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Agent Skill · 研究检索

llm-wiki(LLM Wiki)

llm-wiki 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。

llm-wiki 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 60,488。

总安装 367周安装 15GitHub Stars 430下载 118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
unknown

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:llm-wiki(LLM Wiki)
来源仓库:https://github.com/lewislulu/llm-wiki-skill
仓库路径:skills/llm-wiki
安装命令:
npx skills add https://github.com/lewislulu/llm-wiki-skill --skill llm-wiki
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/lewislulu/llm-wiki-skill --skill llm-wiki

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。

llm-wiki 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。适用于大语言模型知识库查询、AI 技术文档检索和智能助手训练资料筛选等场景。

通过 npx skills add 命令从指定 GitHub 仓库安装,需确认权限范围和联网需求。建议结合原始 README 核验具体用法,注意维护状态及是否触发文件读写操作。llm-wiki 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/llm-wiki

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/lewislulu/llm-wiki-skill --skill llm-wiki;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

llm-wiki 怎么安装?

在终端执行 npx skills add https://github.com/lewislulu/llm-wiki-skill --skill llm-wiki。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

llm-wiki 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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。

llm-wiki 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

llm-wiki 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

llm-wiki 用的人多吗?

公开数据是累计安装 367 次,近一周安装 15 次,下载 118 次,GitHub 430 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。

llm-wiki 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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 Codex,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。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 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
llm-wiki

LLM Wiki · 本页

367430只读未明确有提醒

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

LLM Wiki — Karpathy Knowledge Base Pattern

Experimental skill — iterating. Authored by Lewis Liu (lylewis@outlook.com) · Inspired by Karpathy's llm-wiki Gist

Core idea

Instead of RAG (re-retrieving raw docs on every query), the LLM compiles raw sources into a persistent, cross-linked wiki. Every ingest, query, lint, and audit pass makes the wiki richer. Knowledge compounds — and the human stays in the loop via a structured feedback channel instead of ad-hoc corrections that get lost.

  • You own: sourcing raw material, asking good questions, steering direction, filing feedback on anything the AI got wrong.
  • LLM owns: all writing, cross-referencing, filing, bookkeeping, and acting on your feedback.

The wiki is a living artifact with five operations — compile, ingest, query, lint, audit. Every session starts by reading CLAUDE.md and wiki/index.md.

Directory layout

<wiki-root>/
├── CLAUDE.md          ← Schema: scope, conventions, current articles, gaps
├── log/               ← Per-day operation log (one file per day)
│   ├── 20260409.md
│   └── 20260410.md
├── audit/             ← Human feedback inbox (one file per comment)
│   ├── 20260409-143022-claude-code-size.md
│   └── resolved/      ← Processed feedback, archived with resolution notes
├── raw/               ← Immutable source documents (LLM reads, never writes)
│   ├── articles/
│   ├── papers/
│   ├── notes/
│   └── refs/          ← Pointer files for large binaries kept outside raw/
├── wiki/              ← LLM-generated knowledge (LLM writes, you read)
│   ├── index.md       ← Master catalog — every page, structured by category
│   ├── concepts/      ← Concept/topic pages (split into subfolders when >1200 words)
│   ├── entities/      ← People, tools, papers, organizations
│   └── summaries/     ← Per-source summary pages
└── outputs/
    └── queries/       ← Query answers (promote durable ones to wiki/)

CLAUDE.md is the schema file — the single most important configuration. It tells the LLM the wiki's scope, naming conventions, current article list, open questions, and research gaps. Read references/schema-guide.md for what to put in it. Read it at the start of every session.

Core principles

Four rules govern everything below. If a future instruction contradicts one, flag it to the user before acting.

1. Divide and conquer

A single concept page should never try to cover a complex topic end-to-end. Target: 400–1200 words per page. When a topic would blow past that:

  • Create a subfolder: wiki/concepts/<topic>/
  • Put a short index page at wiki/concepts/<topic>/index.md — definition, list of sub-pages, one-line summaries
  • Put each aspect in its own file: wiki/concepts/<topic>/<aspect>.md
  • In wiki/index.md, show the hierarchy via indented bullets

Example layout (from a real wiki):

wiki/tech/claude-code/
├── index.md                         (overview + links to sub-pages)
├── Claude_Code_Architecture.md
├── Claude_Code_Agent_Framework.md
├── Claude_Code_Bridge_System.md
├── Claude_Code_Query_Engine.md
├── Claude_Code_Skills_Plugins.md
├── Claude_Code_State_Management.md
└── Claude_Code_Tool_System.md

One fat file covering all seven aspects would be unreadable and unlinkable. Seven focused files + an index page give you navigation, selective reading, clean backlinks, and small audit targets.

