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Audit ML release readiness and reproducibility claims against provenance, privacy, licensing, and release gates.
Independently audit ML release and reproducibility claims when a user asks whether a model, dataset, run, artifact, or deployment is ready; reconcile provenance, hashes, privacy, licenses, and gates with PASS, FAIL, or ABSTAIN.
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Original instructions from the publisher’s SKILL.md
# Independent release audit Require requirements lock, data audit, validation report, research claims, model revisions, run and tracking records, artifact hashes, privacy/licensing evidence, deliverable schema, promotion state, and immutable manifests. ## Workflow 1. Build an evidence index with exact paths, URLs, revisions, hashes, timestamps, and owners. 2. Reconcile every requirement, claim, dataset split, model/tokenizer, environment, metric, artifact, and approval against measured evidence. 3. Check reproducibility, leakage, privacy, license, schema, provenance, and promotion gates; label measured, derived, unknown, and contradicted values. 4. Review release target and rollback state independently; do not accept the experimenter's or deployer's self-approval. 5. Return exactly `PASS`, `FAIL`, or `ABSTAIN`, blocking evidence, remediation, and next owner: `ml-stack-deployment` only after PASS, otherwise the named upstream owner. ## Boundary and outputs Never fill missing evidence with assumptions, mutate artifacts, or approve an unobserved operation. `ABSTAIN` means evidence is insufficient; `FAIL` means a known gate is violated. Return an audit report with exact paths and hashes. Next owner is `ml-stack-deployment` after PASS, `ml-stack-experiment` for reproducibility gaps, `ml-stack-data` for leakage/privacy gaps, or `ml-stack-hub` for metadata/licensing gaps. See [audit report](references/audit-report.md), [claims and provenance](references/claims-and-provenance.md), [release gates](references/release-gates.md), and [privacy and licensing](references/privacy-and-licensing.md).