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Agent Skill · 研究检索

llm-wiki-bootstrap(LLM wiki bootstrap 搜索)

llm-wiki-bootstrap 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-bootstrap 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,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 30,367。

总安装 2,892周安装 117GitHub Stars 31下载 908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-bootstrap(LLM wiki bootstrap 搜索)
来源仓库:https://github.com/nanzhipro/karpathy-llm-wiki-bootstrap-skill
仓库路径:skills/llm-wiki-bootstrap
安装命令:
npx skills add https://github.com/nanzhipro/karpathy-llm-wiki-bootstrap-skill --skill llm-wiki-bootstrap
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/nanzhipro/karpathy-llm-wiki-bootstrap-skill --skill llm-wiki-bootstrap

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。

用于检索和筛选 LLM 维基启动相关的信息。适合基于任务场景或来源线索定位技术资料。通过 GitHub 安装,需参考原始仓库了解实现细节。使用前应确认是否具备联网和执行命令的权限。建议核实维护状态以避免兼容性问题。llm-wiki-bootstrap 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-bootstrap

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/nanzhipro/karpathy-llm-wiki-bootstrap-skill --…;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

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

llm-wiki-bootstrap 怎么安装?

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

llm-wiki-bootstrap 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-bootstrap 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

llm-wiki-bootstrap 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-bootstrap 用的人多吗?

公开数据是累计安装 2,892 次,近一周安装 117 次,下载 908 次,GitHub 31 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-bootstrap 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。

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。

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-bootstrap

LLM wiki bootstrap 搜索 · 本页

2,89231需要联网未明确通过

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

Karpathy LLM Wiki Bootstrap

Scaffold a complete LLM Wiki framework from zero: directory structure, schema (the AI instruction file), index, log, and operational workflows. The output is a ready-to-use wiki that the user and their LLM agent can immediately start populating.

Guiding principle: The wiki is a persistent, compounding artifact. The LLM writes and maintains all content. The human curates sources, directs analysis, and asks questions. This skill builds the scaffolding; the user and their LLM fill it with knowledge over time.

Workflow

Six sequential phases. Each phase completes before the next begins.

Phase 1: Gather Requirements

BLOCKING — must complete before any file creation.

Use AskUserQuestion with ALL questions in ONE call:

Q1 — header: "Domain", question: "What is this wiki about?"

  • Options: "Research topic", "Book / media", "Personal (goals, health, learning)", "Business / team", "Other (describe below)"

Q2 — header: "Wiki name", question: "Short name for the wiki root directory?"

  • Free text. Suggest: {domain}-wiki (e.g. ml-wiki, lotr-wiki, health-wiki)

Q3 — header: "Runtimes", question: "Which runtimes will read this wiki? (select all that apply)", multiSelect: true

  • Options:

- "Claude Code" — also generates a CLAUDE.md pointer - "OpenAI Codex" — also generates an AGENTS.md pointer - "Copilot (VS Code)" — also generates a .github/copilot-instructions.md pointer - "Other / generic" — no pointer file (agent reads SCHEMA.md directly)

SCHEMA.md is always generated and is the single source of truth. Each selected runtime gets an additional thin pointer file that redirects to SCHEMA.md. Multiple runtimes may be selected; multiple pointers will coexist.

Q4 — header: "Editor", question: "Primary editor for browsing the wiki?"

  • Options: "Obsidian (recommended)", "VS Code", "Other / plain files"

Q5 — header: "Source types", question: "What kind of sources will you add?"

  • Multi-select. Options: "Web articles", "PDFs / papers", "Books (chapter by chapter)", "Meeting notes / transcripts", "Personal notes / journals", "Images / diagrams", "Data files (CSV, JSON)", "Other"

Q6 — header: "Output location", question: "Where to create the wiki?"

  • Options:

- "Current directory" — create {wiki-name}/ here - "Custom path" — user types absolute path

Store all answers as session variables for subsequent phases.

Phase 2: Create Directory Structure

Based on Phase 1 answers, create the directory tree:

{wiki-root}/
├── raw/                    # Immutable source documents
│   └── assets/             # Images, attachments (if image sources selected)
├── wiki/                   # LLM-maintained markdown pages
│   ├── index.md            # Content catalog
│   ├── log.md              # Chronological operation log
│   └── overview.md         # High-level synthesis (starts empty)
├── SCHEMA.md               # AI instruction file — single source of truth (always)
├── {pointer-files}         # Optional thin redirects per Q3 (CLAUDE.md, AGENTS.md, .github/copilot-instructions.md)
└── .gitignore              # Ignore OS files, keep everything else

Conditional directories / files:

ConditionAdd
Q5 includes images/diagramsraw/assets/
Q4 = Obsidian.obsidian/ is NOT created (Obsidian auto-creates it)
Any source type selectedraw/ with a .gitkeep
Q3 includes Claude CodeCLAUDE.md pointer
Q3 includes OpenAI CodexAGENTS.md pointer
Q3 includes Copilot (VS Code).github/copilot-instructions.md pointer

Phase 3: Generate Schema File

The schema is the most critical output and the single source of truth for all LLM rules. Always write it to {wiki-root}/SCHEMA.md using the template at references/templates/schema.md. Agent-specific files (CLAUDE.md, AGENTS.md, copilot-instructions.md) are generated as pointers in Phase 3.5 and must never duplicate rule content.

