Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Productivity
Qmd
Local hybrid search for markdown notes and docs. Use when searching notes, finding related content, or retrieving documents from indexed collections.
skill openclawclawhub Free
Local hybrid search for markdown notes and docs. Use when searching notes, finding related content, or retrieving documents from indexed collections.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- aryannate
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
qmd - Quick Markdown Search
Local search engine for Markdown notes, docs, and knowledge bases. Index once, search fast.
When to use (trigger phrases)
"search my notes / docs / knowledge base" "find related notes" "retrieve a markdown document from my collection" "search local markdown files"
Default behavior (important)
Prefer qmd search (BM25). It's typically instant and should be the default. Use qmd vsearch only when keyword search fails and you need semantic similarity (can be very slow on a cold start). Avoid qmd query unless the user explicitly wants the highest quality hybrid results and can tolerate long runtimes/timeouts.
Prerequisites
Bun >= 1.0.0 macOS: brew install sqlite (SQLite extensions) Ensure PATH includes: $HOME/.bun/bin Install Bun (macOS): brew install oven-sh/bun/bun
Install
bun install -g https://github.com/tobi/qmd
Setup
qmd collection add /path/to/notes --name notes --mask "**/*.md" qmd context add qmd://notes "Description of this collection" # optional qmd embed # one-time to enable vector + hybrid search
What it indexes
Intended for Markdown collections (commonly **/*.md). In our testing, "messy" Markdown is fine: chunking is content-based (roughly a few hundred tokens per chunk), not strict heading/structure based. Not a replacement for code search; use code search tools for repositories/source trees.
Search modes
qmd search (default): fast keyword match (BM25) qmd vsearch (last resort): semantic similarity (vector). Often slow due to local LLM work before the vector lookup. qmd query (generally skip): hybrid search + LLM reranking. Often slower than vsearch and may timeout.
Performance notes
qmd search is typically instant. qmd vsearch can be ~1 minute on some machines because query expansion may load a local model (e.g., Qwen3-1.7B) into memory per run; the vector lookup itself is usually fast. qmd query adds LLM reranking on top of vsearch, so it can be even slower and less reliable for interactive use. If you need repeated semantic searches, consider keeping the process/model warm (e.g., a long-lived qmd/MCP server mode if available in your setup) rather than invoking a cold-start LLM each time.
Common commands
qmd search "query" # default qmd vsearch "query" qmd query "query" qmd search "query" -c notes # Search specific collection qmd search "query" -n 10 # More results qmd search "query" --json # JSON output qmd search "query" --all --files --min-score 0.3
Useful options
-n <num>: number of results -c, --collection <name>: restrict to a collection --all --min-score <num>: return all matches above a threshold --json / --files: agent-friendly output formats --full: return full document content
Retrieve
qmd get "path/to/file.md" # Full document qmd get "#docid" # By ID from search results qmd multi-get "journals/2025-05*.md" qmd multi-get "doc1.md, doc2.md, #abc123" --json
Maintenance
qmd status # Index health qmd update # Re-index changed files qmd embed # Update embeddings
Keeping the index fresh
Automate indexing so results stay current as you add/edit notes. For keyword search (qmd search), qmd update is usually enough (fast). If you rely on semantic/hybrid search (vsearch/query), you may also want qmd embed, but it can be slow. Example schedules (cron): # Hourly incremental updates (keeps BM25 fresh): 0 * * * * export PATH="$HOME/.bun/bin:$PATH" && qmd update # Optional: nightly embedding refresh (can be slow): 0 5 * * * export PATH="$HOME/.bun/bin:$PATH" && qmd embed If your Clawdbot/agent environment supports a built-in scheduler, you can run the same commands there instead of system cron.
Models and cache
Uses local GGUF models; first run auto-downloads them. Default cache: ~/.cache/qmd/models/ (override with XDG_CACHE_HOME).
Relationship to Clawdbot memory search
qmd searches your local files (notes/docs) that you explicitly index into collections. Clawdbot's memory_search searches agent memory (saved facts/context from prior interactions). Use both: memory_search for "what did we decide/learn before?", qmd for "what's in my notes/docs on disk?".
Category context
Workflow acceleration for inboxes, docs, calendars, planning, and execution loops.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs
- SKILL.md
- README.md