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LLM Wiki

作者 lhuaizhong · GitHub ↗ · v0.1.1 · MIT-0
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在 OpenClaw 中安装
/install lhuaizhong-llm-wiki
功能描述
Build and maintain a local Markdown knowledge wiki that compounds over time instead of relying on one-shot RAG retrieval. Use when the user wants to turn not...
使用说明 (SKILL.md)

LLM Wiki

Overview

Build a local-first, Markdown-based wiki where the human curates sources and asks questions, while the agent maintains the compiled knowledge pages, links, summaries, and indexes.

Prefer this pattern when the goal is cumulative understanding across many sessions, files, and questions — not just answering one query from raw documents.

Core Idea

Treat the wiki as a compiled artifact, not the raw archive.

Use three layers:

  1. Raw sources: immutable inputs such as notes, PDFs, transcripts, copied text, logs, screenshots, and chat exports.
  2. Wiki pages: curated Markdown pages written and maintained by the agent.
  3. Schema/rules: lightweight conventions for page layout, naming, linking, and maintenance.

The human focuses on sourcing, exploration, and direction. The agent handles summarizing, cross-referencing, page updates, consolidation, and bookkeeping.

Recommended Directory Shape

Adapt the exact layout to the user’s workspace, but prefer a structure like:

knowledge/
  raw/
    inbox/
    docs/
    transcripts/
    web/
  wiki/
    concepts/
    people/
    projects/
    timelines/
    sources/
  logs/
    knowledge-log.md
  INDEX.md
  SCHEMA.md

Guidelines:

  • Keep raw/ append-only when practical.
  • Keep wiki/ human-readable and link-heavy.
  • Keep INDEX.md short and navigational.
  • Keep SCHEMA.md focused on conventions, not prose.

Operation Modes

1. Ingest

Use when new material arrives.

Goal: convert raw inputs into durable wiki structure.

Steps:

  1. Inspect new source material.
  2. Decide whether it belongs in raw/ unchanged.
  3. Extract stable entities: concepts, people, projects, decisions, methods, events, open questions.
  4. Create or update wiki pages.
  5. Add backlinks and related-page links.
  6. Update INDEX.md if navigation changed.
  7. Append a concise entry to the knowledge log.

Prefer updating existing pages over creating near-duplicates.

2. Query

Use when the user asks a question about the knowledge base.

Steps:

  1. Search the wiki first.
  2. Read the most relevant compiled pages.
  3. Fall back to raw sources only if the wiki is missing detail.
  4. Answer the question.
  5. If the answer revealed a gap, update the wiki before finishing when appropriate.

Prefer reading 2–5 good pages over sweeping the entire vault.

3. Reindex

Use when the structure has drifted or grown.

Tasks:

  • Refresh INDEX.md
  • Merge overlapping pages
  • Rename pages that no longer fit
  • Repair broken links
  • Normalize tags or page sections
  • Split oversized pages when they cover multiple topics

4. Lint

Use when the wiki may be internally inconsistent.

Check for:

  • duplicate pages for the same concept
  • contradictory claims
  • stale summaries after new evidence
  • orphan pages with no inbound or outbound links
  • pages that lack source pointers
  • vague claims that should be marked as uncertain or disputed

When high-stakes accuracy matters, preserve disagreement explicitly instead of forcing one synthetic summary.

Page Design Rules

Prefer simple Markdown that Obsidian and other editors render well.

For most concept pages, use sections like:

# Topic Name

## Summary
A short stable summary.

## Key points
- ...

## Relationships
- Related: [[Other Page]]
- Depends on: [[Another Page]]

## Sources
- [[Source - Interview 2026-04-06]]
- raw/transcripts/interview-2026-04-06.md

## Open questions
- ...

Rules:

  • Put the most reusable summary near the top.
  • Prefer explicit links like [[Page Name]] when the vault supports wiki links.
  • Separate facts, interpretations, and open questions when ambiguity matters.
  • Record provenance whenever practical.
  • Avoid decorative formatting and long narrative filler.

Indexing and Logging

Maintain two special files:

INDEX.md

Use as a map of the vault.

Include:

  • major sections
  • important hub pages
  • canonical names for key topics
  • pointers to active or recently changed areas

Do not turn it into a giant dump of every page.

logs/knowledge-log.md

Use as a chronological maintenance trail.

Each entry should briefly record:

  • what changed
  • why it changed
  • which pages were added or updated
  • any unresolved ambiguity

Example:

## 2026-04-06
- Ingested interview transcript on home networking setup.
- Updated [[OpenClaw 微信 channel 插件项目]] and created [[网络规划偏好]].
- Flagged uncertainty around deployment target and auth flow.

Workflow Heuristics

  • Prefer modifying a small number of relevant pages over regenerating the whole wiki.
  • Create new pages when a concept is likely to recur.
  • Merge pages when names differ but the concept is clearly the same.
  • Preserve raw material; do not destroy source context unless the user asks.
  • If the wiki disagrees with a source, update the wiki and mark the change.
  • If evidence is mixed, preserve the disagreement explicitly.
  • Use the wiki as the primary retrieval surface; use raw sources as ground truth.

