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/version-dataset

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.

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medsci-skills
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Install
$ npx -y skills add Aperivue/medsci-skills --skill version-dataset --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/version-dataset

Context 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.

SKILL.md

version-dataset.SKILL.md
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"

Version Dataset Skill

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.

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." The provenance note is the user's text; never invent it.

Step 2: Verify before each run (gate)

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).

Step 3: Diff across versions

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.

Non-Deterministic Artifacts

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

  • deterministic result tables) that `verify --strict` checks; the reference gives the demo

command.

Scope

  • Run `/deidentify` before a manifest is shared: example values are not stored, but provenance

notes may carry context.

  • Cleaning, profiling or de-identifying the data → `/clean-data`, `/generate-codebook`,

`/deidentify`. `/generate-codebook` documents *what* is in the data; this skill locks *which version*.

Read more
Ships withmedsci-skills

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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