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Journal club review

Skill Axect/skills/journal-club-review

Reusable skills for AI coding agents (Claude Code, Codex, Forge) covering paper review, commit triage, GPU rentals, reference search, research logs, image-prompt composition, and more.

Install
npx -y skills add Axect/skills --skill journal-club-review

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Produce a journal-club-style paper presentation (9 sections: TL;DR, Problem, Key Idea, How It Works, Key Results, Why It Matters, Strengths/Limitations/Open Questions, Discussion Questions, Takeaways) from an arXiv ID/URL, a PDF, raw text/markdown, or a local LaTeX source (.tex / project dir). Helps a reading group UNDERSTAND and DISCUSS the paper — not score or accept/reject it. Grounds every claim in the source, renders math as LaTeX, auto-matches the source language (Korean source -> Korean review). When LaTeX source is available (arXiv e-print or local .tex), embeds the paper's real figures with captions; optionally generates two friendly-whiteboard infographic figures via the bundled codex image_generation tool. Use when the user wants a journal-club review, paper walkthrough/presentation, or paper explainer, to "review this PDF/paper like a journal club", or 논문 저널클럽 리뷰/발표자료/논문 설명. For an OpenReview referee report use workshop-paper-review; for an adversarial pre-submission audit use adversarial-review.

SKILL.md

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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 "<arxiv-id | url | path>"

(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 <out_dir>/source.md (default ./reviews/<slug>/). 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 <out_dir>/figures/paper/ and writes <out_dir>/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 <out_dir>/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 <out_dir>/figures_manifest.json. Curate the most relevant figures and embed them into the matching sections with their captions (figures/paper/<name>.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 <out_dir>/figures/. After they finish, embed ![Method overview](figures/method.png) and ![Key results](figures/results.png) 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: <out_dir>/review.md (with figures alongside in <out_dir>/figures/: real figures in figures/paper/, infographics in figures/).
  • Tell the user the path and give a 1-2 line summary.

6. Export PDF and file into the JournalClub archive (default)

Every finished review is exported to PDF and mirrored into the user's ~/Dropbox/JournalClub archive. Do this automatically, do not wait to be asked.

  1. Export the PDF with the md2pdf-typora skill on <out_dir>/review.md, producing <out_dir>/review.pdf. (This runs regardless of the review language; the Whitey theme handles Korean and English both.)
  2. Pick the topic folder. The archive is organised as ~/Dropbox/JournalClub/<Topic>/reviews/<slug>/. List the existing topics (ls ~/Dropbox/JournalClub) and choose the one that fits the paper. If none fits, ask the user which topic to use or whether to create a new one (e.g. SpectralGeometry, InverseProblem); create it only after they confirm the name. Do not silently invent a topic.
  3. Copy the artifacts into ~/Dropbox/JournalClub/<Topic>/reviews/<slug>/: review.md, review.pdf, source.md, and the figures/ directory (both figures/paper/ real figures and the generated figures/*.png infographics). Match the existing layout in sibling review folders.
  4. Confirm the destination path and file sizes to the user.

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.

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