generate-codebook
FeaturedGenerate a citable data dictionary / codebook from a tabular dataset (CSV/TSV/Excel/Parquet/Stata/SAS). Profiles every variable — role, type, units placeholder, level frequencies, range/quantiles, missingness — and emits codebook.md + codebook.json. Flags coded variables whose level meanings are unknown as [NEEDS DICTIONARY] rather than guessing them, feeding /define-variables and the dictionary-first workflow.
Install
Quality Score: 95/100
Skill Content
Details
- Author
- Aperivue
- Repository
- Aperivue/medsci-skills
- Created
- 3 months ago
- Last Updated
- today
- Language
- Python
- License
- MIT
Bundled in these plugins
Similar Skills
Semantically similar based on skill content — not just same category
clean-data
Interactive data profiling and cleaning assistant for medical research. Three-stage workflow (profile, flag, code-generate) with user approval gates at each step. Handles missing values, outliers, duplicates, and type mismatches in CSV/Excel clinical data. Does NOT auto-clean — all decisions require researcher confirmation.
book-compiler
Turns a book on writing, editing, communication, rhetoric, storytelling, or copywriting into an executable Claude skill that performs the book's method rather than summarizing it. Use when the user supplies a book (PDF, EPUB, DOCX, or extracted text) and wants a skill built from it, wants an existing skill audited or rebuilt against its source book, or wants a library of books processed into skills.
data-tidy
Clean up messy data into a structured, validated table — from any source (a junk-filled .xlsx/CSV, a pasted email/markdown table, a Word table, or an Outlook .msg) to a clean .xlsx plus an audit/change report. Use when the user wants to "clean up this data", "tidy this spreadsheet", "normalise this list", "structure this messy export", "dedupe this", "standardise these dates / currencies", or "turn this into a clean table". (For getting data OUT of PDFs — tables, forms, scanned documents — use data-extract, not this skill.) Works INTENT-FIRST: it asks what the data is for and the expected output up front, then profiles → proposes a transform recipe → you confirm → applies it deterministically → reports every change and flags cells for review. Never mangles silently; transforms are computed locally by a deterministic engine. Standalone — no other toolkit required. NOT deal-document intelligence (lease abstraction, model review, comps) — that's out of scope.