2. Mermaid for diagrams, KaTeX for formulas

  • Any flow, sequence, hierarchy, or state diagram must be written in mermaid — never ASCII art. ASCII boxes rot fast and are impossible to annotate. ``` `mermaid flowchart LR A[raw/article.md] --> B[summary] B --> C[concept page] C --> D[index.md] ` ```
  • Any formula must be written in KaTeX: inline $f(x) = \sum_i w_i x_i$ or block $$...$$.

Both render in the web viewer (server-side KaTeX, client-side mermaid) and in Obsidian with default settings.

3. Raw file policy

Small text-based sources (md, txt, small pdfs, small images) → copy into raw/<subfolder>/.

Large binaries (videos, model weights, installers, datasets, large PDFs >10 MB) → do not copy. Instead:

  • Create a pointer file at raw/refs/<slug>.md with: --- kind: ref external_path: /Volumes/external/models/llama-3-70b/ size: ~140 GB --- followed by a short description of what it is and why it matters to this wiki.
  • Wiki pages cite [[raw/refs/<slug>]] exactly like any other source.

This keeps the wiki repo git-friendly and portable.

4. Audit is the human feedback surface

The wiki is AI-written; it will be wrong sometimes. The raw sources are human-written; they will contradict each other. The audit/ directory is how humans correct both without losing the corrections in chat history.

  • Humans file feedback via the Obsidian plugin or the web viewer. Each feedback is one file in audit/ with YAML frontmatter (anchor, target, severity) and a markdown body.
  • The AI must periodically run the audit op — never silently ignore audit/*.md files.
  • When feedback is applied, the file moves to audit/resolved/ with a # Resolution section appended and a log entry recorded in log/YYYYMMDD.md.

See references/audit-guide.md for the full file format and processing workflow.


The five operations

Every action on the wiki is one of these five. Each appends an entry to the current day's log file (log/YYYYMMDD.md).

1. compile

(Re)structure wiki content from existing raw/ material — including splitting oversized pages, merging near-duplicates, and rebuilding index.md.

When to run: after a big ingest batch, when an existing page has outgrown 1200 words, when index.md no longer reflects reality, or when the user says "clean up the wiki".

Steps:

  1. Read CLAUDE.md, wiki/index.md, and every file in the target subtree.
  2. For each page over ~1200 words: plan a split into concepts/<topic>/ with an index + sub-pages. Confirm the plan with the user before writing.
  3. For each pair of near-duplicate pages: propose a merge. Confirm, then rewrite.
  4. Regenerate wiki/index.md so every page is listed exactly once.
  5. Log: ## [HH:MM] compile | <what you did — files touched, splits, merges>

2. ingest

Add a new source. One source typically touches 5–15 wiki pages.

Steps:

  1. Save source to the right subfolder:

- web article → raw/articles/<slug>.md - paper → raw/papers/<slug>.md (extracted text for big PDFs) - note → raw/notes/<slug>.md - large binary → raw/refs/<slug>.md pointer file (see raw file policy)

  1. Read the source in full.
  2. Create wiki/summaries/<slug>.md (200–400 words — key takeaways, not a rewrite; see references/article-guide.md).
  3. Create or update relevant concept pages in wiki/concepts/. Respect divide-and-conquer: if a concept page would exceed 1200 words, split instead of cramming.
  4. Create or update entity pages in wiki/entities/ for any new people / tools / papers / organizations referenced.
  5. Update wiki/index.md so the new pages appear under the right category.
  6. Log: ## [HH:MM] ingest | <slug> — <one-line description> (touched N pages)

3. query

Answer a question grounded in the wiki, not general knowledge.

Steps:

  1. Read wiki/index.md. Scan for relevant pages by category.
  2. Read the identified pages in full; follow one level of wikilinks.
  3. If the wiki doesn't have enough material, say so and suggest what to ingest next instead of making something up.
  4. Synthesize the answer, citing pages inline with [[Page Name]].
  5. Save to outputs/queries/<YYYY-MM-DD>-<question-slug>.md.
  6. If the answer is durable (a comparison, analysis, or new synthesis) → promote a cleaned-up version to wiki/concepts/, add to index.md.
  7. Log: ## [HH:MM] query | <question-slug> (and a separate ## [HH:MM] promote |... line if promoted).

4. lint

Health check. Run:

python3 scripts/lint_wiki.py <wiki-root>

The script reports:

  • Dead wikilinks — [[Target]] where Target.md doesn't exist
  • Orphan pages — pages with no inbound wikilinks
  • Missing index entries — pages not listed in wiki/index.md
  • Frequently-linked missing pages — [[X]] referenced 3+ times but no page
  • log/ shape — stray files or wrong filenames in log/
  • audit/ shape — malformed YAML frontmatter in audit/*.md
  • Audit target resolution — every open audit's target file must exist

For each issue, propose a fix, confirm with the user, then apply. Log: ## [HH:MM] lint | <N> issues found, <M> fixed.

5. audit

Process human feedback from audit/.

