An early-stage framework for documenting an existing product’s design and architecture. Command parsing, output setup, and metadata generation are implemented; extracting colors, components, APIs, and architecture remains in progress or planned.
Reverse-engineer design systems, tokens, and components from live products or screenshots
Review the instructions the agent will receive before you install.
Host: Codex CLI — This skill was designed for Claude Code and adapted for Codex. Cross-reference commands use installed skill names in Codex rather than
/octo:*slash commands. Use the active Codex shell and subagent tools. Do not claim a provider, model, or host subagent is available until the current session exposes it. For host tool equivalents, seeskills/blocks/codex-host-adapter.md.
The extract skill provides comprehensive reverse-engineering capabilities for design systems and product architectures. It transforms undocumented codebases into structured, implementation-ready documentation.
Priority Order (High to Low Confidence):
theme.ts, tokens.json, Tailwind config:root declarationsColor Clustering Algorithm:
Detection Strategies:
Supported Frameworks:
Service Boundary Heuristics:
API Endpoint Detection:
When enabled, the extract feature uses multiple AI providers for higher accuracy:
Provider Roles:
Consensus Mechanism:
90_evidence/disagreements.mdoctopus-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
Automated validation ensures extraction quality:
/octo:extract ./my-app
/octo:extract ./my-app --mode design --storybook true
/octo:extract ./my-app --depth deep --multi-ai force
/octo:extract https://example.com --mode design --depth quick
Common error codes:
ERR-001: Invalid input (path/URL not found)ERR-002: Network timeout (URL extraction)ERR-003: Permission deniedERR-004: Out of memory (use --depth quick)VAL-001: Validation failed (no tokens detected)VAL-004: Low multi-AI consensus| Depth | Time Target | Coverage Target |
|---|---|---|
| Quick | < 2 min | 70% coverage, basic analysis |
| Standard | 2-5 min | 85% coverage, comprehensive |
| Deep | 5-15 min | 95% coverage, multi-AI validation |
This skill is informed by research on:
Current Version: 1.0.0 (Skeleton)
Implemented:
In Progress:
Planned:
See implementation plan in project documentation.
Implementation phases:
This skill implements the design specified in PRD v2.0 (AI-Executable)
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Interpret the saved results of a completed AI council review. Count verdicts, check whether enough participants responded and whether models are independent, surface substantive dissent, and explain what action the outcome supports.
Turn a technical choice into a structured debate between different AI model families. Assign explicit positions, launch the discussion, and summarize agreement, disagreement, and a recommendation supported by the strongest argument that survives the debate.
Clarify a feature’s requirements with multiple AI providers, combining technical constraints with user and business needs. Produce a scoped definition covering priorities, edge cases, exclusions, and a requirements checklist before implementation begins.
Review code or documents with available AI providers before delivery. Combine findings into a scored validation report that checks prior requirements and decisions, separates critical issues from warnings, and recommends next steps.
Develop complex features or business documents by gathering approaches from available AI providers. Combine their perspectives with prior project decisions and quality checks to produce a proposed plan, then proceed with implementation after user confirmation.
Research technical or business questions through multiple available AI providers, each assigned a different perspective. Combine usable results into a report covering findings, disagreements, evidence gaps, priorities, and recommended next steps.