bundled-skills/akf-trust-metadata/SKILL.md
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.
npx skillsauth add FrancoStino/opencode-skills-antigravity akf-trust-metadataInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Every photo has EXIF. Every song has ID3. AKF is the native metadata format for AI-generated content.
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.
akf stamp <file> --agent <agent-name> --evidence "<what you did>"
Evidence examples:
akf read <file> # Check existing trust metadata
akf inspect <file> # See detailed trust scores
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
--label confidential for finance/secret/internal paths--label public for README, docs, examplesinternalpip install akf
npm install akf-formattools
Authorized security assessment of LLM applications and AI agents: prompt injection, tool abuse, RAG exposure, memory poisoning, system-prompt extraction, and agent-compliance engineering per OWASP LLM/ASI Top 10.
development
Builds two parameterized UI modes—流光溢彩白 (iridescent white) and 五彩斑斓黑 (colorful black)—with OKLCH, WebGL/CSS fallback, vision gating, screenshot QA, and total/per-color intensity reports. Use when a UI request names either mode or needs measured color parameters.
tools
Delegate coding tasks to the Kimi Code CLI (`kimi`) only when the user explicitly requests it, while the orchestrator retains review and landing responsibility.
development
Front-end JavaScript reverse engineering: locate signature chains, analyze encrypted request parameters, sample runtime behavior, and reproduce logic locally in Node for evidence-based output.