# 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 cl… - **Type:** Skill - **Install:** `agentstack add skill-axect-skills-journal-club-review` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [Axect](https://agentstack.voostack.com/s/axect) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [Axect](https://github.com/Axect) - **Source:** https://github.com/Axect/skills/tree/main/journal-club-review ## Install ```sh agentstack add skill-axect-skills-journal-club-review ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # 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. 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](https://github.com/Axect) - **Source:** [Axect/skills](https://github.com/Axect/skills) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** yes - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-axect-skills-journal-club-review - Seller: https://agentstack.voostack.com/s/axect - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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