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extractionextraction 文档

Agent Skill

extraction 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

27,048

周安装

1,150

GitHub Stars

公开资料未说明

下载量

9,476
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:extraction(extraction 文档)
来源仓库:https://github.com/wpank/extraction
安装命令:
openclaw skills install extraction
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 extraction

简介

将设计系统、架构模式和方法从代码库中提取为可重用的技能和文档。在分析项目以捕获模式、从现有代码创建技能、提取设计标记或记录项目的构建方式时使用。触发“提取模式”、“从该存储库提取”、“分析该代码库”、“从该项目创建技能”、“提取设计系统”。

SKILL.md

name
pattern-extraction
model
reasoning
description
Extract design systems, architecture patterns, and methodology from codebases into reusable skills and documentation. Use when analyzing a project to capture patterns, creating skills from existing code, extracting design tokens, or documenting how a project was built. Triggers on "extract patterns", "extract from this repo", "analyze this codebase", "create skills from this project", "extract design system".

Pattern Extraction

Extract reusable patterns, skills, and methodology documentation from existing codebases.

Installation

OpenClaw / Moltbot / Clawbot

npx clawhub@latest install extraction

Before Starting

MANDATORY: Read these reference files based on what you're extracting:

ExtractingRead First
Any extractionmethodology-values.md — priority order and what to look for
Specific categoriesextraction-categories.md — detailed patterns per category
Generating skillsskill-quality-criteria.md — quality checklist

Extraction Process

Phase 1: Discovery

Analyze the project to understand what exists.

Scan for project structure:

- Root directory layout
- Key config files (package.json, tailwind.config.*, etc.)
- Documentation (README, docs/, etc.)
- Source organization (src/, app/, components/, etc.)

Identify tech stack:

IndicatorTechnology
package.json with reactReact
tailwind.config.*Tailwind CSS
components.jsonshadcn/ui
go.modGo
DockerfileDocker
k8s/ or .yaml manifestsKubernetes
turbo.jsonTurborepo
MakefileMake automation

Look for design system signals:

  • Custom Tailwind config (not defaults)
  • CSS variables / custom properties
  • Theme files
  • Design documentation
  • Mood boards or reference lists

Capture key findings:

  • What's the tech stack?
  • What's the folder structure?
  • Is there a documented design direction?
  • What workflows exist (Makefile, scripts)?

Phase 2: Categorization

Map discoveries to extraction categories, prioritized:

Priority order:

  1. Design Systems — Color tokens, typography, spacing, motion, aesthetic documentation
  2. UI Patterns — Component organization, layouts, interactions
  3. Architecture — Folder structure, data flow, API patterns
  4. Workflows — Build, dev, deploy, CI/CD
  5. Domain-Specific — Patterns unique to this application type

For each category found, note:

  • What specific patterns exist?
  • Where are they defined? (file paths)
  • Are they documented? (comments, docs)
  • Are they worth extracting? (used in multiple places, well-designed)

Filter by value:

ExtractSkip
Patterns used across multiple componentsOne-off solutions
Customized configs with intentionDefault configurations
Documented design decisionsArbitrary choices
Reusable infrastructureProject-specific hacks

Phase 3: Extraction

For each valuable pattern, generate outputs.

Design Systems → Design System Doc + Skill

  1. Read the Tailwind config, CSS files, theme files
  2. Extract actual token values (colors, typography, spacing)
  3. Document the aesthetic direction
  4. Create:

- docs/extracted/[project]-design-system.md using design-system.md template - ai/skills/[project]-design-system/SKILL.md if patterns are reusable

Architecture → Methodology Doc

  1. Document folder structure with reasoning
  2. Capture data flow patterns
  3. Note key technical decisions
  4. Create docs/extracted/[project]-summary.md using project-summary.md template

Patterns → Skills

For each pattern worth a skill:

  1. Load skill-quality-criteria.md
  2. Use skill-template.md template
  3. Verify the quality checklist:

- Description has WHAT, WHEN, KEYWORDS - No explanations of basics Claude knows - Has specific NEVER list - < 300 lines ideal

  1. Create ai/skills/[project]-[pattern]/SKILL.md

Phase 4: Validation

Before writing output, validate extracted content.

