Stamp and scan AI provenance metadata blocks in source files.
npx skills add https://github.com/a3lem/my-claude-plugins --skill frontmatter-a3lem
Please help me install this Agent Skill. Skill: frontmatter Source: https://github.com/a3lem/my-claude-plugins/tree/main/plugins/frontmatter/skills/frontmatter Command: npx skills add https://github.com/a3lem/my-claude-plugins --skill frontmatter-a3lem
This Skill addresses the challenge of tracking AI-generated or AI-assisted code, ensuring clarity on human review status, access controls, and the specific rules or skills that governed AI modifications.
human-reviewed flag to false, prompting a human review. It can also stamp new files with access = "read" to prevent accidental AI writes.Stamp the file src/main.py with human-reviewed = false and access = "read".
High-intent search queries and answers about installing and using this skill.
AI provenance metadata is tracked by stamping source files with a custom comment-based block that records human review status, access permissions, and rule references, ensuring clarity on how AI modified the code.
To mark AI-generated code for human review, stamp the file with a metadata block setting human-reviewed to false. This flag automatically resets whenever an AI agent modifies the file.
Yes, you can enforce read-only access for AI agents by stamping files with the access = "read" metadata field, preventing accidental AI writes on sensitive code.
The best way to scan repository file coverage for AI metadata is using automated scanning to parse custom comment-based provenance blocks across the codebase, reporting which files contain review status and access control stamps.
No specific dependencies are required to manage AI provenance metadata. The Skill uses standalone scripts to stamp and scan custom comment-based blocks directly within source files.
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