git clone --depth 1 https://github.com/Aperivue/medsci-skills /tmp/intake-project && cp -r /tmp/intake-project/skills/intake-project ~/.claude/skills/intake-projectSKILL.md
# Intake-Project Skill
## Purpose
This skill is the front door for a new or messy project. It converts a folder, document bundle, or mixed set of notes into a structured project state that other skills can use safely.
Use this skill when:
- a new paper or proposal folder has been created
- an older folder exists but is poorly organized
- the user asks "what is this project and what should I do next?"
- another skill needs a reliable project summary before proceeding
---
## Communication Rules
- Communicate with the user in their preferred language.
- Keep project labels and file names in the language already used by the workspace.
- Use English for manuscript section names, study design names, and medical/statistical terminology.
---
## Inputs
Accept any of the following:
- a project folder
- a manuscript draft
- an abstract or proposal
- tables/figures plus notes
- a mixed folder with PDFs, drafts, and analyses
If information is incomplete, infer cautiously from file names and contents, then label uncertain items clearly.
---
## Core Tasks
### 1. Project classification
Determine:
- project type: `original | review | meta-analysis | case report | technical note | grant | peer review | challenge | career-doc`
- primary domain: `radiology | medical AI | multimodal LLM | intervention | survival/prognostic | diagnostic accuracy | workflow`
- target output: `paper | abstract | grant | review | rebuttal | CV`
- likely target journal or venue, if recoverable
### 2. State reconstruction
Identify:
- what already exists
- what is missing
- current phase
- blocking dependencies
### 3. Project memory scaffold
If missing, propose or create lightweight anchor files:
- `PROJECT.md`
- `STATUS.md`
- `CLAIMS.md`
- `DATA_DICTIONARY.md`
- `ANALYSIS_PLAN.md`
- `REVIEW_LOG.md`
Create only files that are justified by the project type.
### 4. Action plan
Produce the next 3-5 actions in dependency order.
---
## Canonical Manuscript Folder Structure
For any manuscript project (cohort, MA, RCT, case series), enforce this structure when scaffolding or reorganizing. Map every new artifact into one of these slots — do not invent ad-hoc folders.
```
{project_root}/
├── HANDOFF.md # session handoff entry point
├── README.md # project overview
├── data/ # raw data (NEVER edit; read-only)
├── analysis/ # reproducible scripts (00_* → 04_*)
├── output/ # analysis outputs: CSVs, PNGs, intermediates
├── irb/ # IRB/ethics docs
├── proposal/ # original protocol / approved proposal
├── reviews/ # external correspondence
├── manuscript/ # SOURCE manuscript + drafting
│ ├── manuscript_v{N}.{md,docx,pdf} # current canonical working version (top level)
│ ├── build_unified_docx.py # or pandoc wrapper
│ ├── archive/ # ALL prior versions v1 .. v{N-1}
│ ├── reviews/ # QC: self_review, peer_review, STROBE/PRISMA, critic
│ ├── figures/ # figure scripts + rendered PNG/PDF
│ └── tables/ # table scripts + rendered docx
└── submission/ # per-journal packages
└── {journal-slug}/ # e.g., chest/, kjr/
├── CHECKLIST.md
├── cover_letter.{md,docx,pdf}
├── title_page.docx # separated for double-anonymized
├── manuscript_anonymized.{docx,pdf}
├── supplement.{docx,pdf}
├── strobe_checklist.md # or PRISMA / CONSORT
├── circulation_email.md
└── figures/ # submission-ready DPI copies
```
### Rules
- **`manuscript/` = source; `submission/{journal}/` = derived artifacts.** Regenerate submission files from `manuscript/manuscript_v{N}.md`; never edit anonymized/title-page directly.
- **One canonical working version** at `manuscript/manuscript_v{N}.{md,docx,pdf}`. Older versions move to `manuscript/archive/` immediately on version bump.
- **No loose files at project root.** Only `HANDOFF.md`, `README.md`, folder entries.
- **QC artifacts** (self_review, peer_review, STROBE, critic reports) live in `manuscript/reviews/`, not at manuscript top level.
- **On rejection/retarget:** `cp -r submission/{old} submission/{new}`, then rewrite cover letter and reformat.
- **Double-anonymized journals** (Chest, AJRCCM): title page and anonymized manuscript MUST be separate files under `submission/{journal}/`.
### When to apply
- At project intake: scaffold empty structure.
- At first submission prep: create `submission/{journal}/` and populate.
- Mid-project cleanup: when `manuscript/` has >3 versioned files or QC docs at top level, reorganize.
- Before session handoff: reorganize if structure is drifting.
**Precedent:** an STROBE cohort with a mortality endpoint reorganized v1–v6 plus QC docs from the manuscript/ top level so that a reject-retarget path to a different journal requires only `cp -r submission/chest submission/<new_journal>`.
---
## Workflow
### Phase 1: Discover context
1. Read top-level folder names and key files.
2. Detect manuscript-like files, tables, figures, protocols, and analysis outputs.
3. Extract:
- project title or working title
- study question
- dataset or cohort hints
- collaborators or institutions
- venue/journal hints
### Phase 2: Classify project stage
Assign one current stage:
- `idea`
- `data assembly`
- `analysis planning`
- `analysis in progress`
- `drafting`
- `revision`
- `submission prep`
- `archived/unclear`
**Gate:** Present the classification (project type, stage, target output) to the user.
Confirm before creating any files — misclassification leads to wrong scaffold and
wrong skill routing.
### Phase 3: Surface missing inputs
Check for common gaps:
- no explicit study question
- no target jourMedical 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.
>
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.
>
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.