Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Aperivue/medsci-skills --skill generate-codebookgit clone --depth 1 https://github.com/Aperivue/medsci-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aperivue/medsci-skills/generate-codebook)<a href="https://agentmods.dev/skills/aperivue/medsci-skills/generate-codebook"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/generate-codebook/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aperivue/medsci-skills/generate-codebook"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/generate-codebook.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00068 | $0.01064 |
| Opus 5.5 | $0.00027 | $0.00426 |
| Sonnet 5.5 | $0.00014 | $0.00213 |
| Haiku 4.5 | $0.00007 | $0.00106 |
Grade A, and why
generate-codebook scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 25d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate Codebook Skill
Turn a raw tabular dataset into a structured, citable data dictionary (codebook). This is the
generator side of the dictionary-first workflow: it produces the artifact that /define-variables
and dictionary-first QC later consume. Distributions, types, and missingness are observable and the
bundled script profiles them; the meaning of a coded value (fatty_liver_grade = 0) is not
observable from the data and lives only in the authoritative data dictionary. You generate code and
review output — you do not invent the meaning of coded values.
Deterministic Script
Run the bundled profiler rather than describing columns from memory:
python "${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.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 25d ago Changed · +10 lines 6c461d01e931
- 27d ago Changed · -82 lines · -29 tokens per session 711923b19301
- 1mo ago First seen · 156 lines · 97 tokens per session scan A 043903ea13d5
generate-codebook is a skill published in the GitHub repository Aperivue/medsci-skills (353 stars, last pushed today), licensed MIT. It adds 68 tokens to every session and 1,064 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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