check-ai-transparency

name	check-ai-transparency
description	Review a structured record of AI-generated outputs for provenance evidence, human-readable disclosure, model identification, and documented edits. Use when preparing for publication or policy review and teams need gaps and next actions without a legal conclusion.

Check AI Transparency

Check whether a publication record is ready for human review. This is an evidence checklist, not legal advice or a compliance certification.

All paths below are relative to this skill's directory. If you are running from elsewhere, use an absolute path to scripts/check_transparency.py.

Workflow

1. Copy the schema from references/record-schema.md and describe each output.

2. Run:

   python3 scripts/check_transparency.py /absolute/path/to/record.json
   

3. Resolve each item in gaps or document why it is not applicable.

4. Keep machine-readable provenance and human-readable disclosure as separate controls.

5. Escalate jurisdiction-specific decisions to qualified counsel or the responsible policy owner.

Interpretation

• READY_FOR_REVIEW: nothing is outstanding; it does not mean legally compliant.
• READY_WITH_REVIEW_ITEMS: nothing required is missing, but a human decision is outstanding (for example whether signer trust must be evaluated for this release). Advisory items never block readiness.
• GAPS_FOUND: one or more required evidence or disclosure fields are absent.
• UNKNOWN: the record could not be reliably evaluated.

Read required_gap_count and review_item_count separately; only the first blocks. For Anthropic text, an UNVERIFIABLE detector state is acceptable only when a truthful human-readable disclosure is present and the limitation is retained. Text-bearing formats include PDF, DOCX, ODT, JSON and XML, not only text/*.