Journal Club Review

Journal-Club Review

Turn any paper (arXiv ID/URL, PDF, text, or a local LaTeX source) into a journal-club presentation: a warm, accurate, discussion-oriented walkthrough in nine sections, with LaTeX math, the paper's real figures (when source is available), and two optional infographic figures. This is a teaching/discussion artifact, not a referee report: no scores, no accept/reject.

When to use

• "Give me a journal-club review of 2401.00001"
• "Review this PDF like a journal club" / "make presentation notes for this paper"
• "이 논문 저널클럽 리뷰 만들어줘" / "발표자료처럼 정리해줘"
• Any text or markdown draft the user wants walked through for a reading group.

If the user wants a peer-review referee report (rating, confidence, weaknesses for OpenReview) use workshop-paper-review. For an adversarial pre-submission audit of their own draft use adversarial-review.

Inputs

One of:

• arXiv id (2401.00001, 2401.00001v2, hep-ph/0101001) or arXiv URL
• a local PDF path
• a local .md / .txt path, or pasted text
• a local LaTeX source: a .tex file or a project directory (e.g. an Overleaf

checkout). Figures referenced by the source are harvested automatically.

Workflow

1. Ingest the source

Run the extractor to get a uniform working directory with source.md:

uv run scripts/extract_text.py ""

(scripts/extract_text.py is relative to this skill's base directory; pass an absolute path if your cwd is elsewhere.)

It prints a JSON summary (slug, title, authors, categories, out_dir, source_md, n_chars, plus n_figures, figures_dir, figures_manifest) and writes /source.md (default ./reviews//). For pasted text, save it to a .md file first, then pass that path.

When LaTeX source is available (arXiv e-print tarball, or a local .tex/dir input), the extractor also converts the paper's figures to PNG under /figures/paper/ and writes /figures_manifest.json (n_figures > 0). For PDF-only or plain-text inputs there are no source figures (n_figures is 0); that is fine, just skip step 3.

Read source.md. If extraction yielded little text (scanned PDF, n_chars small), tell the user and proceed with whatever is available (abstract-level).

2. Detect language and write the review

Read references/style-and-math.md, references/section-pipeline.md.

• Detect the source language; write the review in that language unless the user

asked otherwise (see style-and-math.md).

• Produce all nine sections in order, grounded in source.md with section /

equation / figure citations. Render all math as LaTeX ($...$, $$...$$).

• Build a method figure brief inside section 4 and a results figure brief

inside section 5 (schema in references/figure-generation.md).

• Follow the output skeleton in section-pipeline.md. Save the draft to

/review.md (leave figure image lines out until steps 3-4 confirm which figures exist).

3. Embed the paper's real figures (when source available)

If n_figures > 0, read references/figure-generation.md ("Real source figures") and /figures_manifest.json. Curate the most relevant figures and embed them into the matching sections with their captions (figures/paper/.png): overview/architecture/schematic into How It Works, result plots into Key Results. Verify each embedded PNG is non-empty before referencing it. Do not dump every figure; note any a reader might expect that you skipped.

Skip if n_figures is 0 (PDF/text input).

4. Generate infographics (optional, on by default)

Read references/figure-generation.md ("Generated infographics"). Check codex login status. If logged in, compose the two friendly-whiteboard prompts from the briefs and launch both codex exec jobs in parallel into /figures/. After they finish, embed ` and ` for whichever PNGs are non-empty; note any that were skipped. These coexist with the real figures from step 3.

Skip this step if the user passed --no-figures / "no images" / "text only", or if codex is not logged in (then say so and keep the review text-only).

5. Deliver

• Final file: /review.md (with figures alongside in

/figures/: real figures in figures/paper/, infographics in figures/).

• Tell the user the path and give a 1-2 line summary.
• If the review is Korean, offer to export a PDF with the md2pdf-typora skill.

Notes

• This skill is self-contained: it does not require the arXiv Explorer app. It

reuses that project's journal-club section design and figure style, but Claude itself does the analysis here.

• For arXiv inputs the extractor uses the PDF for text and the e-print tarball

for figures. If you have the LaTeX source already, pass the .tex/dir path instead for cleaner math and section structure plus the same figure harvest.

• Real-figure conversion uses whatever rasterizer is on PATH (pdftoppm,

magick/convert, or gs); if none is present, vector figures are skipped and only raster (PNG/JPG) figures survive.

• Generated infographics depend on a logged-in bundled codex runtime (ChatGPT

OAuth). Without it the review still renders, just text-only.

Files

• scripts/extract_text.py: arXiv/PDF/text/LaTeX -> source.md + JSON metadata;

harvests real figures to figures/paper/ + figures_manifest.json when LaTeX source is available (PEP 723 inline deps: pdfplumber, httpx, feedparser; uses system pdftoppm/magick/gs for conversion; run with uv run).

• references/section-pipeline.md: the nine sections and output skeleton.
• references/figure-generation.md: real-figure embedding policy, infographic

briefs, style block, codex command.

• references/style-and-math.md: language rule, LaTeX math, tone, anti-patterns.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Axect
• Source: Axect/skills
• License: MIT

Install and usage instructions live in the source repository linked above.