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 LiYu0524/Auto-Reasearch-Skills --skill paper-reviewergit clone --depth 1 https://github.com/LiYu0524/Auto-Reasearch-SkillsWrote 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/liyu0524/auto-reasearch-skills/paper-reviewer)<a href="https://agentmods.dev/skills/liyu0524/auto-reasearch-skills/paper-reviewer"><img src="https://agentmods.dev/badge/skills/liyu0524/auto-reasearch-skills/paper-reviewer/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/liyu0524/auto-reasearch-skills/paper-reviewer"><img src="https://agentmods.dev/badge/skills/liyu0524/auto-reasearch-skills/paper-reviewer.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 | $0.00068 | $0.01009 |
| Opus 5 | $0.00034 | $0.00504 |
| Sonnet 5 | $0.00014 | $0.00202 |
| Haiku 4.5 | $0.00007 | $0.00101 |
Grade A, and why
paper-reviewer 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 13d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Reviewer
Overview
Read a paper end-to-end (prefer PDF), then produce a teachable explanation and a reviewer-style critique: content summary, innovation points, evidence quality, and actionable concerns.
Quick Start (Inputs)
- Paper: local PDF path (preferred), or arXiv/DOI/citation.
- Audience: beginner / familiar-with-field / expert.
- Focus: method / experiments / critique / implementation.
- Depth: 10-min / 30-min / 90-min talk notes (default: 30-min).
- Target venue (optional): e.g., NeurIPS/ICLR/ACL, or "internal reading group".
If the user does not specify, assume: audience="熟悉基础 ML", focus="method + experiments + critique", depth="30-min", language="Chinese".
Workflow
1) Identify the paper
- If multiple PDFs exist, ask which one to review.
- Record title/authors/venue/year (as shown), and page count.
2) Extract text and render pages (prefer visual skim)
- Use the helper script to extract per-page text and (optionally) render pages to PNG for figure/table inspection:
python3 skills/paper-reviewer/scripts/dump_paper_pdf.py --pdf "<PATH>" --out-dir "tmp/paper-review/<slug>" --render
- If rendering fails (missing
fitz/PyMuPDF), rerun without--renderand continue.
3) First pass: map the paper (10-20 min)
- Identify:
- Problem setting, inputs/outputs, assumptions.
- 3-5 core contributions (claimed novelty).
- The "main loop" of the method in one paragraph.
- Which experiments are intended to support which claims.
4) Second pass: teach the method
- Explain in this order (even if the paper orders differently):
- Problem + why it matters.
- Baseline mental model (what a reasonable approach would do).
- What is new (the delta vs baselines/prior work).
- Method (step-by-step; pseudocode-level).
- Complexity and failure modes.
- For equations: explain what each term does, not just restate symbols.
- When referencing results, cite section/figure/table numbers (and page numbers if helpful).
What ships with it
3 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.
- 13d ago First seen · 100 lines · 68 tokens per session scan A 1fa52a7c021e
paper-reviewer is a skill published in the GitHub repository LiYu0524/Auto-Reasearch-Skills (11 stars, last pushed 6mo ago), licensed MIT. It adds 68 tokens to every session and 1,009 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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