Knowledge Index Skill

knowledge_index_skill

Skill for using the KnowledgeIndex registry to discover which directories contain .knowledge.yaml files without walking the filesystem. KnowledgeIndex is a lightweight SQLite cache. It maps directory paths to file metadata (mtime, memory_count, link_count). The database path is caller-provided — npcpy does not hardcode a default. Key operations: - Upsert a directory after writing to its YAML: upsert_directory(db_path, directory, memory_count, link_count)

• List known directories: get_known_directories(db_path, min_mtime=None) - Full rescan of a tree: scan_root(db_path, root, max_depth=5) - Remove a stale directory: remove_directory(db_path, directory) Typical flow: 1. Call scan_root(db_path, root="/home/user/projects", max_depth=5) to populate the index with every .knowledge.yaml found.

2. Query get_known_directories(db_path) to get a list of directories with memory/link counts.
3. For each directory of interest, instantiate KnowledgeStore(directory) and call load() or build_context().

The index is a cache, not the source of truth. If a .knowledge.yaml is deleted or modified outside the app, re-run scan_root to refresh.

Inputs

• name (default: 'action')
• description (default: 'scan | list | upsert | remove')
• name (default: 'db_path')
• description (default: 'Path to the knowledge index SQLite file')
• name (default: 'root_or_directory')
• description (default: 'Root to scan or specific directory to upsert/remove')

Steps

• instruct → instruct.py

Usage

/run_jinx jinx_ref=knowledge_index_skill input_values={"name": "root_or_directory", "description": "Root to scan or specific directory to upsert/remove"}