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ragclawragclaw 文档

Agent Skill

ragclaw 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

5,983

周安装

257

GitHub Stars

1

下载量

2,097
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:ragclaw(ragclaw 文档)
来源仓库:https://github.com/emdzej/ragclaw
安装命令:
openclaw skills install ragclaw
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 ragclaw

简介

本地优先知识库,支持 SQLite 与嵌入模型的离线检索。

  • 适用于文档管理与网页内容的本地化索引场景。
  • 可辅助构建私有知识图谱与语义搜索功能。
  • 安装命令:openclaw skills install ragclaw。
  • 需确保设备具备足够本地计算资源。ragclaw 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

RagClaw Knowledge Base Skill

Local-first knowledge base for OpenClaw.

Description

Index and search your documents, code, and web pages locally. Zero external APIs, offline embeddings, SQLite-based storage.

Commands

/kb add <source>

Index a file, directory, or URL.

Examples:

/kb add ./docs/
/kb add https://docs.example.com
/kb add ~/projects/my-app/src/
/kb add https://docs.example.com --crawl --crawl-max-depth 2

Options:

  • --db <name> — Knowledge base name (default: "default")
  • --recursive — Recurse into directories (default: true)
  • --embedder <preset> — Embedder preset: nomic|bge|mxbai|minilm (default: nomic)
  • --include <pattern> — Regex filter: include only matching filenames
  • --exclude <pattern> — Regex filter: exclude matching filenames
  • --max-depth <n> — Maximum directory recursion depth
  • --max-files <n> — Maximum number of files to index
  • --crawl — Follow links from a seed URL
  • --crawl-max-depth <n> — Link traversal depth (default: 3)
  • --crawl-max-pages <n> — Max pages to fetch (default: 100)
  • --crawl-same-origin — Stay on the same domain (default: true)
  • --crawl-include <patterns> — Comma-separated URL path prefixes to include
  • --crawl-exclude <patterns> — Comma-separated URL path prefixes to exclude
  • --crawl-concurrency <n> — Concurrent fetchers (default: 1)
  • --crawl-delay <ms> — Delay between requests in ms (default: 1000)
  • --enforce-guards — Enable path/URL security guards

/kb search <query>

Search the knowledge base.

Examples:

/kb search how to configure authentication
/kb search async function error handling
/kb search "memory leak" --mode hybrid --limit 10

Options:

  • --db <name> — Knowledge base name (default: "default")
  • --limit <n> — Max results (default: 5)
  • --mode <mode> — Search mode: vector|keyword|hybrid (default: hybrid)
  • --json — Machine-readable JSON output

/kb reindex

Re-process changed sources and keep vectors up to date.

Options:

  • --db <name> — Knowledge base name (default: "default")
  • -f, --force — Force full rebuild (ignore hashes)
  • -p, --prune — Remove sources that no longer exist on disk
  • --embedder <preset> — Switch embedder and rebuild all vectors

/kb merge <source.sqlite>

Merge another knowledge base into the local one.

Options:

  • --db <name> — Destination knowledge base (default: "default")
  • --strategy <strict|reindex>strict copies vectors verbatim (same embedder required); reindex re-embeds locally (default: strict)
  • --on-conflict <skip|prefer-local|prefer-remote> — Conflict resolution (default: skip)
  • --dry-run — Preview changes without writing
  • --include <paths> — Comma-separated path prefixes to import
  • --exclude <paths> — Comma-separated path prefixes to skip

/kb status

Show knowledge base statistics (chunks, sources, vector backend, embedder).

Options:

  • --db <name> — Knowledge base name (default: "default")

/kb list

List indexed sources.

Options:

  • --db <name> — Knowledge base name (default: "default")
  • -t <file|url> — Filter by source type

/kb remove <source>

Remove a source from the index.

Options:

  • --db <name> — Knowledge base name (default: "default")
  • -y — Skip confirmation prompt

/kb embedder list

List all available embedder presets with RAM requirements and status.

/kb embedder download [preset]

Pre-download a model for offline use.

Options:

  • --all — Download all built-in presets

/kb doctor

Check system health: Node.js version, RAM, sqlite-vec status, embedder compatibility, loaded plugins.

/kb plugin list

List discovered plugins with enabled/disabled status.

/kb plugin enable <name>

Enable a plugin (use --all for all discovered plugins).

/kb plugin disable <name>

Disable a plugin.

/kb config list

Show all configuration values and their source (env / config file / default).

/kb config get <key>

Show a single config value.

/kb config set <key> <value>

Persist a config value to ~/.config/kbclaw/config.yaml.

Supported Formats

TypeExtensions
Markdown.md, .mdx
Text.txt
PDF.pdf (OCR for scanned pages)
Word.docx
Code.ts, .js, .py, .go, .java
Images.png, .jpg, .gif, .webp, .bmp, .tiff (OCR)
Webhttp://, https://

Embedder Presets

AliasModelLanguageContextDims~RAMStrengths
nomicnomic-ai/nomic-embed-text-v1.5English8 192 tok768~600 MBLong docs, balanced, default
bgeBAAI/bge-m3100+ languages8 192 tok1024~2.3 GBMultilingual
mxbaimixedbread-ai/mxbai-embed-large-v1English512 tok1024~1.4 GBBest English MTEB
minilmsentence-transformers/all-MiniLM-L6-v2English256 tok384~90 MBMinimal RAM

Run /kb doctor to check which presets fit your available RAM.

Storage

Knowledge bases are stored as SQLite files following XDG conventions:

  • Default data dir: ~/.local/share/kbclaw/
  • Config file: ~/.config/kbclaw/config.yaml
  • Backwards compat: if ~/.openclaw/kbclaw/ exists it will be used automatically.

How It Works

  1. Extract — Pull text from documents (PDF, DOCX, HTML, code, images via OCR)
  2. Chunk — Split into semantic units (paragraphs, functions, classes)
  3. Embed — Generate vectors using a configurable local model (default: nomic-embed-text-v1.5, 768 dims)
  4. Store — SQLite with FTS5 for keyword search; embedder info written to DB metadata
  5. Search — Hybrid: 70% vector similarity + 30% BM25 keyword; embedder auto-detected from DB

All processing happens locally. No API keys required.

适合场景

01

研究助手

02

事实核查

03

知识库问答

04

带来源的搜索总结

能力概览

能力 1

组合搜索和大模型调用

能力 2

支持多来源检索和总结

能力 3

强调引用来源和事实核查

能力 4

适合研究型 Agent 流程

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

70.04%
按下载量换算1,469

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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

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