Skills Artificial Intelligence AKF: AI Provenance Metadata Stamping

AKF: AI Provenance Metadata Stamping

v20260427
akf-trust-metadata
AKF is the native metadata format specifically designed for tracking AI-generated or AI-modified content. It allows users to stamp files (including DOCX, PDF, images, and code) with detailed trust scores, source provenance, and compliance metadata. This tool is essential for professional workflows requiring strict auditing, compliance assurance, and verifiable data lineage, especially concerning regulations like the EU AI Act, HIPAA, and SOX.
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Overview

AKF — The AI Native File Format

Every photo has EXIF. Every song has ID3. AKF is the native metadata format for AI-generated content.

When to Use

Use this skill when you need to stamp, inspect, or audit provenance and trust metadata on AI-generated or AI-modified files for compliance, review, or handoff workflows.

After creating or modifying files

akf stamp <file> --agent <agent-name> --evidence "<what you did>"

Evidence examples:

  • "generated from user prompt"
  • "refactored existing code"
  • "tests pass"
  • "docs reviewed"

Before modifying existing files

akf read <file>          # Check existing trust metadata
akf inspect <file>       # See detailed trust scores

Compliance auditing

akf audit <file> --regulation eu_ai_act    # EU AI Act Article 50
akf audit <file> --regulation hipaa         # HIPAA
akf audit <file> --regulation sox           # SOX
akf audit <file> --regulation nist_ai       # NIST AI RMF

Classification

  • --label confidential for finance/secret/internal paths
  • --label public for README, docs, examples
  • Default: internal

Install

pip install akf

Links

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
Info
Name akf-trust-metadata
Version v20260427
Size 1.93KB
Updated At 2026-04-28
Language