rankbrain obsidian semantic search

    2

    Semantic search for Obsidian vault using RankBrain-style TF-IDF + graph scoring for Claude Code agents

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    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more

    rankbrain obsidian semantic search

    Example session with this skill installed

    obsearch "playwright bypass bot detection"

    • Read your context and instructions
    • Compiled the rankbrain obsidian semantic
    1. playwright_human_bypass_pattern.md (0.94)
    2. browser_stealth_methods.md (0.89)
    3. cloudflare_waf_workaround.md (0.82)

    Reflecting on these 3 notes will save you 2 hours of re-researching stealth headers.

    1. playwright_human_bypass_pattern.md

    Connects securely to your tools. The creator never sees your data.

    What you get

    Surface past solutions before starting new technical tasksDetect orphaned notes and get intelligent link recommendationsIdentify pillar pages using graph centrality metricsAutomate vault auditing without external API costs

    About this skill

    The problem

    Static knowledge bases often become "write-only" archives where past solutions are forgotten and reinvented. Builders waste time researching technical hurdles or process steps they already solved months ago because keyword search fails to surface conceptually related notes.

    What it does

    • Performs hybrid semantic searches using TF-IDF, graph distance, and node centrality.
    • Surfaces "orphaned" notes with zero links and suggests relevant connections to build a denser knowledge graph.
    • Identifies "pillar pages" or Map of Content (MOC) candidates by calculating network centrality.
    • Operates entirely locally via CLI without external API dependencies or subscription costs.

    Frameworks & tools

    Python 3.8+, Obsidian, TF-IDF, NetworkX for graph analysis. Works as a CLI tool integrated with your local vault.

    Why this beats prompting it yourself

    Building a hybrid search engine that combines text similarity with graph theory requires significant boilerplate for parsing markdown links and calculating network metrics. This skill provides a battle-tested ranking formula (60% TF-IDF, 30% Proximity, 10% Centrality) out of the box, optimized for the RankBrain "search gate" workflow.

    Use cases

    • Pre-task research: Pull up past successful patterns and failure logs before starting a new dev ticket.
    • Vault maintenance: Finding and linking disconnected research notes to improve discoverability.
    • Strategy mapping: Identifying high-impact hub notes to consolidate into structured Map of Content pages.

    Known limitations

    The included TF-IDF version is optimized for vaults under 1,000 notes. For databases exceeding 10,000 notes, localized embedding-based methods are recommended.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

      Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.

    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

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    Recently published to Agensi

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    Security scanned

    Verified clean 2 months ago

    • Passed all security checks, Safe to install

    Listed2 months ago

    What's inside

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