academic-aio
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Use when you must prove an analysis ran on the intended data or lock a dataset version. Builds a deterministic content-hash manifest (file SHA-256, schema, per-column value hashes), verifies later copies against it for drift, and diffs two manifests.
$ npx -y skills add Aperivue/medsci-skills --skill version-dataset --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/version-datasetContext preview
The summary Claude sees to decide when to auto-load this skill.
Use when you must prove an analysis ran on the intended data or lock a dataset version. Builds a deterministic content-hash manifest (file SHA-256, schema, per-column value hashes), verifies later copies against it for drift, and diffs two manifests.
name: version-dataset description: Use when you must prove an analysis ran on the intended data or lock a dataset version. Builds a deterministic content-hash manifest (file SHA-256, schema, per-column value hashes), verifies later copies against it for drift, and diffs two manifests. metadata: triggers: "version dataset, dataset version, data manifest, data hash, dataset drift, reproducibility lock, verify dataset, data provenance, did my data change, manifest.lock"
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 "${CLAUDE_SKILL_DIR}/scripts/version_dataset.py" diff --old v1.json --new v2.jsonFile hashing is stdlib-only; tabular schema/column hashing uses pandas when present. `--ignore-cols` excludes volatile columns; `--base` makes manifest keys relative.
Build the manifest at the moment the dataset is frozen for analysis. **Gate:** confirm with the user the seed and provenance note are correct before locking — the manifest is the record they will cite as "this is the data the results came from." The provenance note is the user's text; never invent it.
Before re-running an analysis (or in CI), `verify --strict`. Never say a dataset is unchanged without running `verify`. **Gate:** if drift is reported (`MANIFEST_DRIFT`, exit 1), stop and show the user the drift report exactly as computed — never downplay a changed column hash. Do not proceed on changed data without their explicit acknowledgement and a re-lock. Silent re-run on drifted data is the failure this skill exists to prevent.
Read `${CLAUDE_SKILL_DIR}/references/manifest_schema.md` before interpreting drift — it defines each drift category. A CSV/TSV file is compared on its logical content (schema + per-column value hashes), not raw bytes, so re-quoting, reordering columns, or an `--ignore-cols` volatile column does not trip a false drift. Binary tabular files (Parquet/Stata/SAS/Excel) are also checked at the byte level, since labels, metadata and other sheets are not in the column hashes, unless an `--ignore-cols` column is present in the file (its changes alter the bytes).
When a dataset is intentionally updated, `diff` the old and new manifests and present the change set (added/removed/changed columns, row-count delta) so the user can record what changed and re-lock. **Gate:** the user approves the new version before it replaces the locked one.
Do not put PPTX/DOCX or figure binaries under strict byte verification — they embed timestamps or render metadata and change on every build. Manifest only the deterministic inputs and tabular outputs (data files, result CSVs), or use `--ignore-cols` for volatile columns; the policy is in `references/manifest_schema.md`. Each bundled `demo/*/` carries a `manifest.lock.json` (input data
command.
notes may carry context.
`/deidentify`. `/generate-codebook` documents *what* is in the data; this skill locks *which version*.
Created & maintained by Yoojin Nam, MD Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea MedSci Skills is a set of Agent Skills for clinical research: literature
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