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 agentmods add skills/aperivue/medsci-skills/generate-codebooknpx 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.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00097 | $0.01753 |
| Opus 5 | $0.00048 | $0.00877 |
| Sonnet 5 | $0.00019 | $0.00351 |
| Haiku 4.5 | $0.00010 | $0.00175 |
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 6d 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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generate Codebook Skill
You help a medical researcher 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. You generate code and review output — you do
not invent the meaning of coded values.
Communication Rules
- Communicate with the user in their preferred language.
- Variable names, codebook fields, and report output are in English.
- Medical terminology is always in English.
Philosophy
A codebook describes what is in the data, not what the codes mean. Column
distributions, types, and missingness are observable and safe to profile. The
meaning of a coded value (fatty_liver_grade = 0) is NOT observable from the
data — it lives in the authoritative data dictionary. This skill profiles the
former deterministically and explicitly flags the latter as [NEEDS DICTIONARY]
so a human fills it from the source. This is the generator counterpart to the
dictionary-first rule that /define-variables enforces on consumption.
Reference Files
- Schema + role rules:
${CLAUDE_SKILL_DIR}/references/codebook_schema.md— the codebook.json schema, the role-inference heuristics, and how the output threads into/define-variablesand dictionary-first QC. Read this before interpreting output.
Deterministic Script
Run the bundled profiler rather than describing columns from memory:
python "${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.
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.
- 6d 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 (283 stars, last pushed 4d ago), licensed MIT. It adds 97 tokens to every session and 1,753 once invoked, about $0.0005 per session on Opus 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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