qmd

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Search the Canvas Notebook workspace with qmd. Use when the user asks to find files, search workspace content, or locate related notes/documents. Default to mode=search (BM25), use vsearch (vector) only as fallback, avoid query (hybrid) by default as it is expensive. Triggers: find files, search workspace, locate notes, Finde, suche, workspace search, content search, qmd.

canvascoding By canvascoding schedule Updated 5/8/2026

name: qmd description: "Search the Canvas Notebook workspace with qmd. Use when the user asks to find files, search workspace content, or locate related notes/documents. Default to mode=search (BM25), use vsearch (vector) only as fallback, avoid query (hybrid) by default as it is expensive. Triggers: find files, search workspace, locate notes, Finde, suche, workspace search, content search, qmd." compatibility: Requires Bun >= 1.0.0 and SQLite3 allowed-tools: Bash(qmd:*) metadata: version: "1.0" author: canvas-studios

Workspace Search (qmd)

Local workspace search for Canvas Notebook. Searches direct text files via workspace-text and derived document text such as DOCX extracts via workspace-derived.

When to Use

Use this skill when the user requests:

  • "Search my notes"
  • "Find related documents"
  • "Search my workspace"
  • "Search for ... in my workspace"
  • "Where is ..."
  • "Finde ..."

Default Behavior

  • Use the PI tool contract: qmd({ query, mode, limit, collection })
  • Prefer mode=search (BM25) - fast and safe as the default
  • Use mode=vsearch only if keyword search fails and semantic similarity is needed
  • Avoid mode=query unless it was explicitly enabled for this runtime or the user explicitly asks for the expensive path

Parameters

  • query (required): Search query
  • mode: Search mode (search, vsearch, query). Default: search
  • collection: Collection to search. Default: workspace-text + workspace-derived
  • limit: Maximum number of results. Default: 10

Search Modes

  • qmd search (default): Fast keyword search (BM25)
  • qmd vsearch (last resort): Semantic similarity (vector). Often slow due to local LLM before lookup.
  • qmd query (mostly skip): Hybrid search + LLM reranking. Often slower than vsearch.

Examples

Standard search: qmd({ query: "my search term", mode: "search" })

Specific collection: qmd({ query: "search term", collection: "workspace-text" })

More results: qmd({ query: "search term", limit: 10 })

Semantic search (slower): qmd({ query: "conceptually similar content", mode: "vsearch" })

Requirements

  • Bun >= 1.0.0 (automatically installed)
  • SQLite3 (available in container)
Install via CLI
npx skills add https://github.com/canvascoding/canvas-notebook --skill qmd
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