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The Compospec Methodology

Specification intelligence for AI-native development

Build product specifications as structured context cards with auto-generated flow diagrams.

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The Problem: Spec Chaos

AI can't fix broken specs. If your product requirements live in scattered docs, slides, and Slack threads: you're building prompt debt.

Every hallucination. Every misalignment. Every "AI got it wrong" moment traces back to one thing: unstructured context.

graph TB
    A[User Needs] --> Z[Scattered Sources]
    B[UI Specs] --> Z
    C[Requirements] --> Z
    D[Revisions] --> Z
    E[New Features] --> Z
    Z --> F[Developer Confusion]
    Z --> G[AI Hallucinations]
    Z --> H[Wasted Time]
    
    style Z fill:#ef4444,stroke:#991b1b,color:#fff
    style F fill:#f59e0b,stroke:#92400e,color:#fff
    style G fill:#f59e0b,stroke:#92400e,color:#fff
    style H fill:#f59e0b,stroke:#92400e,color:#fff
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Traditional documentation:

  • 74-page Word doc → AI context window overload
  • Vague requirements → 40-question clarification loop
  • Scattered sources → AI stitches wrong pieces together

Result: Communication breakdown between business logic and technical reality.


The Solution: Structured Context Blocks

Compospec is a specification intelligence platform that transforms chaotic requirements into:

  • 6-level hierarchy: Product → Module → UI → Section → Component → Element
  • Card-based specifications: Persistent, linked, version-controlled
  • Auto-generated flow diagrams: Visual representation of user journeys
  • AI-ready semantic structure: No prompt engineering required
graph LR
    A[📝 Write] --> B[🔗 Link]
    B --> C[📊 Flow]
    
    A1[Structured context cards] -.-> A
    B1[Parent-child relationships<br/>across hierarchy] -.-> B
    C1[Auto-generated diagrams<br/>from card connections] -.-> C
    
    style A fill:#7c3aed,stroke:#5b21b6,color:#fff
    style B fill:#7c3aed,stroke:#5b21b6,color:#fff
    style C fill:#10b981,stroke:#047857,color:#fff
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Your specs become a semantic intelligence layer ready for AI agents, developers, and stakeholders.


How It Works

1. Write → Structured Context Cards

Select a card type from the 6-level hierarchy:

📦 Product      (top-level business goal)
  └─ 📂 Module      (feature cluster)
      └─ 🖥️ User Interface (screen/page)
          └─ 📐 Section       (layout region)
              └─ 🧩 Component    (interactive element)
                  └─ ⚛️ Element       (atomic unit)

Write requirements in natural language. No learning curve.

graph TD
    A[Select Card Type] --> B[Write Title + Content]
    B --> C[Add Attachments/Images]
    C --> D[Card Created]
    
    style A fill:#7c3aed,stroke:#5b21b6,color:#fff
    style D fill:#10b981,stroke:#047857,color:#fff
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2. Link → Parent-Child Relationships

Every card (except Product) links to one parent. This creates:

  • Dependency graph (what depends on what)
  • Context inheritance (child inherits parent constraints)
  • Traceability (requirement → implementation path)
graph TB
    P[📦 Product: Mobile Banking App]
    P --> M1[📂 Module: Money Transfer]
    P --> M2[📂 Module: Account Management]
    
    M1 --> U1[🖥️ UI: Transfer Dashboard]
    U1 --> S1[📐 Section: Balance Widget]
    S1 --> C1[🧩 Component: Currency Selector]
    C1 --> E1[⚛️ Element: Dropdown Menu]
    
    style P fill:#7c3aed,stroke:#5b21b6,color:#fff
    style M1 fill:#8b5cf6,stroke:#6d28d9,color:#fff
    style U1 fill:#a78bfa,stroke:#7c3aed,color:#fff
    style S1 fill:#c4b5fd,stroke:#8b5cf6,color:#000
    style C1 fill:#ddd6fe,stroke:#a78bfa,color:#000
    style E1 fill:#ede9fe,stroke:#c4b5fd,color:#000
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3. Flow → Auto-Generated Diagrams

Flow diagrams aren't drawn — they're derived from parent-child relationships.

