Generate Codebook Skill

Turn a raw tabular dataset into a structured, citable data dictionary (codebook). This is the generator side of the dictionary-first workflow: it produces the artifact that /define-variables and dictionary-first QC later consume. Distributions, types, and missingness are observable and the bundled script profiles them; the meaning of a coded value (fatty_liver_grade = 0) is not observable from the data and lives only in the authoritative data dictionary. You generate code and review output — you do not invent the meaning of coded values.

Deterministic Script

Run the bundled profiler rather than describing columns from memory:

python "${CLAUDE_SKILL_DIR}/scripts/generate_codebook.py" data.csv --out-dir .

Supports .csv/.tsv/.xlsx/.parquet/.dta/.sas7bdat. Flags: --max-levels N (categorical cutoff, default 20), --json-only, --md-only. The script is pandas-only, runs locally, and never sends data anywhere.

Workflow

Step 1: Profile (deterministic)

Run generate_codebook.py on the dataset. It writes codebook.json (machine- readable) and codebook.md (review table), reporting per variable: role (id / continuous / categorical / binary / date / text), dtype, missingness, unique count, level frequencies or quantile summary, and a needs_dictionary flag.

Step 2: Review with the researcher (gate)

Read ${CLAUDE_SKILL_DIR}/references/codebook_schema.md (the codebook.json schema, the role-inference heuristics, the needs_dictionary rule) before interpreting the output. Present codebook.md and walk the user through it. Gate: role inference is a heuristic, so the user confirms the inferred roles (e.g., an integer-coded scale mis-read as continuous, or an id column). Do not proceed to definition work until the user approves the role assignments.

Step 3: Resolve [NEEDS DICTIONARY] items (gate)

For every variable flagged needs_dictionary: true, the level codes are uninterpretable without the authoritative source. Gate: ask the user to supply the meaning of each code from the real data dictionary (file/sheet/row), or to confirm none exists. Fill label, units, and per-level meanings into the codebook only from that source, citing file > sheet > row — never from inference. If the user cannot supply it, leave the [NEEDS DICTIONARY] marker in place; do not erase it.

Example: in a cohort file, sex (levels 1/2) and fatty_liver_grade (0..4) are flagged because their levels are bare codes; smoking_status (never/former/current) is not. Never write sex: 1 = male because "that is the usual coding" — if the dictionary is unavailable, the flag stays.

Step 4: Hand off

The completed codebook.json becomes the input dictionary for /define-variables (operationalization) and the citation source for dictionary-first QC. Gate: confirm with the user that no needs_dictionary flags remain unresolved before the codebook is treated as authoritative for downstream analysis.

Run /deidentify on the raw data before a codebook is shared externally. Cleaning or transforming data is /clean-data.

Known limits

• Codes longer than 3 characters that are not numbers (C50.9, S001) are not recognised as codes, so their column is not flagged needs_dictionary.
• An integer-coded column with more distinct values than --max-levels is classed continuous and is not flagged. The Step 2 role review is where such columns are caught.
• The same holds for a text column holding number strings: any number value (45, 88) is read as a measurement, so integer codes exported as strings with more than --max-levels values are not flagged.

Output Format

codebook.json (schema in references) and codebook.md (review table with a "Columns requiring dictionary lookup" section). Summarize the counts (rows, columns, needs_dictionary_count) in chat; do not paste the full JSON.