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 claesbackman/AI-research-feedback --skill review-papergit clone --depth 1 https://github.com/claesbackman/AI-research-feedbackWrote 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/claesbackman/ai-research-feedback/review-paper)<a href="https://agentmods.dev/skills/claesbackman/ai-research-feedback/review-paper"><img src="https://agentmods.dev/badge/skills/claesbackman/ai-research-feedback/review-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.
<a href="https://agentmods.dev/skills/claesbackman/ai-research-feedback/review-paper"><img src="https://agentmods.dev/badge/skills/claesbackman/ai-research-feedback/review-paper.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 170 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00000 | $0.08759 |
| Opus 5.5 | $0.00000 | $0.03504 |
| Sonnet 5.5 | $0.00000 | $0.01752 |
| Haiku 4.5 | $0.00000 | $0.00876 |
Grade A, and why
review-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 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 — 664 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are coordinating a rigorous pre-submission review of an academic economics paper. You will run 8 specialized review agents in parallel and consolidate their findings into a structured report.
Phase 1: Parse Arguments and Discover the Paper
Parse $ARGUMENTS as follows:
- The recognized journal names are:
- Top-5 economics:
AER,QJE,JPE,Econometrica,REStud - Finance:
JF,JFE,RFS,JFQA - Macro:
AEJMacro,JME,RED - (case-insensitive; users can add further journals by editing this list in the skill file)
- Top-5 economics:
- If the first token of
$ARGUMENTSmatches one of these names, treat it as the target journal and treat any remaining text as the file path. - If no token matches a journal name, treat the entire
$ARGUMENTSas a file path and set the target journal totop-field(meaning the review applies high general standards without a specific journal persona). - If
$ARGUMENTSis empty, set both to their defaults: no file path (auto-detect) and target journaltop-field.
Store the resolved target journal as TARGET_JOURNAL for use in Agent 6 and the report header.
If a file path was provided, use it as the main LaTeX file. Otherwise, auto-detect:
- Use Glob with pattern
**/*.texto list all .tex files in the current directory (exclude any_minted-*or build output folders). - Identify the main document among the .tex files that contain
\documentclassor\begin{document}. Read each candidate briefly if needed. Multiple candidates are common — old drafts, beamer slides, and response letters also contain\documentclass— so apply these rules in order:- Discard files whose document class is
beamer(slides) and files whose name or folder suggests they are not the current paper: names likeresponse*,letter*,slides*,presentation*,old*, or files inside folders namedold/,archive/,previous/,submitted/, or similar. - Among the remaining candidates, choose the one with the largest include-graph (the most
\input{}/\include{}/\subfile{}references, counted recursively). - If it is still ambiguous, ask the user which file is the main document before proceeding.
- Discard files whose document class is
- Read the main file and extract all
\input{},\include{}, and\subfile{}references (recursively) to build the paper's include-graph. - Read all component .tex files to understand the complete paper structure (introduction, data, methodology, results, appendix, etc.). The .tex file list passed to the review agents in Phase 2 is exactly the main file plus its include-graph. Do not pass .tex files that the glob in step 1 found but the paper does not include (old drafts, response letters, slides, notes).
- Use Glob to list figure files: patterns covering common directories and formats:
**/Figures/**/*.pdf,**/figures/**/*.pdf,**/Figure/**/*.pdf,**/figure/**/*.pdf**/Figures/**/*.png,**/figures/**/*.png,**/Figure/**/*.png,**/figure/**/*.png**/Figures/**/*.eps,**/figures/**/*.eps,**/Figure/**/*.eps,**/figure/**/*.eps**/Figures/**/*.jpg,**/figures/**/*.jpg,**/Figure/**/*.jpg,**/figure/**/*.jpg**/Figures/**/*.jpeg,**/figures/**/*.jpeg,**/Figure/**/*.jpeg,**/figure/**/*.jpeg**/Figures/**/*.svg,**/figures/**/*.svg,**/Figure/**/*.svg,**/figure/**/*.svg- Root-level:
*.pdf,*.png,*.eps,*.jpg,*.jpeg,*.svg - Exclude:
**/_minted-*/**,**/build/**,**/output/**,**/.git/**
- Use Glob to list table files: patterns covering common directories:
**/Tables/**/*.tex,**/tables/**/*.tex,**/Table/**/*.tex,**/table/**/*.tex- Root-level:
*table*.tex,*Table*.tex - Exclude:
**/_minted-*/**,**/build/**,**/output/**,**/.git/**
- Filter to files the paper actually uses: keep only figure files whose path stem appears in an
\includegraphics{...}call somewhere in the include-graph (LaTeX often omits the file extension — match on the stem), and only table files that are\input{}/\include{}d from the include-graph. Record any excluded, unreferenced files separately — do not pass them to the agents; they are often stale outputs from earlier drafts.
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 · 664 lines · 0 tokens per session scan A 78ee0fe52db6
review-paper is a skill published in the GitHub repository claesbackman/AI-research-feedback (528 stars, last pushed 9d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 8,759 tokens. 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.
review-paper compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| review-paperclaesbackman/AI-research-feedback | Skill | 528 | 9d ago | MIT | A | 0 | Claude Code |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 2d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | today | MIT | A | 43 | Claude Code, Codex |
| mapping-to-snomedmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 205 | Claude Code |