Three public datasets. Three study types. Each produces a complete manuscript, publication-ready figures, reporting compliance audit, and presentation slides.

from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()  # 569 samples, zero download

Output from orchestrate --e2e (see full demo):

Pipeline: analyze-stats → make-figures → write-paper → AI pattern scan → check-reporting (STARD) → self-review → DOCX build → present-paper

library(metafor)
data(dat.bcg)  # 13 RCTs, 357,347 participants (Colditz et al. 1994)

Output from orchestrate --e2e (see full demo):

Pipeline: analyze-stats (R metafor) → make-figures → write-paper → AI pattern scan → check-reporting (PRISMA 2020) → self-review → DOCX build → present-paper

# Pre-processed NHANES 2017-2018 CSV included
# 4,866 US adults after exclusions

Output from orchestrate --e2e (see full demo):

Pipeline: analyze-stats → make-figures → write-paper → AI pattern scan → check-reporting (STROBE) → self-review → DOCX build → present-paper

Each demo (and real project) follows this role-based folder layout:

project/
├── data/                          # Input data
│   └── raw_data.csv
├── analysis/                      # /analyze-stats + /make-figures outputs
│   ├── tables/
│   ├── figures/
│   │   └── _figure_manifest.md
│   ├── _analysis_outputs.md
│   └── analyze.py
├── manuscript/                    # /write-paper outputs
│   ├── manuscript.md
│   ├── manuscript_final.docx
│   └── title_page.md
├── qc/                            # Quality verification
│   ├── reporting_checklist.md     # /check-reporting
│   ├── self_review.md             # /self-review
│   └── _pipeline_log.md
├── submission/                    # Post-journal-selection (manual trigger)
│   └── {journal_short}/
│       ├── cover_letter.md
│       ├── checklist.md
│       └── peer_review.md
└── presentation/
    └── presentation.pptx

The E2E pipeline (orchestrate --e2e) produces everything up to qc/. The submission/ directory is created after journal selection via /find-journal.

                              ┌─────────────────────────────────┐
                              │  orchestrate: single entry point │
                              │  classifies intent, routes to    │
                              │  the right skill or chains them  │
                              └───────────────┬─────────────────┘
                                              │
                  ┌───────────────────────────┼───────────────────────────┐
                  │                           │                           │
            intake-project              (main pipeline)             grant-builder
            (new/messy projects)              │                    (proposals)
                  │                           │
                  ▼                           ▼
                                    ┌── calc-sample-size ──┐
                                    │                      ▼
ma-scout -> search-lit -> fulltext-retrieval -> design-study ──> write-protocol -> manage-project
   │            │
   │            └── find-cohort-gap (DB variables → literature gap → ranked topic proposals)
   │                                    │
   │                                    ▼
   │                         deidentify -> clean-data -> analyze-stats -> make-figures -> write-paper
   │                                                        │                                │
   │                                           replicate-study (paper → new DB)         humanize
   │                                           cross-national (parallel survey)              │
   │                                           batch-cohort (N × M matrix)                   ▼
   │                                                                          find-journal <── self-review
   │                                                                               │                    │
   │                                                                               │                    ▼
   │                                                                               │          humanize -> academic-aio (AI-search visibility)
   │                                                                               ▼
   │                                                    [cover-letter] -> check-reporting -> revise -> present-paper
   │                                                                                                       │
   └── meta-analysis                                                                                  peer-review
                         lit-sync (Zotero + Obsidian sync)     author-strategy (PubMed profile analysis)

                              ┌─────────────────────────────────────────────┐
                              │  publish-skill: package any skill above for │
                              │  open-source distribution (PII audit,       │
                              │  license check, generalization)             │
                              └─────────────────────────────────────────────┘
                              ┌─────────────────────────────────────────────┐
                              │  add-journal: add new journal profiles to   │
                              │  the database (write-paper + find-journal   │
                              │  dual profile generation with quality gates)│
                              └─────────────────────────────────────────────┘

🇰🇷 프로그래밍 경험이 없으신가요? 한국어 설치 가이드를 따라하세요. Claude Code Desktop 앱으로 터미널 없이 설치할 수 있습니다.

git clone https://github.com/Aperivue/medsci-skills.git
cp -r medsci-skills/skills/* ~/.claude/skills/

git clone https://github.com/Aperivue/medsci-skills.git
cp -r medsci-skills/skills/check-reporting ~/.claude/skills/

1. Download ZIP from this repo (green Code button → Download ZIP)
2. Unzip and copy the skills/ folder contents to ~/.claude/skills/
3. Restart Claude Code Desktop

See the full step-by-step guide for detailed instructions with screenshots.

After copying, restart Claude Code. Skills are automatically discovered from ~/.claude/skills/.

Tip: Not sure which skill to use? Start with /orchestrate -- it will classify your request and route you to the right tool.

orchestrate --e2e or write-paper --autonomous runs the full pipeline from data to submission-ready DOCX with bounded validation. Skills pass outputs via structured manifests (_analysis_outputs.md, _figure_manifest.md) and project artifacts (project.yaml, artifact_manifest.json, qc/status.json). If a skill fails to produce expected outputs, the pipeline halts rather than proceeding with missing data. Phase 7 enforces a strict QC chain: AI pattern removal → reporting compliance check → /verify-refs citation audit → numerical claim audit → self-review with auto-fix (max 2 iterations) → DOCX/submission build.

Every reference produced by search-lit is verified against PubMed, Semantic Scholar, or CrossRef APIs. Existing manuscripts should then run /verify-refs, which writes a visible reference audit and blocks fabricated references before submission. No citation is ever generated from memory alone. API errors are batched silently -- no token waste from repeated failure messages.

