neeeophytee/ai-watermarks-reality-check · Archived

check-ai-transparency

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.

First seen Aug 13, 2026

Installation

$ npx skills add neeeophytee/ai-watermarks-reality-check --skill check-ai-transparency

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Repository health

Stars 12
License LICENSE
Default branch main
Open issues 0
Status Archived

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,845 B
  • docs SUMMARY.md 292 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 17 installs

SKILL.md

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:

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

  1. Resolve each item in gaps or document why it is not applicable.
  2. Keep machine-readable provenance and human-readable disclosure as separate controls.
  3. Escalate jurisdiction-specific decisions to qualified counsel or the responsible policy owner.

Interpretation

  • READYFORREVIEW: nothing is outstanding; it does not mean legally compliant.
  • READYWITHREVIEW_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 requiredgapcount and reviewitemcount 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/*.