Search local Canvas Notebook workspace files using BM25 with vector fallback.
npx skills add https://github.com/canvascoding/canvas-notebook --skill qmd-canvascoding
Please help me install this Agent Skill. Skill: qmd Source: https://github.com/canvascoding/canvas-notebook/tree/main/seed_skills/qmd Command: npx skills add https://github.com/canvascoding/canvas-notebook --skill qmd-canvascoding
Local workspace search across Canvas Notebook to quickly locate notes, files, and documents, including derived content from scanned or converted sources.
Ask the AI to search the workspace for a term and surface matching notes and documents.
High-intent search queries and answers about installing and using this skill.
You can search for notes and documents across a local workspace by applying BM25 text-based search by default, with vector search fallback as needed to surface matching files across multiple projects and file types.
Finding related notes across team folders requires a workspace search tool that supports derived content extraction, allowing you to quickly locate specific documents and surface text from scanned or converted sources.
Yes, workspace search requires a runtime environment satisfying Bun >= 1.0.0 and SQLite3 to execute local text searches and manage the underlying workspace data effectively.
Yes, searching for derived content extracted from scanned or converted documents is supported, allowing you to surface and locate files that originate from non-text sources across your projects.
BM25 is used by default for locating workspace files because it provides fast, safe text-based search results, while vector search serves as an automatic fallback when query complexity requires semantic matching.
Yes, qmd supports searching both workspace-text and workspace-derived sources, ensuring comprehensive coverage when locating notes and files across your local Canvas Notebook environment.
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