/meta-analysis Phase 6b, /self-review Phase 2.5a, /revise Step 2.5, and /write-paper Step 7.3a enforce a common 3-layer audit (CSV ↔ analysis script ↔ manuscript) with primary- source back-checking for pooled estimates and revision-era numbers. Hand-typed numerical matrices without CSV-coordinate comments are flagged as structural risks even when the values are currently correct, since the next revision will re-introduce the same failure mode.

check-reporting includes bundled checklists for 33 guidelines and risk-of-bias tools: STROBE, STARD, STARD-AI, TRIPOD, TRIPOD+AI, PRISMA 2020, PRISMA-DTA, PRISMA-P, MOOSE, ARRIVE, CONSORT, CARE, SPIRIT, CLAIM, SQUIRE 2.0, CLEAR, GRRAS, MI-CLEAR-LLM, SWiM, AMSTAR 2, QUADAS-2, QUADAS-C, RoB 2, ROBINS-I, ROBINS-E, ROBIS, ROB-ME, PROBAST, PROBAST+AI, NOS, COSMIN, RoB NMA. Includes Results/Discussion section boundary checks and machine-readable JSON summary for pipeline integration.

analyze-stats generates reproducible Python/R code for 13 analysis types -- including regression, propensity score, and repeated measures -- with mandatory calibration for prediction models. make-figures produces journal-specification figures (300 DPI, colorblind-safe palettes, proper dimensions), visual/graphical abstracts, and a tool selection guide (D2 for flow diagrams, matplotlib for data plots). --study-type auto-generates the complete figure set for each study design.

write-paper enforces strict separation: Results contain only factual findings (no interpretation, no "why"), Discussion uses interactive anchor-paper scaffolding. The critic rubric includes a dedicated Section Boundaries pass/fail gate.

design-study -> calc-sample-size -> write-protocol gives you an IRB-ready protocol. After data collection: clean-data -> analyze-stats -> write-paper -> self-review -> find-journal -> cover letter. Every transition is a defined skill handoff.

Skills call each other. check-reporting invokes make-figures for PRISMA diagrams. write-paper calls search-lit for citation verification. self-review delegates reporting compliance to check-reporting. calc-sample-size output feeds directly into write-protocol's IRB justification section.

• Claude Code CLI or IDE extension
• Python 3.9+ (for statistical analysis and figure generation)
• R 4.0+ with meta (>=7.0), metafor (>=4.0), mada (>=0.5.11) packages (for meta-analysis)

"I have data and want a complete manuscript with zero manual steps."

/orchestrate --e2e      # Autonomous: analyze → figures → write → QC → DOCX

Or equivalently: /write-paper --autonomous if analysis and figures already exist.

"I have a diagnostic accuracy study draft and need to check compliance."

/design-study          # Review study design for leakage and bias
/analyze-stats         # Generate DTA statistics (sensitivity, specificity, AUC with CIs)
/make-figures          # Create ROC curve + STARD flow diagram
/check-reporting       # Audit against STARD checklist

"I'm starting a meta-analysis and need to find relevant studies."

/search-lit            # Systematic search across PubMed + Semantic Scholar
/fulltext-retrieval    # Batch download open-access PDFs for included studies
/meta-analysis         # Full DTA or intervention MA pipeline
/make-figures          # Forest plot + PRISMA flow diagram
/check-reporting       # Audit against PRISMA-DTA checklist

"I need to present a paper at journal club."

/present-paper         # Analyze paper, find supporting refs, draft speaker script

"I need to submit an IRB protocol for a new study."

/search-lit            # Background literature for rationale
/design-study          # Validate study design, identify bias risks
/calc-sample-size      # Power analysis with IRB justification text
/write-protocol        # Generate 4 core sections + 6 skeleton sections

"I have an interesting case to publish."

/write-paper           # Case report mode (CARE 2016, 1000-word short-form)
/self-review           # Pre-submission self-check
/find-journal          # Which journal accepts case reports in this field?

"My paper was rejected. Where else should I submit?"

/find-journal          # Exclude rejected journal, recommend alternatives
/write-paper           # Generate new cover letter (Phase 8+)

"I have messy clinical data that needs cleaning before analysis."

/deidentify            # Remove PHI from clinical data (standalone Python, no LLM)
/clean-data            # Profile dataset, flag issues, generate cleaning code
/analyze-stats         # Run statistics on cleaned data
/make-figures          # Publication-ready figures

"I want to write a grant proposal for a radiology AI project."

/design-study          # Validate study design before writing
/grant-builder         # Structure significance, innovation, approach
/search-lit            # Find supporting literature with verified citations

These skills are research productivity tools. They do not provide clinical decision support, medical advice, or diagnostic recommendations. All outputs should be reviewed by qualified researchers before use in any publication or clinical context.

• make-figures Critic Loop is inspired by PaperBanana (Zhu et al., Automating Academic Illustration for AI Scientists, arXiv:2601.23265, 2025) and by prior self-refinement research — Self-Refine (Madaan et al., 2023), Reflexion (Shinn et al., 2023), and Constitutional AI (Anthropic, 2022). The implementation in this repository is a clean-room reconstruction specialized for medical publication figures; no code, prompts, or configurations are derived from PaperBanana's repository.
• Reporting-guideline checklists bundled with check-reporting are redistributed under their original Creative Commons licenses (see each checklist for attribution).
• Wong colorblind-safe palette: Wong B. Points of view: Color blindness. Nature Methods 8:441 (2011).

MIT License. See LICENSE for details.

Bundled reporting guideline checklists retain their original Creative Commons licenses. See each checklist file for attribution.

Optional dependency: pdf_to_md.py uses pymupdf4llm (AGPL-3.0). Not bundled -- installed separately by the user via pip install pymupdf4llm.

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