paper-figures

paper-figures is a skill for Claude Code, Codex from Muuuun/luxas. It costs 79 tokens per session (668 once invoked), scanned A, original, MIT.

A workflow for taking figures from downloaded research papers and adding useful ones to survey or review reports.

In plain words
What is it for?
It helps extract figures from PDFs or arXiv source folders, review captions, record whether each figure is used or skipped, and select figures for a report.
Why use it?
It prevents reports from overlooking diagrams, plots, and system illustrations that can explain other researchers' work more clearly than text alone.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps extract figures from PDFs or arXiv source folders, review captions, record whether each figure is used or skipped, and select figures for a report.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/muuuun/luxas/paper-figures
Install

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.

Any agent
npx skills add Muuuun/luxas --skill paper-figures
Clone the repo
git clone --depth 1 https://github.com/Muuuun/luxas

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for paper-figures

README.md
[![agentmods](https://agentmods.dev/badge/skills/muuuun/luxas/paper-figures/github.svg)](https://agentmods.dev/skills/muuuun/luxas/paper-figures)
Your own site
<a href="https://agentmods.dev/skills/muuuun/luxas/paper-figures"><img src="https://agentmods.dev/badge/skills/muuuun/luxas/paper-figures/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.

agentmods 80×15 button for paper-figures

Your own site · 80×15
<a href="https://agentmods.dev/skills/muuuun/luxas/paper-figures"><img src="https://agentmods.dev/badge/skills/muuuun/luxas/paper-figures.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 79 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 668 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

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 →

  • medium Agent Snooping · line 63
    Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.
    Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00079 $0.00668
Opus 5 $0.00039 $0.00334
Sonnet 5 $0.00016 $0.00134
Haiku 4.5 $0.00008 $0.00067

Measured 10d ago against content hash b63f3002e4bc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

paper-figures 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 10d 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.

skills/paper-figures/SKILL.md · 64 lines

How it starts

The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Paper Figures Skill

Figures are information. A survey/review report covering downloaded papers MUST include key figures — architecture diagrams, experimental results, comparisons. Do NOT write a text-only survey when figures are available.

3-step workflow

Step 1 — Extract figures from downloaded papers

Your brain prompt supplies the script's absolute path as {{EXTRACT_FIGURES}}. Invoke it once per paper:

{{EXTRACT_FIGURES}} data/papers/<paper-id>.pdf   # single PDF
{{EXTRACT_FIGURES}} data/papers/<arxiv-id>        # arXiv source directory

This creates data/papers/<id>_figures/ with extracted images and a manifest.json listing each figure with caption and page number.

Step 2 — Review captions and classify every figure

Read every manifest:

cat data/papers/*_figures/manifest.json

Record your decisions in notes/memory.md under a ## Figure Review section. Every figure must end up in one of three states:

  • USE — essential for understanding the topic; will be included in the report
  • SKIP — irrelevant, redundant, or low quality
  • UNREVIEWED — caption not yet read (only acceptable as a transient state)

Select figures that are:

  • Essential for understanding the topic (architecture diagrams, system schematics)
  • Key experimental results that support your narrative
  • Useful comparisons across methods, systems, or time periods
  • Visually informative (not just tables rendered as images)

Step 3 — Include USE figures in the report

For each figure marked USE, embed it directly in LaTeX:

\begin{figure}[t]
  \centering
  \includegraphics[width=\linewidth]{../data/papers/<id>_figures/<filename>}
  \caption{<Your caption describing the figure in the context of your survey>. Adapted from \cite{<key>}.}
  \label{fig:<label>}
\end{figure}

Rules

  • Coverage: survey/review reports include at least 3-5 USE figures from downloaded papers, in addition to any figures you generate yourself.
  • Captions: write your OWN caption in the context of the survey narrative — do not copy the original caption.
  • Attribution: always cite the source paper with \cite{}.
  • No skipping the review step: every extracted figure must be classified before writing the report.
  • Complementary: you may also generate your own figures (see skills/matplotlib-figures/SKILL.md) for data summaries, timelines, or cross-paper comparisons not found in existing figures.

Read the full file on GitHub · 64 lines

Changes

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.

  1. 10d ago First seen · 64 lines · 79 tokens per session scan A b63f3002e4bc

Subscribe to this mod's changes

paper-figures is a skill published in the GitHub repository Muuuun/luxas (1,003 stars, last pushed 3d ago), licensed MIT. It adds 79 tokens to every session and 668 once invoked, about $0.0004 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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