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Extraction

Skill wpank/ai/skills/extraction

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".From its SKILL.md

Install
npx -y skills add wpank/ai --skill extraction

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 11 stars11 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

9.9 KB, ~2.2k tokens by cl100k_base, as published. Nobody here has run it

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
  4. 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

What ships with it: 9 files

30.9 KB alongside SKILL.md

Gives 0 of the 12 instructions most architecture codebase skills give in ~2.2k tokens

Counted across 811 of the 1,134 authors here whose files we hold, read 2026-08-07

  • Ask the user which candidate to explorein 45 of 811, across 15 files
  • Apply the deletion test to suspected shallow modulesin 43 of 811, across 15 files
  • Read any relevant architecture decision records firstin 31 of 811, across 8 files
  • Use exact glossary terms in every suggestionin 30 of 811, across 10 files
  • Accept dependencies instead of creating themin 24 of 811, across 5 files
  • Include before and after visualisations for each candidatein 24 of 811, across 5 files
  • Read the domain glossary before exploringin 24 of 811, across 6 files
  • Return results instead of producing side effectsin 23 of 811, across 4 files
  • Explore the codebase for shallow modules and frictionin 23 of 811, across 3 files
  • Introduce seams only where things varyin 22 of 811, across 3 files
  • Reduce the number of methodsin 21 of 811, across 2 files
  • Design deep modules with small interfacesin 21 of 811, across 3 files

Said here and by no other author read

  • read specified reference files before starting extraction
  • map discoveries to extraction categories in priority order
  • filter patterns by reusability and intentionality
  • extract actual token values from design system files
  • verify quality checklist before writing output
  • create docs and skills in target output locations

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 326,851. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.