# Journal Club Review > 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: `axect/journal-club-review` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add axect/journal-club-review` - Raw SKILL.md: https://api.skillmd.com/api/skills/axect/journal-club-review/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Security - Author: Axect (https://skillmd.com/u/axect) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/axect/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`: ```bash 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. For arXiv ids and local PDF paths it also keeps the paper itself at `/source.pdf`. That file is part of the deliverable: `source.md` is a lossy text dump with the LaTeX gone, so any verbatim quote you plan to reuse should be checked against `source.pdf`, and step 6 archives it alongside the review. Text and LaTeX inputs have no PDF to keep, which is fine. 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 `![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: `/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. ### 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 `/review.md`, producing `/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//reviews//`. 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//reviews//`: `review.md`, `review.pdf`, `source.md`, `source.pdf` (the original paper, when the input produced one), `figures_manifest.json` (when it exists), and the `figures/` directory (both `figures/paper/` real figures and the generated `figures/*.png` infographics). Match the existing layout in sibling review folders. The archive is the long-lived copy, so the original PDF belongs there: a text extraction cannot settle a page reference, an equation number, or a figure panel later. 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, and `source.pdf` for arXiv/PDF inputs (arXiv is downloaded, a local PDF is copied); 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.