51ec416cdb
  2. 1mo ago Changed · +3 lines 1bb2e5be118a
  3. 2mo ago First seen · 35 lines · 51 tokens per session scan A 2e76de16ad47

Subscribe to this mod's changes

review-model-paper is a skill published in the GitHub repository chengziyue1222/math-model-agent (24 stars, last pushed 4d ago), licensed MIT. It adds 51 tokens to every session and 1,399 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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