git clone --depth 1 https://github.com/Aperivue/medsci-skills /tmp/present-paper && cp -r /tmp/present-paper/skills/present-paper ~/.claude/skills/present-paperSKILL.md
# Present-Paper Skill ## Purpose Prepare a polished academic presentation from a research paper. The skill walks through a 5-phase pipeline: paper analysis, supporting research, script writing, slide note injection, and Q&A preparation. Use it when: - preparing a journal club or seminar presentation - presenting a paper for a graduate course - preparing grand rounds or conference talks based on a published paper - building speaker notes for an existing slide deck --- ## Communication Rules - Communicate with the user in their preferred language. - Use English for medical, statistical, and methodological terminology. - Add pronunciation guides for drug names and technical abbreviations in the user's language. - Be direct about paper limitations, but frame them constructively. --- ## Phase 0: Init & Outline ### Step 0a — Load design references (read before drafting outline) Before collecting inputs, the skill loads three reference files: 1. **`references/slide_design_principles.md`** — Reynolds (Presentation Zen) + Duarte (Slide:ology Glance Test™) + Knaflic (Storytelling with Data preattentive attributes) + Tufte (Cognitive Style of PowerPoint). Defines the 5 design principles, reading-time budgets per audience, cognitive-load ceilings, and the anti-patterns this skill is built to avoid. **Read this first** — it shifts the outline from "what content fits" to "what should the audience remember 10 seconds after each slide." 2. **`references/medical_presentation_templates.md`** — Section structure, slide counts, and design seeds for the 5 contexts: journal club, grand rounds, conference talk, lecture, and academic lecture multi-paper survey. Pick the matching template after Phase 0 inputs are collected, then customize. 3. **`references/slide_visual_styles/`** — visual style specs (color palette, typography, layout grid, slide-type templates) callable from any of the 5 context templates. Currently available: `nature_lancet.md` (Nature/Lancet aesthetic — white background, navy primary, coral accent, Inter/Pretendard). Default for academic lectures per `~/.claude/rules/academic-lecture-style.md`. Paired with the generic builder `templates/build_pptx_nature_lancet.py` and the PDF figure extractor `scripts/extract_pdf_figures.py`. These two files mirror the entry-point pattern used in `make-figures/references/design_principles.md` (Step 1 "Specify"). Both skills share the same Reynolds / Knaflic / Tufte foundations — slide-level (this skill) and figure-level (make-figures) are companions, not duplicates. ### Required Inputs Before starting, collect these from the user: | Input | Why | |-------|-----| | **Paper** | PDF path, DOI, or PMID | | **Presentation time** | Determines depth and slide count | | **Target audience** | Specialty mix, knowledge level — controls terminology depth | | **Context** | Course name, conference, journal club format, prior session topics | | **Extension section** | Optional topic to include (e.g., AI directions, clinical implications). Default: none | ### Paper Analysis Read the paper and produce a structured analysis: ```text ## Paper Analysis ### Citation [Full citation with DOI] ### Background - What gap does this paper address? - What was known vs. unknown before this study? ### Study Design - Type: [RCT / cohort / case series / meta-analysis / etc.] - Subjects: [n, inclusion/exclusion] - Methods: [key methodological choices] - Primary outcome: [what was measured] ### Key Results 1. [Finding 1 with effect size and CI/p-value] 2. [Finding 2] 3. [Finding 3] ### Patient/Case Summary Table [If applicable — structured table of individual cases or subgroups] ### Limitations 1. [Limitation 1] 2. [Limitation 2] ### Significance - Why does this matter? - What changes because of this paper? ``` ### Slide Outline Create a slide-by-slide outline with time allocation: ```text ## Slide Outline ([N] slides, [M] minutes) | # | Title | Time | Key Content | |---|-------|------|-------------| | 1 | Title slide | 0:30 | Paper citation, presenter | | 2 | Context / Prior sessions | 1:00 | How this connects to prior knowledge | | 3 | Background | 1:30 | The gap this paper fills | | ... | ... | ... | ... | | N | Take-home messages | 0:30 | 3-5 key points | ``` **Gate: User approves outline before proceeding.** --- ## Phase 1: Supporting Research ### Search Strategy Find references that strengthen the presentation: 1. **Follow-up studies** — Has the main finding been replicated or extended? 2. **Clinical trial data** — Large-scale data that contextualizes the findings 3. **Review articles** — Authoritative summaries that frame the topic 4. **Contradicting evidence** — Important for balanced Q&A preparation **Efficiency rule:** Limit supporting references to 5-8 total. Only search categories that the approved outline (Phase 0) actually requires. Skip categories not needed for the presentation type (e.g., skip clinical trials for a methods-focused paper). ### Selection Criteria Do NOT summarize every paper found. Extract only: - Specific data points needed for slides (incidence rates, OR/HR, AUC values) - Findings that directly support or challenge the main paper - Context that helps the audience understand significance ### Output ```text ## Verified References ### Main Paper 1. [Citation] — PMID: XXXXX, DOI: XX.XXXX/XXXXX ### Supporting References 2. [Citation] — PMID: XXXXX → Used for: [specific data point or context] 3. [Citation] — PMID: XXXXX → Used for: [specific data point or context] ### Key Data for Slides - [Statistic 1]: [value] — Source: [Ref #] - [Statistic 2]: [value] — Source: [Ref #] ``` **Every reference must have a verified DOI or PMID. Mark unverified references with [UNVERIFIED].** --- ## Phase 2: Script & Content ### Speaker Script Draft a complete speaker script with these requirements: 1. **Language**: User's preferred language for narration; English for technical term
Medical AI paper optimization for AI search engines (Perplexity, ChatGPT web, Elicit, Consensus, SciSpace) and RAG-based literature tools. Applies when drafting or reviewing titles, abstracts, structured summary boxes (Key Points / Research in Context / Plain-Language Summary), manuscripts for high-impact medical AI journals (Lancet Digital Health, Radiology, Radiology-AI, npj Digital Medicine, Nature Medicine), preprints (medRxiv/arXiv), GitHub README + CITATION.cff + Zenodo archives, and Hugging Face model/dataset cards. Integrates TRIPOD+AI, CLAIM 2024, STARD-AI, TRIPOD-LLM, DECIDE-AI reporting requirements with generative engine optimization (GEO) principles. Produces a visible pass/fail checklist.
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Statistical analysis for medical research papers. Generates reproducible Python/R code with publication-ready tables and figures. Supports diagnostic accuracy, inter-rater agreement, meta-analysis, survival analysis, survey data, group comparisons, regression, propensity score, and repeated measures.
PubMed author profile analysis. Author name → PubMed fetch → study type classification → visualization → strategy report.
Generate N analysis scripts from a single methodology template × multiple exposure/outcome combinations. The "80-person team" pattern — same validated method, swap variables only. Produces batch R/Python code + summary matrix.
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Check manuscript compliance with medical research reporting guidelines. Supports 32 guidelines including STROBE, CONSORT, STARD, STARD-AI, TRIPOD, TRIPOD+AI, ARRIVE, PRISMA, PRISMA-DTA, PRISMA-P, CARE, SPIRIT, CLAIM, MI-CLEAR-LLM, SQUIRE 2.0, CLEAR, MOOSE, GRRAS, SWiM, AMSTAR 2, and risk of bias tools (QUADAS-2, QUADAS-C, RoB 2, ROBINS-I, ROBINS-E, ROBIS, ROB-ME, PROBAST, PROBAST+AI, NOS, COSMIN, RoB NMA). Generates item-by-item assessment with PRESENT/MISSING/PARTIAL status.