Skill Extract

Extract Skill - Implementation Guide

Overview

The extract skill provides comprehensive reverse-engineering capabilities for design systems and product architectures. It transforms undocumented codebases into structured, implementation-ready documentation.

Capabilities

Design System Extraction

• Token Extraction: Colors, typography, spacing, shadows from code or CSS
• Component Analysis: Props, variants, usage patterns across React/Vue/Svelte
• Pattern Detection: Layout patterns, design rules, accessibility guidelines
• Storybook Generation: Auto-generated stories with variants and controls

Product Architecture Extraction

• Service Detection: Microservice boundaries, modules, domain boundaries
• API Mapping: REST, GraphQL, tRPC, gRPC endpoint cataloging
• Data Modeling: ORM schema extraction (Prisma, TypeORM, Sequelize)
• Feature Inventory: Route-based and domain-based feature detection
• C4 Diagrams: Automated architecture visualization (Mermaid)

Technical Implementation

Token Extraction Pipeline

Priority Order (High to Low Confidence):

1. Code-Defined (95%): theme.ts, tokens.json, Tailwind config
2. CSS Variables (90%): :root declarations
3. Computed Styles (60%): DOM analysis
4. Inferred (40-60%): Color clustering, scale detection

Color Clustering Algorithm:

• Uses CIEDE2000 for perceptually-accurate color distance
• K-means++ initialization for stable clustering
• Default k=8 clusters for primary palettes
• ΔE < 2 threshold for duplicate detection

Component Analysis

Detection Strategies:

• AST parsing for TypeScript/JavaScript
• Prop extraction from interfaces and PropTypes
• Variant detection from union types
• Usage tracking across codebase

Supported Frameworks:

• React (functional, class, hooks)
• Vue (SFC, Composition API, Options API)
• Svelte (script/template separation)

Architecture Detection

Service Boundary Heuristics:

• Package.json in subdirectories
• Independent deployment configs
• Team ownership boundaries
• Communication pattern analysis

API Endpoint Detection:

• Decorator-based routing (NestJS, routing-controllers)
• Express/Fastify route definitions
• GraphQL resolver classes
• tRPC router procedures
• Protocol Buffer (.proto) files

Multi-AI Orchestration

When enabled, the extract feature uses multiple AI providers for higher accuracy:

Provider Roles:

• Claude: Synthesis, conflict resolution, documentation
• Codex: Code-level analysis, type extraction, architecture
• Gemini: Pattern recognition, alternative interpretations, UX insights

Consensus Mechanism:

• Threshold: 67% (2/3 providers must agree)
• Disagreements logged in 90_evidence/disagreements.md
• Confidence scores attached to all outputs

Output Structure

octopus-extract/
└── project-name/
    └── timestamp/
        ├── README.md                   # Navigation and summary
        ├── metadata.json               # Extraction parameters
        │
        ├── 00_intent/
        │   ├── answers.json            # User intent responses
        │   ├── intent-contract.md      # Human-readable summary
        │   └── detection-report.md     # Stack auto-detection results
        │
        ├── 10_design/
        │   ├── tokens.json             # W3C Design Tokens format
        │   ├── tokens.css              # CSS custom properties
        │   ├── tokens.md               # Human-readable token docs
        │   ├── components.csv          # Component inventory (tabular)
        │   ├── components.json         # Structured component data
        │   ├── patterns.md             # Layout and design patterns
        │   └── storybook/              # Storybook scaffold (optional)
        │       ├── .storybook/
        │       └── stories/
        │
        ├── 20_product/
        │   ├── product-overview.md     # What, who, key journeys
        │   ├── feature-inventory.md    # Features by domain
        │   ├── architecture.md         # C4 text description
        │   ├── architecture.mmd        # Mermaid C4 diagrams
        │   ├── PRD.md                  # AI-agent executable PRD
        │   ├── user-stories.md         # Gherkin-style scenarios
        │   ├── api-contracts.md        # Endpoint specifications
        │   ├── data-model.md           # Entity relationships
        │   └── implementation-plan.md  # Phased milestones
        │
        └── 90_evidence/
            ├── quality-report.md       # Coverage and confidence metrics
            ├── disagreements.md        # Multi-AI conflicts
            ├── extraction-log.md       # Timestamped progress log
            └── references.json         # File paths per claim

Quality Gates

Automated validation ensures extraction quality:

1. Token Coverage: Fail if 0 tokens in design mode
2. Component Coverage: Warn if < 50% of component files detected
3. Architecture Completeness: Warn if no services detected in product mode
4. Multi-AI Consensus: Fail if < 50% agreement on key outputs

Usage Patterns

Basic Extraction

/octo:extract ./my-app

Design-Only Extraction

/octo:extract ./my-app --mode design --storybook true

Deep Analysis with Multi-AI

/octo:extract ./my-app --depth deep --multi-ai force

URL Extraction

/octo:extract https://example.com --mode design --depth quick

Integration with Other Skills

• /octo:review: Review extracted outputs for quality
• /octo:deliver: Validate extraction completeness
• /octo:docs: Generate additional documentation from extractions

Error Handling

Common error codes:

• ERR-001: Invalid input (path/URL not found)
• ERR-002: Network timeout (URL extraction)
• ERR-003: Permission denied
• ERR-004: Out of memory (use --depth quick)
• VAL-001: Validation failed (no tokens detected)
• VAL-004: Low multi-AI consensus

Performance Targets

Research Sources

This skill is informed by research on:

• Tokens Studio - Design token automation
• Superposition - Token extraction from websites
• W3C Design Tokens - Token format standard
• C4 Model - Architecture diagramming
• Modern reverse-engineering practices (2026)

Implementation Status

Current Version: 1.0.0 (Skeleton)

Implemented:

• ✅ Command structure
• ✅ CLI argument parsing
• ✅ Output directory setup
• ✅ Metadata generation
• ✅ Multi-AI detection

In Progress:

• 🚧 Token extraction pipeline
• 🚧 Component analysis engine
• 🚧 Architecture detection
• 🚧 PRD generation
• 🚧 Quality gates

Planned:

• ⏳ Storybook scaffold generation
• ⏳ C4 diagram generation
• ⏳ URL extraction mode
• ⏳ CSS inference algorithms

Contributing

See implementation plan in project documentation.

Implementation phases:

1. Foundation & CLI (Week 1)
2. Auto-Detection Engine (Week 2)
3. Design Extraction (Week 3-4)
4. Product Extraction (Week 5-6)
5. Multi-AI Orchestration (Week 7)
6. Quality Gates (Week 8)
7. Testing & Documentation (Week 10)

This skill implements the design specified in PRD v2.0 (AI-Executable)