qmd
Skill17 starsChanged 4 months agoMITView on GitHub
Local hybrid search for markdown notes and docs. Use when searching notes, finding related content, or retrieving documents from indexed collections.
23 other projects use a version of this, 4 of them edited.
Adopted by
Most recently taken up by
- tristanphvn/keeltheir own version, first seen Nov 8, 2026
- droxey/skillstheir own version, first seen Nov 8, 2026
- Liang-MH/skillsedited, first seen Nov 8, 2026
- compozy/compozytheir own version, first seen Nov 7, 2026
- pankaj-mahaur/Agent-Daemontheir own version, first seen Nov 6, 2026
- gabrielkoerich/skillsedited, first seen Nov 1, 2026
Used with
Projects with it also use defuddle, obsidian-bases and 5 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
HK-hubSkilldefuddle
Extract clean markdown content from web pages using Defuddle CLI, removing clutter and navigation to save tokens. Use instead of WebFetch when the user provides a URL to read or analyze, for online documentation, articles, blog posts, or any standard web page.
Also in 4 projects that use this
wangzhuofengSkillobsidian-bases
Create and edit Obsidian Bases (.base files) with views, filters, formulas, and summaries. Use when working with .base files, creating database-like views of notes, or when the user mentions Bases, table views, card views, filters, or formulas in Obsidian.
Also in 4 projects that use this
jykimSkillobsidian-cli
Interact with Obsidian using the official CLI to read, search, create, and manage notes, daily notes, tasks, properties, and more. Use when the user wants to query or manipulate vault content via the obsidian command.
Also in 4 projects that use this
wangzhuofengSkilljson-canvas
Create and edit JSON Canvas files (.canvas) with nodes, edges, groups, and connections. Use when working with .canvas files, creating visual canvases, mind maps, flowcharts, or when the user mentions Canvas files in Obsidian.
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VercelSkillvercel-react-best-practices
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
Also in 3 projects that use this
GoogleSkillgrill-me
A relentless interview to sharpen a plan or design.
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- +1 more from wangzhuofeng
Other versions23 projects use a version of this.
Word for word · 0
None found.
Edited · 4
- levineam/qmd-skill★ 70338% same
- gabrielkoerich/skills★ 812% same
- libevm/agent-skills★ 238% same
- Liang-MH/skills★ 138% same
Wrote their own version · 19
- tobi/qmd★ 30ksame name
- breferrari/obsidian-mind★ 5.4ksame name
- compozy/compozy★ 2.9ksame name
- pedronauck/skills★ 649same name
- SAP/e-mobility-charging-stations-simulator★ 232same name
- benoror/obsidianos_work★ 166same name
- iagocavalcante/claude-turbo-search★ 16same name
- superuser-pal/awesome-second-brain★ 14same name
- docxology/codomyrmex★ 10same name
- changeflowhq/skills★ 9same name
9 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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The file
Show its rules and text
We split this skill into the rules it gives an agent
13 rules. 0 are common, 2 are distinctive, and 11 are very specific to this project.
0
Common
Most of the 22 edited versions we read keep these.
2
Distinctive
This author's own choices. Worth a look.
11
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 19
Prefer qmd search (BM25). It's typically instant and should be the default.
- Very specific: line 20
Use qmd vsearch only when keyword search fails and you need semantic similarity (can be very slow on a cold start).
- Very specific: line 21
Avoid qmd query unless the user explicitly wants the highest quality hybrid results and can tolerate long runtimes/timeouts.
- Very specific: line 90
Use specific keywords: qmd search "Python async context managers" not qmd search "Python"
- Very specific: line 92
Use quotes for exact phrases: qmd search "exact phrase match"
- Very specific: line 93
Check result scores: qmd search "query" --all --min-score 0.5 to filter low-confidence matches
- Distinctive: line 113
Problem: Search doesn't find files because the collection mask is too narrow or collection path is wrong.
