world-model-data-factory

name	world-model-data-factory
description	Plans curation, enrichment, annotation, captioning, augmentation, split policy, and dataset release for world-model training and evaluation.
license	Proprietary
metadata	{"owner":"Chris von Csefalvay (HCLTech)","version":"1.1.0","tags":["data-factory","video","tao","physical-ai"]}

World model data factory

Mission

Use this skill to convert real, simulated, and synthetic observations into model-ready evidence with repeatable quality gates. It owns the decision record for its stage of the data-to-world-model-to-sim2real pipeline and must emit artefacts that downstream agents can validate.

Trigger conditions

Activate this skill when the request involves any of the following:

• Dataset production plan and dataset manifest.
• Inputs such as source manifests, sensor/action schemas, caption and label sources, rights records, simulation output.
• A handoff into or out of this stage of the autonomy world-model workflow.
• A review of whether the current evidence is sufficient to proceed.

Do not use this skill as a generic brainstorming surface. If required records are missing, create the appropriate manifest skeleton and state the missing evidence rather than inventing values.

Required repository tools

Run these tools from the repository root with PYTHONPATH=src or through scripts/wmb:

Tool	Use
scripts/wmb new-manifest --schema schemas/dataset-manifest.schema.json --output <path>	Create the stage manifest contract.
scripts/wmb validate-manifest --schema schemas/dataset-manifest.schema.json --manifest <path>	Validate required fields and basic types.
scripts/wmb readiness-report --root <manifest-dir> --output <report.md>	Summarise evidence completion across manifests.
scripts/wmb skill-audit --root . --output artifacts/skill-audit.json	Confirm skill packaging depth before release.

Skill-local helper:

bash skills/world-model-data-factory/scripts/run-checks.sh <manifest-path>

Required inputs

Before making recommendations, identify and record:

• source manifests.
• sensor/action schemas.
• caption and label sources.
• rights records.
• simulation output.

If the user has not provided a value, mark it as missing evidence in the output. Do not create identifiers, customer names, dataset names, platform names, job ids, or benchmark values.

Preflight

1. Confirm the request is within this skill's authority and does not require an upstream skill first.
2. Locate the relevant manifest, or create a skeleton from schemas/dataset-manifest.schema.json.
3. Run the skill-local check script or scripts/wmb validate-manifest.
4. Classify missing evidence as blocking, non-blocking, or a downstream handoff.
5. Check privacy, rights, safety, and deployment authority before recommending any mutating action.
6. Name the next owner skill for every unresolved workstream.

Operating workflow

1. Partition sources into real, simulated, synthetic, safety-event, and edge-case slices.
2. Define quality gates for corruption, duplication, timing, calibration, captions, labels, and action alignment.
3. Choose enrichment lanes: human review, VLM captioning, TAO annotation, VSS-derived summaries, or Physical AI video augmentation.
4. Protect train, validation, test, stress, and holdout splits before enrichment creates derivative leakage.
5. Emit dataset release evidence for post-training, simulation, and evaluation owners.

Output contract

Return a concise professional artefact containing:

• dataset manifest.
• curation plan.
• split policy.
• augmentation strategy.
• release gate record.

The output must include:

• Manifest path or expected manifest path.
• Evidence accepted from source records.
• Evidence still missing.
• Human review gates.
• Downstream skill handoffs.
• Commands the operator can run to validate or generate the next artefact.

Quality gates

Do not mark the stage ready unless all applicable gates are satisfied:

• Required manifest fields validate against schemas/dataset-manifest.schema.json.
• Source records are authoritative and do not rely on invented values.
• Sensitive data and secrets are referenced by controlled handle only.
• Safety and regulatory context are explicitly carried forward.
• Outputs are suitable for review by engineering, safety, legal, quality, and operations owners.

Stop conditions

Stop and request review when any of these conditions appear:

• train-test leakage.
• weak caption quality.
• missing action semantics.
• synthetic data overwhelming real coverage.
• unresolved consent or license.

Also stop before destructive infrastructure actions, deployment to a real system, model promotion, external publication, or any action involving unapproved customer, medical, regulated, export-controlled, or confidential data.

References to load when needed

• references/operating-playbook.md for detailed stage procedure.
• references/output-contract.md for deliverable structure and evidence tables.
• BENCHMARK.md for evaluation cases and pass criteria.
• evals/benchmark-cases.json for machine-readable skill evaluation cases.