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 agentscope-ai/OpenJudge --skill 01-paper-reviewgit clone --depth 1 https://github.com/agentscope-ai/OpenJudgeWrote 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/agentscope-ai/openjudge/01-paper-review)<a href="https://agentmods.dev/skills/agentscope-ai/openjudge/01-paper-review"><img src="https://agentmods.dev/badge/skills/agentscope-ai/openjudge/01-paper-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/agentscope-ai/openjudge/01-paper-review"><img src="https://agentmods.dev/badge/skills/agentscope-ai/openjudge/01-paper-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 | $0.00095 | $0.02498 |
| Opus 5 | $0.00048 | $0.01249 |
| Sonnet 5 | $0.00019 | $0.00500 |
| Haiku 4.5 | $0.00010 | $0.00250 |
Grade A, and why
01-paper-review scanned grade A with 1 finding 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 yesterday.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Test the endpoint first: `curl <base_url>/models -H "Authorization: Bearer <key>"` How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Review Skill
Multi-stage academic paper review using the OpenJudge PaperReviewPipeline:
- Safety check — jailbreak detection + format validation
- Correctness — objective errors (math, logic, data inconsistencies)
- Review — quality, novelty, significance (score 1–6)
- Criticality — severity of correctness issues
- BibTeX verification — cross-checks references against CrossRef/arXiv/DBLP
Prerequisites
# Install OpenJudge
pip install py-openjudge
# Extra dependency for paper_review
pip install litellm
pip install pypdfium2 # only if using vision mode (use_vision_for_pdf=True)
Gather from user before running
| Info | Required? | Notes |
|---|---|---|
| Paper file path | Yes | PDF or .tar.gz/.zip TeX package |
| API key | Yes | Env var preferred: OPENAI_API_KEY, ANTHROPIC_API_KEY, etc. |
| Model name | No | gpt-5.2, anthropic/claude-opus-4-6, dashscope/qwen-vl-plus. See Model selection below |
| Discipline | No | If not given, uses general CS/ML-oriented prompts |
| Venue | No | e.g. "NeurIPS 2025", "The Lancet" |
| Instructions | No | Free-form reviewer guidance, e.g. "Focus on experimental design" |
| Language | No | "en" (default) or "zh" for Simplified Chinese output |
| BibTeX file | No | Required only for reference verification |
| CrossRef email | No | Improves API rate limits for BibTeX verification |
Quick start
File type is auto-detected: .pdf → PDF review, .tar.gz/.zip → TeX review, .bib → BibTeX verification.
# Basic PDF review
python -m cookbooks.paper_review paper.pdf
# With discipline and venue
python -m cookbooks.paper_review paper.pdf \
--discipline cs --venue "NeurIPS 2025"
# Chinese output
python -m cookbooks.paper_review paper.pdf --language zh
# Custom reviewer instructions
python -m cookbooks.paper_review paper.pdf \
--instructions "Focus on experimental design and reproducibility"
# PDF + BibTeX verification
python -m cookbooks.paper_review paper.pdf \
--bib references.bib --email [email protected]
# Vision mode (for models that prefer images over text extraction)
python -m cookbooks.paper_review paper.pdf \
--vision --vision_max_pages 30 --format_vision_max_pages 10
# TeX source package
python -m cookbooks.paper_review paper_source.tar.gz \
--discipline biology --email [email protected]
# TeX source package with Chinese output and custom instructions
python -m cookbooks.paper_review paper_source.tar.gz \
--language zh --instructions "This is a short paper, be concise"
# Verify a standalone BibTeX file
python -m cookbooks.paper_review --bib_only references.bib --email [email protected]
What ships with it
1 file 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.
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.
- yesterday First seen · 204 lines · 95 tokens per session scan A d98edc9ff3ef
01-paper-review is a skill published in the GitHub repository agentscope-ai/OpenJudge (829 stars, last pushed yesterday), licensed Apache-2.0. It adds 95 tokens to every session and 2,498 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-12.
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