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obsidian-semantic-searchObsidian semantic 搜索

Agent Skill

obsidian-semantic-search 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

6,830

周安装

279

GitHub Stars

1

下载量

2,187
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:obsidian-semantic-search(Obsidian semantic 搜索)
来源仓库:https://github.com/celstnblacc/obsidian-semantic-search
安装命令:
openclaw skills install obsidian-semantic-search
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install obsidian-semantic-search

简介

使用本地嵌入技术对 Obsidian 金库进行语义搜索,支持混合、语义和关键字检索。

  • 适合在知识库中快速定位相关笔记,提升信息查找效率。
  • 通过 MCP 工具实现文件操作与批量读取,支持多种查询方式组合使用。
  • 安装命令:openclaw skills install obsidian-semantic-search。
  • 需确认 Ollama 和 pgvector 环境配置及文件访问权限。

SKILL.md

name
Obsidian Semantic Search
description
Semantic search across your Obsidian vaults using local embeddings (Ollama + pgvector). 10 MCP tools: hybrid/semantic/keyword search, file CRUD, batch reads, live re-indexing, and a monitoring dashboard. Fully local — no API keys, no cloud, zero cost.
version
1.0.0
emoji
🧠
homepage
https://github.com/celstnblacc/obsidian-semantic-mcp
user-invocable
true
disable-model-invocation
false
requires
bins
["docker", "uv"]
anyBins
["python3", "python"]
env
["OBSIDIAN_VAULT"]

Obsidian Semantic Search

Search your Obsidian vault by meaning, not just keywords. This skill installs and configures obsidian-semantic-mcp — a local-first MCP server that indexes your vault with vector embeddings (Ollama + pgvector) and exposes 10 tools to any MCP-compatible AI assistant.

What You Get

10 MCP Tools

ToolWhat it does
search_vaultSemantic, keyword, or hybrid search with similarity scores
simple_searchFast exact-text search across all files
list_filesBrowse vault directories
get_fileRead a single file
get_files_batchRead multiple files in one call
append_contentAppend text to a file (creates if missing)
write_fileOverwrite a file completely
recent_changesList recently modified files
list_indexed_notesSee all indexed notes with timestamps
reindex_vaultForce a full re-index

Monitoring Dashboard (port 8484)

  • Real-time service health (PostgreSQL, Ollama, embedding model)
  • Indexed notes count, vault coverage %, database size
  • Search testing UI — test queries without leaving your browser
  • Manual re-index trigger

Search Modes

  • Hybrid (default): Combines semantic meaning + keyword matching for best results
  • Semantic: Search by meaning only — finds related content even with different wording
  • Keyword: Exact text matching via PostgreSQL full-text search

Installation

Prerequisites

  • Docker Desktop (running)
  • uv (Python package manager): curl -LsSf https://astral.sh/uv/install.sh | sh
  • An Obsidian vault on your local filesystem

One-Liner Install

bash <(curl -fsSL https://raw.githubusercontent.com/celstnblacc/obsidian-semantic-mcp/main/install.sh) --mode 2 --vault /path/to/your/vault

This clones the repo to ~/.local/share/obsidian-semantic-mcp, installs the osm CLI, and runs the setup wizard in Docker mode.

Manual Install

git clone https://github.com/celstnblacc/obsidian-semantic-mcp.git
cd obsidian-semantic-mcp
uv sync
uv run osm init

The wizard detects your OS and offers setup modes:

macOS (4 modes):

  • Mode 1: Native (Homebrew — no Docker needed)
  • Mode 2: Docker + host Ollama (if Ollama already installed)
  • Mode 3: Full Docker (recommended — everything in containers)
  • Mode 4: Docker + remote Ollama (SSH tunnel to a GPU server)

Linux (3 modes):

  • Mode 1: Docker + host Ollama
  • Mode 2: Full Docker (recommended)
  • Mode 3: Docker + remote Ollama

Verify Installation

osm status

Should show: Docker containers running, Ollama healthy, embedding model loaded, vault indexed.

Register with Claude Desktop

The wizard auto-configures this, but if you need to do it manually:

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or ~/.config/Claude/claude_desktop_config.json (Linux):

{
  "mcpServers": {
    "obsidian-semantic": {
      "command": "docker",
      "args": ["exec", "-i", "obsidian-semantic-mcp-mcp-server-1", "python3", "src/server.py"]
    }
  }
}

Restart Claude Desktop after adding.

Configuration

Set these in .env or as environment variables:

VariableRequiredDefaultNotes
OBSIDIAN_VAULTYesPath to your vault
OBSIDIAN_VAULTSNoComma-separated paths for multi-vault
POSTGRES_PASSWORDYes (Docker)Database password
EMBEDDING_MODELNonomic-embed-textOllama model (~7GB)
RERANK_MODELNoOptional cross-encoder for better ranking
DASHBOARD_PORTNo8484Monitoring dashboard port

Usage Examples

Once installed, your AI assistant can use these tools naturally:

Semantic search:

"Search my vault for notes about authentication patterns" → Uses search_vault with hybrid mode, returns top 5 matches ranked by meaning

Find and read:

"Find my meeting notes from last week and summarize them" → Uses recent_changes to find files, get_files_batch to read them

Append to a note:

"Add this action item to my project-x.md file" → Uses append_content to add text without overwriting

Re-index after bulk changes:

"I just reorganized my vault, re-index everything" → Uses reindex_vault to rebuild the full index

Maintenance

osm status       # Check health
osm rebuild      # Rebuild Docker images after updates
osm remove       # Stop and clean up everything

Architecture

  • PostgreSQL 17 + pgvector: Stores 768-dim vector embeddings, IVFFlat index for fast cosine similarity search
  • Ollama (nomic-embed-text): Local embedding model — no API keys, no cloud calls
  • Watchdog file watcher: Auto-re-indexes when vault files change (0.5s debounce)
  • LRU cache: 256 entries, 10-min TTL — skips redundant Ollama calls for repeated queries

Troubleshooting

ProblemFix
"Ollama not responding"Run osm status — if Ollama is down, the dashboard has a "Start Ollama" button
Slow first searchFirst query loads the model into memory (~5s). Subsequent queries are <1s
Missing notes in searchRun osm status to check indexing coverage. Use dashboard to trigger re-index
Docker errorsEnsure Docker Desktop is running. Try osm rebuild to rebuild images
VirusTotal "suspicious" warning on installThis is a false positive — the skill *describes* searching vault content, which triggers pattern detection. Use clawhub install obsidian-semantic-search --force to proceed. The skill is MIT-0 licensed and contains no executable code beyond documentation.

Links

  • Source: https://github.com/celstnblacc/obsidian-semantic-mcp
  • Changelog: https://github.com/celstnblacc/obsidian-semantic-mcp/blob/main/CHANGELOG.md
  • Architecture: https://github.com/celstnblacc/obsidian-semantic-mcp/blob/main/docs/ARCHITECTURE.md
  • License: Apache 2.0 (source repo) / MIT-0 (this skill)

*Built by celstnblacc — 207 unit tests, Docker + native install, multi-vault support.*

适合场景

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用户想查找某类 Agent Skill 时

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需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

97.67%
按下载量换算2,136

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

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来源信息

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