Configure qmd search indexes and manage embeddings for vault searching.
npx skills add https://github.com/onebrain-ai/onebrain --skill qmd-onebrain-ai
Please help me install this Agent Skill. Skill: qmd Source: https://github.com/onebrain-ai/onebrain/tree/main/.claude/plugins/onebrain/skills/qmd Command: npx skills add https://github.com/onebrain-ai/onebrain --skill qmd-onebrain-ai
qmd solves the problem of slow vault-wide searching by building and maintaining a local search index that supports fast keyword lookup and optional semantic retrieval.
Use case: You have a large Obsidian vault and your agent needs to reliably find notes across projects and sessions quickly; you set up qmd once, then reindex after major vault changes and optionally generate embeddings to improve relevance.
Ask the assistant to run: /qmd setup.
High-intent search queries and answers about installing and using this skill.
You can achieve semantic search in Obsidian by generating vector embeddings with the qmd embed command after setting up the index. This allows searches to match notes based on semantic meaning rather than relying solely on exact keyword matches.
You should reindex your vault search index after making major vault changes to ensure the search results reflect the latest content. Running a reindex updates the local search index, maintaining fast and accurate vault-wide search retrieval.
To troubleshoot missing or misconfigured vault search collections, use qmd to check the operational health and index status of your vault. This visibility helps identify whether the vault-specific collection in onebrain.yml requires reindexing or embedding updates.
Yes, you can remove the qmd search integration for a specific vault without uninstalling the underlying qmd CLI binary. The lifecycle management feature cleanly uninstalls the vault's search index configuration while leaving the tool installed for other uses.
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