Token导航 LogoToken导航TokenDH.com
研究检索敏感数据clawhub未标认证来源可访问clear审计通过

obsidian-librarianObsidian librarian 搜索

Agent Skill

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。

总安装

3,757

周安装

152

GitHub Stars

1

下载量

1,180
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-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

简介

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。

SKILL.md

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.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

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

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills