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 JeanDiable/academic-research-plugin --skill paper-reviewinggit clone --depth 1 https://github.com/JeanDiable/academic-research-pluginWrote 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/jeandiable/academic-research-plugin/paper-reviewing)<a href="https://agentmods.dev/skills/jeandiable/academic-research-plugin/paper-reviewing"><img src="https://agentmods.dev/badge/skills/jeandiable/academic-research-plugin/paper-reviewing/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/jeandiable/academic-research-plugin/paper-reviewing"><img src="https://agentmods.dev/badge/skills/jeandiable/academic-research-plugin/paper-reviewing.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 | $0.00116 | $0.03099 |
| Opus 5 | $0.00058 | $0.01550 |
| Sonnet 5 | $0.00023 | $0.00620 |
| Haiku 4.5 | $0.00012 | $0.00310 |
Grade A, and why
paper-reviewing 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 — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Conduct a comprehensive conference-style peer review of an academic paper. This skill analyzes papers across six key dimensions:
- Novelty: How original and innovative is the work?
- Technical Soundness: Are the methods correct and well-justified?
- Clarity: Is the paper well-written and organized?
- Significance: How impactful is this work?
- Reproducibility: Can others reproduce the results?
- Experimental Design: Are experiments properly designed and comprehensive?
The review follows the format and checklist of a specified conference (NeurIPS, ICML, CVPR, ACL, AAAI, ICCV, ICLR) or a generic academic format. Severity can be adjusted (lenient, standard, strict) to calibrate tone and scoring standards.
Arguments
<pdf-path> (required)
Path to the paper PDF file to review. Can be absolute or relative path.
--conference (optional)
Target conference to match review format. Options:
NeurIPS- Neural Information Processing SystemsICML- International Conference on Machine LearningCVPR- IEEE/CVF Conference on Computer Vision and Pattern RecognitionACL- Association for Computational LinguisticsAAAI- Association for the Advancement of Artificial IntelligenceICCV- IEEE/CVF International Conference on Computer VisionICLR- International Conference on Learning Representations- If omitted, uses generic academic format
--severity (optional, default: standard)
Tone and scoring calibration:
lenient- Focus on strengths, constructive framing, generous scoringstandard- Balanced assessment, typical conference reviewer tonestrict- Rigorous evaluation, all issues flagged, conservative scoring
Setup
Install dependencies:
python -m pip install -r "BASE_DIR/scripts/requirements.txt"
Required packages:
PyPDF2orpdfplumber- PDF parsing and text extractionrequests- HTTP requests for arXiv and paper searchespython-dateutil- Date handling
What ships with it
4 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.
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 · 340 lines · 116 tokens per session scan A 31280a4bd1c5
paper-reviewing is a skill published in the GitHub repository JeanDiable/academic-research-plugin (22 stars, last pushed 5mo ago), licensed MIT. It adds 116 tokens to every session and 3,099 once invoked, about $0.0006 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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