agentleFS
ckorhonenckorhonen/claude-skills/skills/qmd/SKILL.md

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

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-hubSkill

    defuddle

    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

  • wangzhuofengSkill

    obsidian-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

  • jykimSkill

    obsidian-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

  • wangzhuofengSkill

    json-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.

    Also in 3 projects that use this

  • VercelSkill

    vercel-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

  • GoogleSkill

    grill-me

    A relentless interview to sharpen a plan or design.

    Also in 3 projects that use this

  • +1 more from wangzhuofeng

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
  1. Very specific:

    Prefer qmd search (BM25). It's typically instant and should be the default.

    line 19
  2. Very specific:

    Use qmd vsearch only when keyword search fails and you need semantic similarity (can be very slow on a cold start).

    line 20
  3. Very specific:

    Avoid qmd query unless the user explicitly wants the highest quality hybrid results and can tolerate long runtimes/timeouts.

    line 21
  4. Very specific:

    Use specific keywords: qmd search "Python async context managers" not qmd search "Python"

    line 90
  5. Very specific:

    Use quotes for exact phrases: qmd search "exact phrase match"

    line 92
  6. Very specific:

    Check result scores: qmd search "query" --all --min-score 0.5 to filter low-confidence matches

    line 93
  7. Distinctive:

    Problem: Search doesn't find files because the collection mask is too narrow or collection path is wrong.

    line 113
  8. Very specific:

    Avoid qmd query on large vaults (LLM reranking is too slow)

    line 134
  9. Very specific:

    Use qmd search instead (BM25 is fast even on huge collections)

    line 135
  10. Very specific:

    If semantic search needed, use qmd vsearch with -n 5 to limit results

    line 136
  11. Distinctive:

    If vault exceeds 5000 files, consider splitting into multiple collections

    line 141
  12. Very specific:

    Use qmd query if available (adds LLM reranking), but only if you can tolerate slow runtime

    line 167
  13. Very specific:

    Use -n 3 to get fewer, higher-confidence matches from vector search

    line 168

“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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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.

Search modes

ModeCommandSpeedUse case
BM25 (default)qmd searchInstantKeyword matching
Vectorqmd vsearch~1 min coldSemantic similarity
Hybridqmd querySlowestLLM 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 0.3

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 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" --json

Maintenance

qmd status                      # Index health
qmd update                      # Re-index changed files
qmd embed                       # Update embeddings

Setup (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 search

Performance notes

  • qmd search is typically instant
  • qmd vsearch can be ~1 minute on cold start (loads local LLM for query expansion)
  • qmd query adds LLM reranking on top of vsearch, 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" not qmd 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 0.5 to 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 enabled

Path 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 query on large vaults (LLM reranking is too slow)
  • Use qmd search instead (BM25 is fast even on huge collections)
  • If semantic search needed, use qmd vsearch with -n 5 to 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 count

Semantic 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 query if available (adds LLM reranking), but only if you can tolerate slow runtime
  • Use -n 3 to get fewer, higher-confidence matches from vector search

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.