// Create a versioned training dataset with manifest, fixity, provenance, and archive snapshot
| name | dataset-version |
| description | Create a versioned training dataset with manifest, fixity, provenance, and archive snapshot |
| namespace | training-complete |
| category | publication |
| platforms | ["claude","copilot","cursor","factory","windsurf","warp","codex","opencode","openclaw","hermes"] |
| commandHint | {"argumentHint":"<version> [--seed <n>] [--acknowledge-contamination] [--acknowledge-license-risk]"} |
Create a versioned, reproducible training dataset: validate gates, compute splits and license, generate fixity, record provenance, and snapshot to immutable storage. This is the final pipeline step — a successful run yields a named, fixity-verified dataset version that downstream training, evaluation, or publication can consume deterministically.
Per ADR-022 D6, the YAML manifest is the source of truth; the sibling JSON is a regenerated export. Per ADR-022 D9, effective license is computed most-restrictive-wins across all contributing sources. Per ADR-022 D3, immutable storage is either a Fortemi archive snapshot or an aiwg index snapshot.
<version> (required)Dataset version identifier. MUST be valid CalVer (YYYY.M.PATCH, no leading zeros — e.g., 2026.4.0) or SemVer (MAJOR.MINOR.PATCH). The manifest is written to datasets/<version>.yaml; collision with an existing file is a hard failure (versions are immutable).
--seed <n> (optional)Integer random seed that governs deterministic splits, shuffles, and any sampling. Default: 42. Recorded in the manifest's seed field and in reproduction_recipe — the dataset-reproduce skill requires an exact match.
--split-ratios <train/val/test> (optional)Split ratios expressed as a comma-separated triple summing to 1.0. Default: 0.8,0.1,0.1. Computed split counts are written to split_counts. Test-set human-only enforcement (synthetic_ratio.test == 0.0) still applies unless explicitly overridden in the synthesis config.
--acknowledge-contamination (optional)Override the decontamination-gate lint rule (#843) when decontamination-check reports overlap above threshold. Requires an operator reason (prompted interactively if not provided via --reason). Appends an audit block to manifest.ethical_considerations, .aiwg/activity.log, and the decontamination report.
--acknowledge-license-risk (optional)Override the license-check lint rule (#837) when the computed effective license would block publication (incompatible SPDX combinations, copyleft conflicts, missing attribution chains). Same audit pattern as --acknowledge-contamination.
--storage <fortemi|aiwg-index> (optional)Immutable storage backend. Default: fortemi if a Fortemi archive is configured, else aiwg-index. Recorded in storage_ref per ADR-022 D3.
memory-lint with license-check and decontamination-gate rules against the candidate example set. On ERROR, block publication unless the matching --acknowledge-* flag is present with a recorded reason. On WARN, proceed but surface in the manifest's ethical_considerations.split_counts by applying --split-ratios to the candidate count with the declared --seed (same seed MUST produce same split given same inputs). Enforce synthetic_ratio.test == 0.0 unless explicitly overridden.sources[].license, apply most-restrictive-wins resolution (per #837 and ADR-022 D9), and write to the top-level license field. If resolution is ambiguous (e.g., GPL-2.0-only vs Apache-2.0), escalate per @agentic/code/addons/aiwg-utils/rules/human-authorization.md.synthetic_ratio. Test split is human-only by default (ADR-022 D10); validation typically human-only. Train ratio reflects actual synthesized fraction.integrity-verification (media-curator) to emit datasets/<version>-CHECKSUMS.sha256 covering every example record, config file, and report referenced by the manifest. Path is recorded in fixity_manifest.provenance-create (sdlc-complete) to emit a W3C PROV-O record with the dataset version as prov:Entity and every source (sources[].ref_id), generator config, and upstream skill invocation in the derivation chain. Store at provenance/dataset-<version>.jsonld; record UUID in provenance_record_id.mcp__memory-fortemi__manage_archives) or aiwg index snapshot per ADR-022 D3. Record the resulting snapshot ID in storage_ref (exactly one of fortemi_archive_id or aiwg_index_snapshot_id).datasets/<version>.yaml per the schema at @agentic/code/frameworks/training-complete/schemas/dataset-manifest.yaml. Auto-export the sibling datasets/<version>.json immediately after. The YAML is source of truth; hand-edits to the JSON are forbidden.dataset-version event to .aiwg/activity.log via memory-log-append, including version, snapshot ID, fixity manifest path, and any acknowledgement overrides.Both --acknowledge-contamination and --acknowledge-license-risk follow the same audit pattern:
ethical_considerations naming the rule overridden, the operator, the timestamp, and the recorded reasonmemory-log-append records a dataset-version-override event referencing the version and ruleOverrides do NOT suppress the underlying finding — they record an informed exception. Downstream consumers (model cards, datasheets) can surface these annotations.
| Path | Purpose |
|---|---|
datasets/<version>.yaml | Dataset manifest (source of truth) |
datasets/<version>.json | Auto-exported JSON mirror (regenerated, do not edit) |
datasets/<version>-CHECKSUMS.sha256 | SHA-256 fixity manifest covering every referenced artifact |
provenance/dataset-<version>.jsonld | W3C PROV-O provenance record |
Publication is atomic: if manifest write fails after snapshot is taken, the snapshot is rolled back (Fortemi archive delete or aiwg index snapshot drop) and no files remain in datasets/. Partial writes never leave a half-published version on disk. Any failure is logged as dataset-version-failed via memory-log-append.
On re-run after a failure, a fresh <version> is required — the same version number cannot be retried because the snapshot ID may differ.
@agentic/code/frameworks/media-curator/skills/integrity-verification/SKILL.md@agentic/code/frameworks/sdlc-complete/skills/provenance-create/SKILL.md@agentic/code/addons/semantic-memory/skills/memory-log-append/SKILL.md@agentic/code/addons/semantic-memory/skills/memory-lint/SKILL.md@agentic/code/addons/aiwg-utils/rules/human-authorization.md# Standard publication of a cleanly-gated dataset
dataset-version 2026.4.0
# Seed-deterministic publication with custom split
dataset-version 2026.4.0 --seed 1337 --split-ratios 0.85,0.075,0.075
# Publication with acknowledged contamination override
dataset-version 2026.4.0 --acknowledge-contamination \
--reason "HumanEval overlap limited to 3 docstring comments in 10K corpus; test isolated"
@agentic/code/frameworks/training-complete/schemas/dataset-manifest.yaml — manifest schemaGenerate Datasheet, Model Card, and Data Statement from a dataset manifest
Deterministically rebuild a dataset from its manifest and verify fixity equivalence
End-to-end training dataset pipeline — acquire sources through publication
Detect training-eval overlap against benchmark sets before dataset publication
Generate SFT training examples from raw sources using Self-Instruct / Evol-Instruct / SQuAD / STaR patterns
Convert canonical training examples to Alpaca format for training frameworks