ai-knowledge-harvester

You can consolidate AI documentation by scanning source repositories to extract docs into a private knowledge base. The harvester automatically classifies documents and applies frontmatter taxonomy tags for fast retrieval.

Yes, ad-hoc ingestion allows you to capture pasted AI content and file it directly under a project. The harvester applies automatic document classification and frontmatter tagging to the pasted content.

Frontmatter taxonomy works by automatically classifying harvested markdown files and tagging them with metadata. This ensures documents are organized for fast, searchable retrieval within the private knowledge repository.

Harvesting AI docs is completely non-destructive to original source files. The process preserves source files and stages all changes for your review before committing them to the private knowledge store.

Yes, the harvester can automatically generate or update project READMEs to reflect newly ingested documents. This keeps your knowledge repository documentation synchronized with the latest harvested AI specs and logs.

The best way to organize prompt logs and gap analyses is to harvest them into a centralized private knowledge store. The system unifies these AI docs with automatic classification and frontmatter tagging for fast retrieval.