Customization rules:

VariableSource
{WIKI_NAME}Q2 answer
{DOMAIN_DESCRIPTION}Q1 answer (expanded to 1-2 sentences)
{SOURCE_TYPES}Q5 answers, comma-separated
{EDITOR}Q4 answer
{DATE}Current date in YYYY-MM-DD

After generating, adapt section details:

  • If domain is "Book / media" → add character, timeline, and plot-thread page types
  • If domain is "Research" → add paper-summary and claim-tracking page types
  • If domain is "Personal" → add journal-entry and goal-tracking page types
  • If domain is "Business / team" → add decision-log and meeting-summary page types

Phase 3.5: Generate Pointer Files

For each runtime selected in Q3 (other than "Other / generic"), generate a thin pointer file from references/templates/agent-pointer.md. Pointer files contain no operating rules — only a redirect to SCHEMA.md plus minimal identity context.

Q3 selectionPointer path{SCHEMA_PATH}
Claude Code{wiki-root}/CLAUDE.md./SCHEMA.md
OpenAI Codex{wiki-root}/AGENTS.md./SCHEMA.md
Copilot (VS Code){wiki-root}/.github/copilot-instructions.md../SCHEMA.md

Template variables:

VariableSource
{WIKI_NAME}Q2 answer
{SCHEMA_PATH}./SCHEMA.md (or ../SCHEMA.md for the Copilot path)

If Q3 only selected "Other / generic" (or nothing), skip this phase entirely — SCHEMA.md alone is sufficient.

Phase 4: Generate Initial Wiki Files

Create three seed files using templates in references/templates/:

4.1 index.md — from references/templates/index.md

  • Start with the universal sections (Sources, Entities, Concepts, Comparisons, Synthesis)
  • Add domain-specific sections matching the page types injected in Phase 3:

- Research → Papers, Claims, Methods, Datasets - Book / media → Characters, Timelines, Themes, Locations - Personal → Journal, Goals, Habits, Lessons - Business → Decision Logs, Meetings, Projects, Stakeholders

  • Leave content sections empty with placeholder comments

4.2 log.md — from references/templates/log.md

  • Write the first entry: wiki creation event with current date

4.3 overview.md — from references/templates/overview.md

  • Minimal stub that explains the wiki's purpose and domain

Phase 5: Editor Configuration

If Q4 = Obsidian:

Do NOT create .obsidian/ or modify Obsidian settings. Instead, append a ## Obsidian Setup section to SCHEMA.md (the single source of truth) with recommendations:

  • Set "Attachment folder path" to raw/assets/ in Settings → Files and links
  • Install recommended plugins: Dataview (frontmatter queries), Marp (slide decks if needed)
  • Use graph view to inspect wiki structure
  • Bind "Download attachments" hotkey if using Web Clipper

Never duplicate this section into any pointer file.

If Q4 = VS Code:

Create .vscode/settings.json with markdown-friendly defaults:

{
  "files.exclude": { "**/.DS_Store": true },
  "editor.wordWrap": "on",
  "markdown.preview.breaks": true
}

Do NOT append an Obsidian section to SCHEMA.md.

If Q4 = Other / plain files:

Skip editor configuration entirely. Do NOT append any editor-specific section to SCHEMA.md. Do NOT create .vscode/.

Phase 6: Summary and Next Steps

After all files are created, output a brief summary:

Wiki scaffolded at {wiki-root}/

Structure:
  raw/          → Drop source documents here
  wiki/         → LLM-maintained pages (index, log, overview)
  SCHEMA.md     → AI instructions (single source of truth)

Pointer files (redirect to SCHEMA.md):
  {list each pointer generated in Phase 3.5, or "none" if skipped}

Next steps:
  1. Open {wiki-root}/ in {editor}
  2. Add your first source to raw/
  3. Tell your LLM agent: "Read {wiki-root}/SCHEMA.md, then ingest raw/{filename}"

Post-Bootstrap Operations

The schema file generated in Phase 3 defines three core operations. These are documented in detail in:

The schema file embeds condensed versions of these workflows. The reference files here contain the full rationale and edge cases for skill maintainers.

Design Principles

These inform all generated content:

  1. LLM-native — all instructions are written as executable protocols for language models, not prose for humans
  2. Source of truth separation — raw sources are immutable; the wiki is derived and regenerable
  3. Incremental compilation — each source is integrated once; knowledge compounds, not re-derived
  4. Convention over configuration — sensible defaults, minimal required decisions
  5. Editor-agnostic core — markdown files work everywhere; editor-specific features are optional layers
  6. Git-friendly — plain text, no binary blobs in wiki/, version history for free

平台分布

Codex

35.97%
按下载量换算327

Claude

27.73%
按下载量换算252

Cursor

18.15%
按下载量换算165

Gemini CLI

9.94%
按下载量换算90

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。目前只有一个公开来源,建议同时核对源仓库。

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