anshumanbh-qmdSearch markdown knowledge bases efficiently using qmd. Use this when searching Obsidian vaults or markdown collections to find relevant content with minimal token usage.
Install via ClawdBot CLI:
clawdbot install anshumanbh/anshumanbh-qmdSearch markdown knowledge bases efficiently using qmd, a local indexing tool that uses BM25 + vector embeddings to return only relevant snippets instead of full files.
qmd search "your query" --collection <name>Fast, accurate keyword-based search. Best for specific terms or phrases.
qmd vsearch "your query" --collection <name>Semantic similarity search. Best for conceptual queries where exact words may vary.
qmd hybrid "your query" --collection <name>Combines both approaches with LLM reranking. Most thorough but often overkill.
qmd collection list
# For specific terms
qmd search "api authentication" --collection notes
# For conceptual queries
qmd vsearch "how to handle errors gracefully" --collection notes# Install qmd
bun install -g https://github.com/tobi/qmd
# Add a collection (e.g., Obsidian vault)
qmd collection add ~/path/to/vault --name notes
# Generate embeddings for vector search
qmd embed --collection notes/qmd api authentication # BM25 search for "api authentication"
/qmd how to handle errors --semantic # Vector search for conceptual query
/qmd --setup # Guide through initial setupqmd search) for specific terms, names, or technical keywordsqmd vsearch) when looking for concepts where wording may varyqmd embed after adding significant new content to keep vectors current$ARGUMENTS contains the full search query--semantic flag is present, use qmd vsearch instead of qmd search--setup flag is present, guide user through installation and collection setup--collection is specified, use that collection; otherwise default to checking available collections$ARGUMENTSwhich qmd)Generated Mar 1, 2026
Researchers use QMD Search to quickly find relevant papers and notes across their Obsidian vault, leveraging semantic search to locate concepts even when terminology varies. This reduces time spent manually scanning documents, enabling faster literature synthesis and hypothesis generation.
Developers employ QMD Search to query internal markdown documentation for specific APIs, error handling patterns, or code snippets. The hybrid search ensures both keyword matches and conceptual relevance, speeding up debugging and onboarding processes.
Law firms utilize QMD Search to index case notes and legal precedents in markdown format, allowing quick retrieval of relevant snippets for case preparation. Semantic search helps find related legal concepts across different cases without exact keyword matches.
Content creators use QMD Search to organize and search through markdown drafts, research notes, and idea banks. The token-efficient snippet retrieval aids in repurposing content and maintaining consistency across articles.
Medical professionals index patient notes and treatment protocols in markdown, using QMD Search to find relevant information during consultations. The local, private nature ensures compliance with data privacy regulations while improving access to critical details.
Offer a free tier for individual users with basic search features, and charge teams for advanced analytics, collaboration tools, and priority support. Revenue comes from monthly subscriptions based on the number of users and collections.
Sell customized licenses to large organizations requiring integration with existing knowledge management systems, enhanced security features, and dedicated training. Revenue is generated through annual contracts and implementation fees.
Provide consulting services to help clients set up and optimize QMD Search for specific use cases, along with custom plugin development for niche industries. Revenue streams include hourly rates and project-based fees.
💬 Integration Tip
Integrate QMD Search by first ensuring qmd is installed locally, then indexing markdown collections with 'qmd embed' to enable semantic search for optimal results.
Work with Obsidian vaults (plain Markdown notes) and automate via obsidian-cli.
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Smart ClawdBot documentation access with local search index, cached snippets, and on-demand fetch. Token-efficient and freshness-aware.
Work with Obsidian vaults as a knowledge base. Features: fuzzy/phonetic search across all notes, auto-folder detection for new notes, create/read/edit notes with frontmatter, manage tags and wikilinks. Use when: querying knowledge base, saving notes/documents, editing existing notes by user instructions.