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wiki-knowledge-base维基知识库

Agent Skill

用于搭建或维护带检索增强的 RAG 工作流,适合让 Agent 处理知识库问答、向量检索、来源引用和事实核查。它可以辅助整理数据接入、Embedding、向量库、召回参数和回答生成流程。使用时需要确认数据来源、更新频率、召回阈值和引用展示方式,避免把未命中的资料或过期内容包装成确定事实。

总安装

2,376

周安装

101

GitHub Stars

公开资料未说明

下载量

832
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:wiki-knowledge-base(维基知识库)
来源仓库:https://github.com/alexfly123lee-creator/wiki-knowledge-base
安装命令:
openclaw skills install wiki-knowledge-base
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 wiki-knowledge-base

简介

wiki-knowledge-base 用于构建和维护基于 Markdown 的本地知识维基,支持 RAG 工作流增强。

  • 适合知识库问答、向量检索、来源引用和事实核查等场景。
  • 支持双链接笔记系统和嵌入模型集成,便于知识组织与检索。
  • 使用时需注意数据来源、更新频率及召回阈值设置,避免误判未命中内容。
  • 适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

SKILL.md

name
wiki-knowledge-base
description
Build and maintain a local Markdown-based knowledge wiki with Obsidian-style double-links. Inspired by Karpathy's "let's build" approach. Use when the user wants to create a personal knowledge base, wiki, or structured information repository from research articles, competitive analysis, or domain knowledge. Triggers on phrases like "build a wiki", "knowledge base", "knowledge graph", "organize research", "wiki maintenance", "wiki lint", or when working in a directory with wiki/concepts/entities structure.

Wiki Knowledge Base

Build a local, Obsidian-compatible knowledge wiki from raw research materials. Uses a concept-entity-comparison-source architecture with double-link ([[slug]]) networking.

Directory Structure

<project-root>/
├── raw/                  # Immutable source materials (read-only)
│   └── articles/         # Web articles, reports (Obsidian Web Clipper → Markdown)
├── wiki/                 # LLM-maintained knowledge pages
│   ├── index.md          # Master directory (update after every operation)
│   ├── log.md            # Append-only operation log
│   ├── concepts/         # Abstract concepts (AI Agent, MCP Protocol, ...)
│   ├── entities/         # Concrete products/companies/tools (Smithery, Cursor, ...)
│   ├── comparisons/      # Cross-entity analysis tables
│   └── sources/          # Structured summaries of raw/ materials
└── outputs/              # Generated reports, lint results

Page Format

Every wiki page requires YAML frontmatter:

---
title: Page Title
type: concept | entity | source-summary | comparison
sources:
  - raw/articles/filename.md
related:
  - "[[related-slug]]"
created: YYYY-MM-DD
updated: YYYY-MM-DD
confidence: high | medium | low
---

Naming Conventions

  • File names: kebab-case (ai-agent.md, mcp-model-context-protocol.md)
  • Double-links: must use slug format [[slug]], never Chinese text or PascalCase
  • Source references: plain text path to raw/ files in frontmatter

Four Page Types

TypePurposeExample
conceptAbstract domain knowledge, definitions, frameworksAI Agent, MCP Protocol, Coding Agent
entitySpecific products, companies, tools with facts/dataSmithery, Cursor, Claude Code
comparisonSide-by-side analysis tablesMCP Platform Comparison
source-summaryStructured summary of a raw article提炼 key findings from raw/

Concept vs Entity: concept = "what is X?" (category), entity = "what is Y specifically?" (instance). This avoids duplication—define once, link everywhere.

Three-layer distillation: raw/ (full articles, 10k+ words) → wiki/sources/ (summaries, ~500 words) → wiki/concepts/ + wiki/entities/ (structured knowledge).

Workflow: Ingest

When new materials arrive in raw/:

  1. Read new files in raw/
  2. Discuss key findings with user
  3. Create wiki/sources/<slug>.md summary with proper frontmatter
  4. Create or update related concept/entity pages, extracting information from the source
  5. Update wiki/index.md with new entries
  6. Append operation to wiki/log.md

Workflow: Query

When answering questions from the wiki:

  1. Read wiki/index.md to locate relevant pages
  2. Read related concept/entity/comparison pages
  3. Synthesize answer using [[slug]] citations
  4. If answer has lasting value, propose saving as a new wiki page

Workflow: Lint

Run health checks periodically (or when asked):

  1. Contradiction detection: Find conflicting claims across pages (e.g., different numbers for same metric)
  2. Orphan detection: Find pages with no inbound [[double-link]] from other pages (index.md doesn't count)
  3. Dangling links: Find [[links]] pointing to non-existent files
  4. Ambiguous links: Find links using Chinese/PascalCase instead of slug format
  5. Missing concepts: Find entities mentioned in text but without their own page
  6. Content quality: Flag pages with confidence: low or thin content (<100 words)
  7. Source coverage: Check that concept/entity pages link back to their source summaries

Fix strategy:

  • Dangling links: sed batch-replace to correct slug format
  • Ambiguous links: replace with correct slug, or remove [] if too generic (e.g., [[AI]] → plain text)
  • Orphan source pages: add [[source-slug]] in corresponding concept/entity page body
  • Contradictions: verify against source pages, unify to source-of-truth data

Save lint report to outputs/lint-YYYY-MM-DD.md.

Workflow: Git

After every operation batch:

git add -A && git commit -m "<type>: <description>"

Commit message format: <type>: <description> where type is ingest, lint, fix, create.

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

85.46%
按下载量换算711

安全审计

VirusTotal

未展示

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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