Steps:

  1. Run python3 scripts/audit_review.py <wiki-root> --open to get a grouped list.
  2. For each open audit, read the file. Use the anchor_before / anchor_text / anchor_after window to locate the exact range in the target file (line numbers may have drifted).
  3. Decide the action:

- Accept: apply the correction to the target file. - Partially accept: apply what makes sense, note the rest in the resolution. - Reject: explain why in the resolution — the feedback may be based on a misreading of scope or a contradictory source. - Defer: add to CLAUDE.md "Open research questions" and leave the audit in place with a comment.

  1. For applied audits, append a # Resolution section to the audit file: # Resolution 2026-04-10 · accepted. Fixed the file count (was "~1,900", corrected to "~1,800" per commit abc123). Updated: tech/Claude_Code.md lines 47–48.
  2. Move the file from audit/ to audit/resolved/. Filename unchanged.
  3. Log per resolved audit: ## [HH:MM] audit | resolved 20260409-143022-a1b2 — <one-line what>
  4. Never delete audit files. Rejected ones still go to resolved/ with the rejection rationale in their resolution section — that's valuable history.

See references/audit-guide.md for the full audit file format.


Tooling

ToolPurpose
ObsidianIDE for browsing the wiki; graph view shows connections
plugins/obsidian-audit/Obsidian plugin — select text → add feedback → writes to audit/
web/Local Node.js server — preview the wiki with mermaid/math rendered; select → feedback → audit/
scripts/scaffold.pyBootstrap a new wiki directory tree
scripts/lint_wiki.pySeven-pass health check
scripts/audit_review.pyGroup open/resolved audits by target file
qmdOptional local semantic search (useful at >100 pages)

The Obsidian plugin and the web viewer both write audit files in the same format with the same anchor algorithm, so feedback filed from either place can be resolved by either place.

Starting a new wiki

python3 scripts/scaffold.py <wiki-root> "<Topic Title>"

Creates the full tree (including log/<today>.md, audit/, audit/resolved/), a blank CLAUDE.md based on the new template, and a blank wiki/index.md with the recommended category layout.

After scaffolding:

  1. Fill in CLAUDE.md — define scope, naming conventions, initial research questions.
  2. Start ingesting sources.
  3. Ask questions to build up outputs/queries/; promote durable answers.
  4. Run lint periodically.
  5. Run audit whenever new feedback accumulates.

wiki/index.md format

The LLM rebuilds index.md on every compile and touches it on every ingest. Format:

# Index — <Topic>

> One-sentence scope of the wiki.

## 🔖 Navigation
- [[#Concepts]] · [[#Entities]] · [[#Summaries]] · [[#Open Questions]]

## Concepts
### <Category A>
- [[concepts/Foo]] — one-line summary
- [[concepts/Bar/index|Bar]] — (folder-split) one-line summary
    - [[concepts/Bar/aspect-1]] — ...
    - [[concepts/Bar/aspect-2]] — ...

### <Category B>
- ...

## Entities
- [[entities/Andrej Karpathy]] — AI researcher, author of the llm-wiki pattern

## Summaries (chronological)
- 2026-04-09 — [[summaries/llm-wiki-gist]] — Karpathy's original Gist

## Open Questions
- Q1: ...

Rules:

  • Every wiki page must appear exactly once in index.md. lint enforces this.
  • Folder-split concepts show hierarchy via indented bullets.
  • index.md + CLAUDE.md together are what the AI reads at session start.

log/ format

See references/log-guide.md for full details. Minimum:

  • One file per day: log/YYYYMMDD.md
  • H1 = the date; H2 per entry with ## [HH:MM] <op> | <one-line description>
  • Ops: compile, ingest, query, lint, audit, promote, split, scaffold

Quick grep across history: grep -rh "^## \[" log/ | tail -20.

Use cases

  • Research deep-dive — reading papers/articles on a topic over weeks; the wiki evolves with your understanding, and the audit trail keeps AI mistakes from silently accumulating
  • Personal wiki — journal entries, notes, ideas compiled into a personal encyclopedia; comment on anything you disagree with later, the AI corrects it
  • Team knowledge base — fed by Slack threads, meeting notes, docs; team members file corrections through the web viewer
  • Reading companion — filing each book chapter as you go; builds a rich companion wiki by the end

References

  • references/schema-guide.md — What to put in CLAUDE.md
  • references/article-guide.md — How to write good wiki articles (length, wikilinks, mermaid, math, divide-and-conquer)
  • references/log-guide.md — The log/ folder convention
  • references/audit-guide.md — Audit file format, anchor strategy, processing workflow
  • references/tooling-tips.md — Obsidian setup, Web Clipper, qmd, plugin + web installation

平台分布

Codex

36.19%
按下载量换算43

Claude

29.11%
按下载量换算34

Cursor

16.91%
按下载量换算20

Gemini CLI

9.55%
按下载量换算11

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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