When you link cards, diagrams update automatically. No manual drawing. No stale documentation.

graph LR
    A[Link Card to Parent] --> B{Flow Exists?}
    B -->|Yes| C[Update Existing Flow]
    B -->|No| D[Generate New Flow]
    C --> E[Diagram Auto-Synced]
    D --> E
    
    style A fill:#7c3aed,stroke:#5b21b6,color:#fff
    style E fill:#10b981,stroke:#047857,color:#fff
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Why Developers Care

Hierarchical Specs Match Your Mental Model

Just like HTML DOM structure or component trees:

<Product>
  <Module>
    <UserInterface>
      <Section>
        <Component>
          <Element />
        </Component>
      </Section>
    </UserInterface>
  </Module>
</Product>

Explicit Dependencies = No Hidden Assumptions

Parent-child links make dependencies visible and queryable:

// Pseudocode example
const button = getCard("submit-button");
const dependencies = button.getAncestors();
// Returns: [Form Component, Login Section, Auth UI, User Module, Product]

AI-Ready Context Blocks = No Prompt Debt

Each card is a semantic unit with:

  • Typed fields (not unstructured prose)
  • Explicit relationships (not implicit mentions)
  • Hierarchical context (parent chain = full context)

Feed cards to AI agents → precise, scoped prompts. No guesswork.


Why Compospec for AI-Native Pipelines

AI code generation tools (Cursor, GitHub Copilot, v0.dev) work well for isolated components but fail at system-wide consistency.

The problem isn't AI capability. It's context persistence.

The Three Failure Modes

  1. Wrong Intent: AI misinterprets vague specs
  2. Wrong Visual: Generated code doesn't match design system
  3. Wrong Patterns: Output doesn't fit codebase conventions

Compospec as Pre-AI Semantic Layer

Instead of prompting AI directly, Compospec provides structured context that AI pipelines can query:

  • Hierarchical Cards → preserve intent cascade; parent context flows to child cards
  • Decision Subcards(coming Q2 2026) capture component behavior as queryable state machines
  • Journey Maps → maintain multi-step workflow context across the pipeline
  • MCP Protocol Integration(coming Q1 2026) connects directly to Claude, Cursor, Figma MCP, Code Connect
graph LR
    A[Compospec Cards] --> B[MCP Protocol]
    B --> C[Claude API]
    B --> D[Cursor]
    B --> E[v0.dev]
    B --> F[GitHub Copilot]
    
    C --> G[Context-Aware Code]
    D --> G
    E --> G
    F --> G
    
    style A fill:#7c3aed,stroke:#5b21b6,color:#fff
    style B fill:#10b981,stroke:#047857,color:#fff
    style G fill:#3b82f6,stroke:#1e40af,color:#fff
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Spec.md is a file. Compospec is a schema.

That's the difference between using AI and shipping with it.


Repository Structure

This repository documents the Compospec methodology — the principles, patterns, and practices that make specification intelligence possible.

compospec-methodology/
├── README.md              ← You are here
├── METHODOLOGY.md         ← Deep dive into the system
├── EXAMPLES.md            ← Real-world use cases
├── COMPARISON.md          ← vs other approaches
└── assets/
    └── diagrams/          ← Mermaid source + exported PNGs

Use Cases

  • Spec-driven development: Start with structure, not chaos
  • AI-native pipelines: Pre-AI semantic layer for Cursor, Copilot, v0.dev workflows
  • AI agent context: Feed semantically rich specs to LLMs
  • Cross-team alignment: Single source of truth for product, design, engineering
  • Legacy modernization: Document existing systems with precision

Get Started

📖 Learn the methodology: Read METHODOLOGY.md →

💻 Try Compospec Beta: app.compospec.com

🌐 Website: compospec.com

📧 Questions? info@compospec.com


What's Next

  • SS Annotation (Q1 2026): Screenshot-to-card workflow with browser-native Screen Capture API
  • MCP Integration (Q1 2026): Direct protocol connection to Claude, Cursor, Figma, Code Connect
  • Decision Subcards (Q2 2026): State/event/transition modeling for component behavior
  • Journey Tags (Q2 2026): Filter flows by user task (purchase, login, cancel) across the system

Built with conviction in London 🇬🇧

Compospec is a product of Tabulas Design Ltd

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