- Very specific: line 134
Avoid qmd query on large vaults (LLM reranking is too slow)
- Very specific: line 135
Use qmd search instead (BM25 is fast even on huge collections)
- Very specific: line 136
If semantic search needed, use qmd vsearch with -n 5 to limit results
- Distinctive: line 141
If vault exceeds 5000 files, consider splitting into multiple collections
- Very specific: line 167
Use qmd query if available (adds LLM reranking), but only if you can tolerate slow runtime
- Very specific: line 168
Use -n 3 to get fewer, higher-confidence matches from vector search
“Common” is measured: kept by at least 6 in 10 of the 22 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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 vsearchonly when keyword search fails and you need semantic similarity (can be very slow on a cold start). - Avoid
qmd queryunless the user explicitly wants the highest quality hybrid results and can tolerate long runtimes/timeouts.
Search modes
| Mode | Command | Speed | Use case |
| BM25 (default) | qmd search | Instant | Keyword matching |
| Vector | qmd vsearch | ~1 min cold | Semantic similarity |
| Hybrid | qmd query | Slowest | LLM reranking (skip unless requested) |
Common commands
qmd search "query" # default - fast keyword search
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 0.3Useful 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 documents
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" --jsonMaintenance
qmd status # Index health
qmd update # Re-index changed files
qmd embed # Update embeddingsSetup (if not installed)
# Install
bun install -g https://github.com/tobi/qmd
# Create collection
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 searchPerformance notes
qmd searchis typically instantqmd vsearchcan be ~1 minute on cold start (loads local LLM for query expansion)qmd queryadds LLM reranking on top ofvsearch, even slower
Common Pitfalls
Search query too vague (irrelevant results)
Problem: Broad queries like "notes" or "project" return hundreds of low-relevance matches.
Solution:
- Use specific keywords:
qmd search "Python async context managers"notqmd search "Python" - Combine keywords:
qmd search "deadline March quarterly review" - Use quotes for exact phrases:
qmd search "exact phrase match" - Check result scores:
qmd search "query" --all --min-score 0.5to filter low-confidence matches
Index not updated (missing recent files)
Problem: Newly created or recently modified files don't appear in search results.
Solution:
# Check index status
qmd status
# Re-index all changed files
qmd update
# Full re-index if update doesn't help
qmd collection remove <name>
qmd collection add /path/to/notes --name <name> --mask "**/*.md"
qmd embed # if vector search enabledPath filters wrong (excluding relevant directories)
Problem: Search doesn't find files because the collection mask is too narrow or collection path is wrong.
Solution:
# List current collections
qmd status
# Verify collection path matches your notes location
# If path changed, re-add collection:
qmd collection remove <old-name>
qmd collection add /path/to/notes --name <name> --mask "**/*.md"
# Common issue: subdirectories excluded by mask
# If notes are in journals/2025/*, use: --mask "**/*.md"
# Not: --mask "*.md" (which only matches top level)Search timeout on large vaults (performance issues)
Problem: qmd query or qmd vsearch hangs or times out on large knowledge bases (1000+ files).
Solution:
- Avoid
qmd queryon large vaults (LLM reranking is too slow) - Use
qmd searchinstead (BM25 is fast even on huge collections) - If semantic search needed, use
qmd vsearchwith-n 5to limit results - Restrict search scope with
-c <collection>to search one collection instead of all:qmd search "query" -c notes -n 5 # faster than searching all collections - If vault exceeds 5000 files, consider splitting into multiple collections
Missing configuration (qmd not initialized)
Problem: qmd status shows "No collections" or qmd search returns no results.
Solution:
# Install qmd if missing
bun install -g https://github.com/tobi/qmd
# Set up a collection
qmd collection add /path/to/notes --name notes --mask "**/*.md"
# Enable vector search (optional but recommended)
qmd embed
# Verify setup
qmd status # Should show your collection with file countSemantic search returns irrelevant results (vsearch/query)
Problem: Vector search matches by topic but misses what you actually wanted.
Solution:
- Fall back to
qmd search(BM25 keyword matching is more precise for specific terms) - Rephrase query to include actual keywords: not "What are my goals?" but "goals quarterly review objectives"
- Use
qmd queryif available (adds LLM reranking), but only if you can tolerate slow runtime - Use
-n 3to get fewer, higher-confidence matches from vector search
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