For each skill, verify:

  • [ ] Description has WHAT, WHEN, and trigger KEYWORDS
  • [ ] >70% expert knowledge (not in base Claude model)
  • [ ] <300 lines (max 500)
  • [ ] Has "When to Use" section with clear triggers
  • [ ] Has code examples (if applicable)
  • [ ] Has NEVER Do section with anti-patterns
  • [ ] Project-agnostic (no hardcoded project names)

For documentation, verify:

  • [ ] Actual values extracted (not placeholders)
  • [ ] Templates fully filled out
  • [ ] Aesthetic direction documented (for design systems)
  • [ ] File paths are correct

Conflict detection: Before creating a new skill, check if similar skills exist:

# Check existing skills in the target repo
ls ai/skills/*/
SituationAction
Similar skill existsEnhance existing skill instead
Overlapping patternsNote overlap, may merge in refinement
Unique patternProceed with new skill

Phase 5: Output

Write extracted content to target locations.

Methodology Documentation:

docs/extracted/
├── [project]-summary.md       # Overall methodology
├── [project]-design-system.md # Design tokens and aesthetic
└── [project]-architecture.md  # Code patterns (if complex)

Skills:

ai/skills/
└── [project]-[category]/
    ├── SKILL.md
    └── references/  # (if needed for detailed content)

Create docs/extracted/ directory if it doesn't exist.


Extraction Focus Areas

Design System Extraction (Highest Priority)

When a project has intentional design work, extract thoroughly:

Must capture:

  • Color palette (primary, secondary, accent, semantic)
  • Typography (fonts, scale, weights)
  • Spacing scale
  • Motion/animation patterns
  • The "vibe" or aesthetic direction

Look in:

  • tailwind.config.js / tailwind.config.ts
  • globals.css / app.css / root CSS files
  • theme.ts / theme.js
  • Any design documentation

Generate:

  1. Design system documentation with actual values
  2. Skill capturing the aesthetic philosophy (if distinctive)

Workflow Extraction

Look for:

  • Makefile targets
  • package.json scripts
  • Docker configurations
  • CI/CD workflows

Extract:

  • Dev setup commands
  • Build processes
  • Deployment patterns

Error Handling

SituationResolution
No patterns foundCreate project summary only; document why extraction failed
Pattern too project-specificSkip or generalize by removing project names
Incomplete patternExtract what exists, note gaps in skill
Quality criteria not metRevise skill or skip pattern
Similar skill already existsUpdate existing skill instead of creating new
Can't find source filesNote in extraction log, skip that category

When extraction fails partially:

  1. Complete what can be extracted
  2. Document gaps in the project summary
  3. Note "Incomplete extraction" in output
  4. Suggest what additional information would be needed

NEVER Do

  • NEVER extract default configurations — Only extract customized, intentional patterns
  • NEVER create skills for basic concepts — Claude already knows React, Tailwind basics
  • NEVER skip the aesthetic — Design philosophy is highest priority
  • NEVER generate skills > 500 lines — Use references/ for detailed content
  • NEVER create skills without good descriptions — Description determines if skill activates
  • NEVER extract one-off solutions — Focus on patterns used in multiple places
  • NEVER skip validation phase — Quality check before writing output
  • NEVER leave project names in skills — Make patterns project-agnostic
  • NEVER create duplicate skills — Check for existing similar skills first

Quality Check Before Finishing

  • [ ] Design system captured (if one exists)?
  • [ ] Methodology summary created?
  • [ ] Skills have proper descriptions (WHAT, WHEN, KEYWORDS)?
  • [ ] Skills pass the expert knowledge test?
  • [ ] Anti-patterns documented in skills?
  • [ ] Output files created in correct locations?

After Extraction: Staging for Refinement

If you're extracting to later consolidate patterns across multiple projects:

Copy results to the skills toolkit repo for staging:

# From this project, copy to the skills repo staging area
cp -r ai/skills/[project]-* /path/to/skills-repo/ai/staging/skills/
cp -r docs/extracted/* /path/to/skills-repo/ai/staging/docs/

Staging folder structure:

ai/staging/
├── skills/           # Extracted skills from multiple projects
│   ├── project-a-design-system/
│   ├── project-b-ui-patterns/
│   └── ...
└── docs/             # Extracted methodology docs
    ├── project-a-summary.md
    ├── project-b-design-system.md
    └── ...

After staging content from multiple projects:

  • Say "refine staged content" or "consolidate staged skills"
  • The refinement process will:

- Identify patterns across projects - Consolidate into project-agnostic skills - Update methodology docs with insights - Promote refined skills to active locations


Related Skills

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

73.15%
按下载量换算6,932

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

未展示

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。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。

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