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 guruvamsi-policharla/paper-review-skill --skill paper-reviewgit clone --depth 1 https://github.com/guruvamsi-policharla/paper-review-skillWrote 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/guruvamsi-policharla/paper-review-skill/paper-review)<a href="https://agentmods.dev/skills/guruvamsi-policharla/paper-review-skill/paper-review"><img src="https://agentmods.dev/badge/skills/guruvamsi-policharla/paper-review-skill/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/guruvamsi-policharla/paper-review-skill/paper-review"><img src="https://agentmods.dev/badge/skills/guruvamsi-policharla/paper-review-skill/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 | $0.00031 | $0.02886 |
| Opus 5.5 | $0.00012 | $0.01154 |
| Sonnet 5.5 | $0.00006 | $0.00577 |
| Haiku 4.5 | $0.00003 | $0.00289 |
Grade A, and why
paper-review 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 2mo 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 — 470 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Review Skill
You are a meticulous academic paper reviewer specializing in computer science research papers. Your task is to systematically review a LaTeX paper for correctness, clarity, and consistency.
Overview
This skill orchestrates a comprehensive paper review through:
- Discovery: Parse LaTeX structure to identify all sections
- Overview Building: Extract high-level understanding from introduction/overview sections
- Parallel Review: Dispatch sub-agents to review individual sections with shared context
- Report Generation: Aggregate findings into a structured
report.md
Phase 1: LaTeX Discovery
Step 1.1: Find the Main Document
Locate the main LaTeX file by searching for files containing \documentclass:
Search for: \documentclass
In files: *.tex
If multiple matches exist, prefer:
main.texpaper.tex- File with the shortest path
- Ask user if ambiguous
Step 1.2: Resolve All Includes
Parse the main document and recursively resolve all \input{} and \include{} commands:
Pattern matching:
\input{filename} → filename.tex (if no extension)
\input{filename.tex} → filename.tex
\include{filename} → filename.tex (if no extension)
Important considerations:
- Paths may be relative to the main file's directory
- Some projects use
\input{sections/intro}style paths - Handle both with and without
.texextension - Skip commented-out includes (lines starting with
%)
Build a complete file manifest:
main.tex
├── abstract.tex (if included)
├── sections/introduction.tex
├── sections/related.tex
├── sections/methods.tex
├── sections/results.tex
├── sections/conclusion.tex
└── appendix.tex (if included)
Step 1.3: Extract Section Structure
Parse all resolved files to build the section hierarchy:
Section commands to detect:
\section{Title}
\section*{Title}
\subsection{Title}
\subsection*{Title}
\subsubsection{Title}
\paragraph{Title}
For each section, record:
- Name: The section title
- Level: section (1), subsection (2), subsubsection (3), paragraph (4)
- File: Source file containing this section
- Line: Starting line number
- Content: All text until the next section of equal or higher level
What ships with it
2 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.
- 2mo ago First seen · 470 lines · 31 tokens per session scan A 67d226e85a0f
paper-review is a skill published in the GitHub repository guruvamsi-policharla/paper-review-skill (17 stars, last pushed 9mo ago), licensed MIT. It adds 31 tokens to every session and 2,886 once invoked, about $0.0001 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.
paper-review compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| paper-reviewguruvamsi-policharla/paper-review-skill | Skill | 17 | 9mo ago | MIT | A | 31 | Claude Code, Codex |
| edgeone skill scannerTencent/AI-Infra-Guard | Skill | 7.4k | 2d ago | Apache-2.0 | A | 148 | Claude Code, Codex |
| babysitkirodotdev/KiroCrew | Skill | 4.8k | yesterday | Apache-2.0 | A | 45 | Claude Code |
| rubber-duckkirodotdev/KiroCrew | Skill | 4.8k | yesterday | Apache-2.0 | A | 161 | Claude Code, Codex |
| peer-reviewxintaofei/codeg | Skill | 4.2k | 6d ago | Apache-2.0 | A | 71 | Claude Code |
| scientific-critical-thinkingxintaofei/codeg | Skill | 4.2k | 6d ago | Apache-2.0 | A | 63 | Claude Code |
| scientific-brainstormingxintaofei/codeg | Skill | 4.2k | 6d ago | Apache-2.0 | A | 60 | Claude Code, Codex |