dataset-reproduce

Deterministically rebuild a training dataset from a published manifest and verify that the rebuild matches the original via SHA-256 fixity comparison. Used to validate that a dataset published by dataset-version is genuinely reproducible, and to let third parties reconstruct a dataset from its manifest without access to the original artifacts.

Per the ML Reproducibility Checklist (REF-475), a dataset is "reproducible" only if an independent rebuild from the manifest produces byte-identical fixity hashes. This skill is the verifier.

When to Use

• Self-verification: immediately after dataset-version publishes, rebuild and compare to catch non-determinism before the dataset is used for training
• Third-party reproduction: an external consumer has the manifest and wants to rebuild the dataset from primary sources
• Regression check: when a pipeline config changes, rebuild an older dataset to confirm the change did not break determinism for prior versions
• Drift detection: a published dataset is suspected of drift — rebuild and identify which component (source, config, seed) diverged

Parameters

<manifest-path> (required)

Path to a datasets/<version>.yaml manifest published by dataset-version. The YAML is authoritative — sibling .json exports are ignored by this skill.

--compare-fixity (optional)

Compare rebuilt SHA-256 against the original fixity_manifest. Default: true. Disable only for partial rebuilds where comparison is not meaningful.

--workdir <path> (optional)

Scratch directory for the rebuild. Default: .aiwg/training/reproduce/<version>-<timestamp>/. Must be empty; the skill refuses to overwrite.

Operation

1. Load manifest — parse <manifest-path> as YAML; validate against the schema at @agentic/code/frameworks/training-complete/schemas/dataset-manifest.yaml. Resolve sources[], reproduction_recipe, seed, and split_counts.
2. Version compatibility check — compare manifest's reproduction_recipe.aiwg_version and training_complete_version against the current runtime. On mismatch, emit a WARN — reproducibility across versions is not guaranteed. Proceeding is allowed but flagged in the report.
3. Acquire sources — for each entry in sources[], invoke acquire-training-source using the declared ref_id, license, and format. Fixity of each acquired source is checked against any upstream checksum; mismatch is a hard failure (the source has drifted).
4. Apply pipeline — replay the reproduction_recipe step-for-step: generator_configs via synthetic-data-generator and example-synthesizer; preference_config via preference-generator; filter_thresholds via quality filters; decontamination_thresholds via decontamination-check. Format exports via the declared format_exports adapters.
5. Seed determinism — every stochastic step (split assignment, shuffle, sampling, synthetic prompt sampling) uses the manifest's seed. No new entropy is introduced. The same seed + same inputs + same configs MUST produce the same outputs.
6. Generate fixity — delegate to integrity-verification to emit a fresh SHA-256 manifest over the rebuilt dataset.
7. Compare and report — diff the rebuilt fixity manifest against the original fixity_manifest. Emit per-file match/mismatch with a summary verdict (MATCH, PARTIAL, MISMATCH). Write the report.

Non-Determinism Sources

A mismatch does not always mean a bug. Known non-determinism sources to document in the report:

• Model API drift — synthesis or preference generation that called an external LLM may produce different tokens if the model was updated or quantization changed; pin to the exact model version declared in reproduction_recipe.generator_configs.
• External source drift — a web-scraped or HuggingFace source may have changed since the original publication; fixity of the acquired source catches this at step 3.
• Floating-point non-determinism — embedding generation on GPU is not bit-exact across hardware; quality filters that use embeddings may yield different accept/reject decisions on different GPUs.
• Filesystem ordering — implicit reliance on OS-specific directory iteration order; all pipeline steps MUST sort inputs before processing.
• Timestamp embedding — any field that records created_at in a per-example record will always differ; these are excluded from fixity scope.

The report's "Mismatch Analysis" section classifies each divergence against this list.

Output

reports/reproduce-<version>-<timestamp>.md containing:

• Summary verdict (MATCH / PARTIAL / MISMATCH) with example counts
• Version compatibility block (manifest vs current runtime)
• Per-source acquisition fixity comparison
• Per-split rebuilt vs original fixity
• Mismatch analysis (classified against known non-determinism sources)
• Reproducibility block (seed used, configs loaded, adapter versions)

Delegation

• Source acquisition: acquire-training-source (training-complete)
• Synthesis replay: example-synthesizer, synthetic-data-generator (training-complete)
• Preference replay: preference-generator (training-complete)
• Format exports: format-adapter-alpaca, format-adapter-sharegpt, format-adapter-chatml, format-adapter-jsonl, format-adapter-parquet
• Decontamination check: decontamination-check (training-complete)
• Fixity generation: @agentic/code/frameworks/media-curator/skills/integrity-verification/SKILL.md

Examples

bash
# Self-verify a freshly-published dataset
dataset-reproduce datasets/2026.4.0.yaml

# Reproduce with explicit workdir and no fixity comparison (partial rebuild)
dataset-reproduce datasets/2026.4.0.yaml \
  --workdir /tmp/repro-2026.4.0 \
  --compare-fixity false

References

• REF-475 ML Reproducibility Checklist — authoritative reproducibility criteria
• REF-474 Dataset Versioning and Reproducibility
• ADR-022 — training-complete pipeline architecture
• Issue #844 — dataset-version skill (this skill's counterpart)
• @agentic/code/frameworks/training-complete/schemas/dataset-manifest.yaml — manifest schema and validation rules