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 version-datasetgit 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/version-dataset)<a href="https://agentmods.dev/skills/aperivue/medsci-skills/version-dataset"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/version-dataset.svg" alt="Measured on agentmods" 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 | $0.00086 | $0.01477 |
| Opus 5 | $0.00043 | $0.00739 |
| Sonnet 5 | $0.00017 | $0.00295 |
| Haiku 4.5 | $0.00009 | $0.00148 |
Grade A, and why
version-dataset 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 8d 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Dataset Skill
You help a medical researcher put a dataset under version control: fingerprint it, detect when it changes, and lock a reproducible version. This guards the data-integrity rule — an analysis must run on the data it claims to, with a fixed seed — by making any drift between runs loud instead of silent.
Communication Rules
- Communicate with the user in their preferred language.
- Manifest fields, drift reports, and provenance notes are in English.
Philosophy
A dataset is an input to a result; if it changes silently, every downstream number is suspect. This skill records a deterministic fingerprint (file SHA-256 +, for tabular files, schema and per-column value hashes) so a later run can prove the inputs are unchanged. It does not alter data, and it records nothing non-deterministic (no timestamps unless explicitly passed), so the same data always yields the same manifest.
Reference Files
- Manifest schema + workflow:
${CLAUDE_SKILL_DIR}/references/manifest_schema.md— the manifest.json structure, what each drift category means, and the non- deterministic-artifact policy (PPTX/DOCX timestamps). Read before interpreting drift.
Deterministic Script
# Build a manifest (record the analysis seed + provenance)
python "${CLAUDE_SKILL_DIR}/scripts/version_dataset.py" manifest data.csv \
--out manifest.json --seed 42 --provenance "KNHANES 2018 extract v1"
# Verify a later copy against it (CI / pre-analysis gate)
python "${CLAUDE_SKILL_DIR}/scripts/version_dataset.py" verify --manifest manifest.json --strict
# Compare two manifests (what changed between versions)
python "${CLAUDE_SKILL_DIR}/scripts/version_dataset.py" diff --old v1.json --new v2.json
File hashing is stdlib-only; tabular schema/column hashing uses pandas when present.
--ignore-cols excludes volatile columns; --base makes manifest keys relative.
Workflow
Step 1: Lock the version (gate)
Build the manifest at the moment the dataset is frozen for analysis. Gate: confirm with the user the seed and provenance note are correct before locking — the manifest is the record they will cite as "this is the data the results came from."
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.
- 8d ago First seen · 144 lines · 86 tokens per session scan A d6d1a0a67acc
version-dataset is a skill published in the GitHub repository Aperivue/medsci-skills (287 stars, last pushed today), licensed MIT. It adds 86 tokens to every session and 1,477 once invoked, about $0.0004 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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