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Dataset version control for research reproducibility. Builds a deterministic content-hash manifest of a dataset (file SHA-256 + tabular schema + per-column value hashes), verifies a later copy against it to detect drift (schema change, row-count change, value changes), and diffs two manifests. Use to prove an analysis ran on the intended data, lock a dataset version, or reproducibility-lock bundled demos.
Passed 20 of 22 test cases with the skill, and 10 of 22 without it.
and 13 more in the full report
12 fixed11 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
A newer attempt on gemini-3.6-flash on 10/3/2026 did not produce a publishable comparison (not comparable). The snapshot above is the previously measured run.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
I am writing a command to generate a lockfile `genomics_manifest.json` from `variants.vcf`. What CLI option specifies the output manifest destination path?
| Checked | Without skill | With skill |
|---|---|---|
| Uses the out argument to specify the manifest file destination. | Missed | Passed |
The instructions your model reads when the skill loads.
An analysis must run on the data it claims to, with a fixed seed. This skill makes drift between runs loud instead of silent: it records a deterministic fingerprint (file SHA-256 plus, for tabular files, schema and per-column value hashes; no timestamp unless explicitly passed) so a later run can prove the inputs are unchanged. It never alters data, and manifests hold hashes, not the data itself. Provenance notes are in English.
# 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.
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." The provenance note is the user's text; never invent it.
Before re-running an analysis (or in CI), verify --strict. Never say a dataset is unchanged
without running verify. Gate: if drift is reported (MANIFEST_DRIFT, exit 1), stop and
show the user the drift report exactly as computed — never downplay a changed column hash. Do not
proceed on changed data without their explicit acknowledgement and a re-lock. Silent re-run on
test cases passed, vs 10 / 22 without
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