Obsidian-Friendly Usage

If the user keeps notes in Obsidian:

  • write plain Markdown files
  • preserve wikilinks where possible
  • prefer stable filenames over clever ones
  • keep pages short enough to browse comfortably
  • use hub pages for dense topics instead of giant monoliths

Obsidian is the browsing interface; the agent is the maintainer.

Suggested User Requests That Should Trigger This Skill

Examples:

  • “把这些零散笔记整理成知识库”
  • “帮我做一个 second brain / wiki”
  • “把这些聊天记录沉淀成长期知识”
  • “给这个 Obsidian 仓库设计 ingest/query/lint 流程”
  • “把原始资料编译成可维护的 Markdown wiki”
  • “帮我重构这个知识库结构,减少重复页面”

Quick Start

For a brand-new vault, initialize the scaffold first:

python3 scripts/init_wiki.py /path/to/knowledge

Add --force to overwrite existing scaffold files.

After initialization:

  1. review SCHEMA.md
  2. place or copy source material into raw/
  3. ask the agent to ingest the material into wiki/
  4. use query / reindex / lint as the vault grows

References

Read these bundled references only when needed:

  • references/scaffold.md: example scaffold for SCHEMA.md, INDEX.md, and page conventions
  • references/operations.md: practical checklists for ingest, query, reindex, and lint runs
  • references/publish-notes.md: short external-facing positioning and use-case wording

Bundled Script

  • scripts/init_wiki.py: create a starter wiki vault with directories, hub pages, INDEX.md, SCHEMA.md, and logs/knowledge-log.md
安全使用建议
This skill appears coherent and limited to local wiki maintenance, but take these precautions before installing or running: (1) review the init_wiki.py script (it only writes a scaffold) and SKILL.md for behavior you accept; (2) run the init script in a test directory first (do not point it at your home directory or other sensitive paths); (3) back up any existing notes before using --force; (4) if you grant an agent autonomy to run this skill, restrict its filesystem scope so it can only read/write the intended wiki directory; and (5) if you later combine this skill with other skills that have network access or credentials, be aware an agent could transmit wiki contents — consider least privilege for networked skills. If you want higher assurance, run the scaffold and keep the agent's access confined (container or dedicated directory) and inspect any changes before trusting them.
功能分析
Type: OpenClaw Skill Name: lhuaizhong-llm-wiki Version: 0.1.1 The llm-wiki skill bundle is a legitimate tool designed to help an AI agent maintain a local Markdown-based knowledge base. The included Python script (scripts/init_wiki.py) safely initializes a directory structure and template files using standard libraries, and the SKILL.md instructions provide clear, non-malicious guidelines for document ingestion and organization without any signs of prompt injection, data exfiltration, or unauthorized execution.
能力评估
Purpose & Capability
The name and description match the SKILL.md workflow and the included Python scaffold. Required env vars/binaries/config paths are none, and the only code provided (init_wiki.py) creates a local vault scaffold — which is coherent with a wiki/second‑brain purpose.
Instruction Scope
SKILL.md instructs the agent to read, index, update, and maintain files inside a user-provided knowledge vault. This is expected for this purpose, and there are no instructions to call external endpoints or access unrelated system paths. The guidance is somewhat open-ended about what to ingest ("inspect new source material"), so operators should constrain the target directory to avoid accidental exposure of unrelated sensitive files.
Install Mechanism
No install spec (instruction-only) and no network downloads. The included script is a small local Python scaffold that writes Markdown files and directories; it does not fetch or execute remote code.
Credentials
The skill declares no environment variables, credentials, or config paths. Its runtime behavior (reading and writing files in the vault) is proportionate to its stated goal. There are no requests for unrelated secrets or service tokens.
Persistence & Privilege
always:false and user-invocable are set. The only persistent action is writing scaffold files into the user-specified target directory when the script runs. The skill does not request persistent global privileges or modify other skills/configs.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install lhuaizhong-llm-wiki
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /lhuaizhong-llm-wiki 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.1.1
Add wiki scaffold initializer script, improve quick-start guidance, and refine publish-facing notes.
v0.1.0
Initial release: local-first markdown wiki skill with ingest/query/reindex/lint workflow.
元数据
Slug lhuaizhong-llm-wiki
版本 0.1.1
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 2
常见问题

LLM Wiki 是什么?

Build and maintain a local Markdown knowledge wiki that compounds over time instead of relying on one-shot RAG retrieval. Use when the user wants to turn not... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 125 次。

如何安装 LLM Wiki?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install lhuaizhong-llm-wiki」即可一键安装,无需额外配置。

LLM Wiki 是免费的吗?

是的,LLM Wiki 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

LLM Wiki 支持哪些平台?

LLM Wiki 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 LLM Wiki?

由 lhuaizhong(@lhuaizhong)开发并维护,当前版本 v0.1.1。

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