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Agent Skill · 研究检索

obsidian-librarian(Obsidian librarian 搜索)

obsidian-librarian 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。

obsidian-librarian 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 25,477。

总安装 3,757周安装 152GitHub Stars 1下载 1,180核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
0.2.7

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-librarian(Obsidian librarian 搜索)
来源仓库:https://github.com/shahalay007/obsidian-librarian
安装命令:
openclaw skills install obsidian-librarian
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-librarian

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

将 URL、推文等内容转为结构化 Markdown 存入 Obsidian。适合构建个人知识图谱与第二大脑系统。使用 openclaw skills install obsidian-librarian 安装。需确认内容清洗逻辑是否符合隐私要求。建议自定义分类规则以匹配工作流。

obsidian-librarian 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install obsidian-librarian;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

obsidian-librarian 怎么安装?

在终端执行 openclaw skills install obsidian-librarian。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

obsidian-librarian 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

obsidian-librarian 需要什么权限?

权限级别是接触敏感数据。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。

obsidian-librarian 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

obsidian-librarian 用的人多吗?

公开数据是累计安装 3,757 次,近一周安装 152 次,下载 1,180 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

obsidian-librarian 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
obsidian-librarian

Obsidian librarian 搜索 · 本页

3,7571接触敏感数据MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
obsidian-librarian
description
Obsidian second-brain and knowledge-base skill. Save any URL, article, tweet, or X post to your Obsidian vault as clean, categorized, wikilinked markdown. Two-pass Gemini pipeline handles structure, tags, and categories. Ask your whole vault anything with RAG, backed by a local JSON index or Supabase pgvector. URL fetch via Apify. Triggers on "save this", "save it", "save this url", research capture, vault search, or querying saved notes.
version
0.2.7
metadata
{"openclaw":{"primaryEnv":"GEMINI_API_KEY","requires":{"env":["GEMINI_API_KEY","OBSIDIAN_VAULT_PATH"],"bins":["python3","curl"]},"homepage":"https://github.com/openclaw/obsidian-librarian"}}

Obsidian Librarian

A second brain for Obsidian, on autopilot. Drop any URL, article, tweet, X post, or pasted text into OpenClaw and it lands in your vault as a clean, categorized, wikilinked markdown note. Then ask your whole vault anything and get grounded answers with citations.

Use this skill when the user wants OpenClaw to store text or a URL in the Obsidian vault as a cleaned, categorized markdown note, or to search and query notes they have already saved.

Trigger shortcuts:

  • Treat save this, save it, save this url, and save this link as Obsidian-librarian requests when the same message contains a URL, pasted text, or quoted content to preserve.
  • Treat short follow-ups like save it as Obsidian-librarian requests when the immediately preceding user message provided the text or URL to store.
  • Treat phrases like search my notes, search my vault, search Obsidian, what do my notes say about ..., ask my vault, and query my saved notes as Obsidian-librarian requests that should run the RAG ask path.
  • If the message only says save this or save it with no actual content or URL available in context, do not guess; ask what should be saved.
  • If the intent is ambiguous between saving to the local filesystem versus saving to the knowledge vault, prefer the Obsidian vault when the content looks like a note, article, research snippet, or social post.

The vault is mounted in the container at /data/.openclaw/obsidian-vault. Raw inputs are staged in /data/.openclaw/obsidian-vault/_Inbox, then processed into category folders.

Environment

Required:

  • GEMINI_API_KEY: Gemini API key used for both ingest and RAG answer generation.
  • OBSIDIAN_VAULT_PATH: Absolute path to the mounted Obsidian vault.

Conditional:

  • APIFY_API_KEY: Required for URL ingestion.

Optional:

  • OBSIDIAN_INBOX_FOLDER: Override the inbox folder name. Default: _Inbox.
  • OBSIDIAN_GEMINI_MODEL: Primary model override for librarian operations.
  • GEMINI_MODEL: Fallback model name when OBSIDIAN_GEMINI_MODEL is unset.
  • OBSIDIAN_RAG_INDEX_PATH: Override the local JSON RAG index path.
  • SUPABASE_URL: Enable Supabase-backed vector storage.
  • SUPABASE_KEY: Supabase API key for vector storage.
  • EMBEDDING_MODEL: Embedding model override. Default: gemini-embedding-001.
  • EMBEDDING_DIMENSIONS: Embedding size. Default: 384.

URL handling policy:

  • Always use Apify to read the URL first.
  • For x.com / twitter.com post URLs, use the dedicated Apify tweet actor.
  • If an X post contains linked URLs, follow those linked URLs through the same Apify-first path before falling back.
  • If direct URL reading fails, run a web-search fallback and stage the search-result snapshot instead.
  • If both stages fail, surface the full error back to OpenClaw instead of silently swallowing it.

Supported Inputs

  • Pasted text
  • A local text/markdown file
  • A blog/article URL
  • An existing file already sitting in _Inbox
  • A natural-language question about the saved vault

Workflow

  1. Stage the raw source in _Inbox/.
  2. Run Gemini pass 1 to clean and structure it into markdown.
  3. Run Gemini pass 2 to choose category, tags, source attribution, and candidate wikilinks.
  4. Scan existing vault notes for titles and aliases to resolve [[wikilinks]].
  5. Write the final note with YAML frontmatter into the chosen category folder.
  6. Delete the _Inbox file only after the final note is written successfully.

Ingest From Text File

python3 {baseDir}/scripts/run_pipeline.py ingest --text-file /data/.openclaw/workspace/input.txt

Ingest From URL

python3 {baseDir}/scripts/run_pipeline.py ingest --url "https://example.com/article"

Ingest An Existing Inbox File

python3 {baseDir}/scripts/run_pipeline.py ingest --inbox-file /data/.openclaw/obsidian-vault/_Inbox/some-file.md

Ask The Vault (RAG)

python3 {baseDir}/scripts/run_pipeline.py --vault-path /data/.openclaw/obsidian-vault ask "What do my notes say about AI agents?" --print-json

Optional flags: --category <Category>, --threshold <float> (default 0.65), --limit <N> (default 5).

Reindex The Vault

python3 {baseDir}/scripts/run_pipeline.py --vault-path /data/.openclaw/obsidian-vault reindex

Add --file <path> to re-embed a single note instead of the full vault.

Notes

  • For long pasted text, prefer writing it to a temp file under /data/.openclaw/workspace/ and using ingest --text-file.
  • Use --title "Custom Title" on ingest for an explicit note title override.
  • Use --keep-inbox only when debugging. Normal behavior is to clean up the staged source after success.
  • X status URLs preserve deterministic post metadata and captured post content instead of relying on a generic article-style rewrite.
  • The pipeline does forward-linking only in v1. Existing notes are not modified.
  • URL ingestion requires APIFY_API_KEY in the container environment.
  • RAG indexing runs after successful ingests. By default it uses a local JSON index; set SUPABASE_URL and SUPABASE_KEY to use Supabase pgvector instead (requires EMBEDDING_DIMENSIONS=384 to match sql/vault_chunks.sql).
  • SUPABASE_URL must point at a Supabase-compatible API surface. All requests are issued against /rest/v1/..., so self-hosted PostgREST needs a gateway or reverse proxy that serves that prefix.
  • Before enabling Supabase, apply sql/vault_chunks.sql to the target database. It provisions the vault_chunks table, the HNSW index, and the match_vault_chunks RPC that the ask command calls.

平台分布

OpenClaw

71.04%
按下载量换算838

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

接触敏感数据

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

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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