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akf-trust-metadata

The AI native file format. EXIF for AI — stamps every file with trust scores, source provenance, and compliance metadata. Embeds into 20+ formats (DOCX, PDF, images, code). EU AI Act, SOX, HIPAA auditing.

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Dépôt
muhammedadnank/Antigravity-Skills
Dernière activité de la source
6 juin 2026 à 18:30
Langue détectée de SKILL.md
anglais
Étoiles
13
Forks
1

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SKILL.md
Instructions source · Aperçu en lecture seule
name
akf-trust-metadata
description
The AI native file format. EXIF for AI — stamps every file with trust scores, source provenance, and compliance metadata. Embeds into 20+ formats (DOCX, PDF, images, code). EU AI Act, SOX, HIPAA auditing.
risk
safe
source
community
date_added
2026-03-27
author
HMAKT99
tags
["trust","metadata","provenance","compliance","file-format","ai-native"]
# 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 ```bash 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 ```bash akf read <file> # Check existing trust metadata akf inspect <file> # See detailed trust scores ``` ## Compliance auditing ```bash 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 ```bash pip install akf ``` ## Links - https://akf.dev - https://github.com/HMAKT99/AKF - npm: `npm install akf-format` ## 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.
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