Sparc Specification

SPARC Specification Agent

Requirements analysis specialist focused on creating comprehensive, clear, and testable specifications for the SPARC methodology.

Quick Start

# Invoke SPARC Specification phase
npx claude-flow sparc run spec-pseudocode "Define authentication system requirements"

# Or directly in Claude Code
# "Use SPARC specification to define requirements for user authentication"

When to Use

• Starting a new feature or project that needs clear requirements
• Translating stakeholder needs into technical specifications
• Creating acceptance criteria for user stories
• Documenting constraints (technical, business, regulatory)
• Defining use cases with preconditions and postconditions

Prerequisites

• Clear understanding of project goals
• Access to stakeholders for requirements validation
• Knowledge of existing system constraints
• Understanding of compliance requirements (if applicable)

Core Concepts

SPARC Specification Phase

The Specification phase is the foundation of SPARC methodology:

1. Define clear, measurable requirements - Avoid ambiguity
2. Identify constraints and boundaries - Technical, business, regulatory
3. Create acceptance criteria - Testable pass/fail conditions
4. Document edge cases and scenarios - What happens when things go wrong
5. Establish success metrics - How to measure completion

Requirement Types

Implementation Pattern

Requirements Document Structure

specification:
  functional_requirements:
    - id: "FR-001"
      description: "System shall authenticate users via OAuth2"
      priority: "high"
      acceptance_criteria:
        - "Users can login with Google/GitHub"
        - "Session persists for 24 hours"
        - "Refresh tokens auto-renew"

  non_functional_requirements:
    - id: "NFR-001"
      category: "performance"
      description: "API response time <200ms for 95% of requests"
      measurement: "p95 latency metric"

    - id: "NFR-002"
      category: "security"
      description: "All data encrypted in transit and at rest"
      validation: "Security audit checklist"

Constraint Analysis

constraints:
  technical:
    - "Must use existing PostgreSQL database"
    - "Compatible with Node.js 18+"
    - "Deploy to AWS infrastructure"

  business:
    - "Launch by Q2 2024"
    - "Budget: $50,000"
    - "Team size: 3 developers"

  regulatory:
    - "GDPR compliance required"
    - "SOC2 Type II certification"
    - "WCAG 2.1 AA accessibility"

Use Case Definition

use_cases:
  - id: "UC-001"
    title: "User Registration"
    actor: "New User"
    preconditions:
      - "User has valid email"
      - "User accepts terms"
    flow:
      1. "User clicks 'Sign Up'"
      2. "System displays registration form"
      3. "User enters email and password"
      4. "System validates inputs"
      5. "System creates account"
      6. "System sends confirmation email"
    postconditions:
      - "User account created"
      - "Confirmation email sent"
    exceptions:
      - "Invalid email: Show error"
      - "Weak password: Show requirements"
      - "Duplicate email: Suggest login"

Acceptance Criteria (Gherkin)

Feature: User Authentication

  Scenario: Successful login
    Given I am on the login page
    And I have a valid account
    When I enter correct credentials
    And I click "Login"
    Then I should be redirected to dashboard
    And I should see my username
    And my session should be active

  Scenario: Failed login - wrong password
    Given I am on the login page
    When I enter valid email
    And I enter wrong password
    And I click "Login"
    Then I should see error "Invalid credentials"
    And I should remain on login page
    And login attempts should be logged

Configuration

# sparc-specification-config.yaml
specification_settings:
  output_format: "markdown"
  id_prefix: "FR-"
  priority_levels: ["critical", "high", "medium", "low"]

templates:
  requirements_doc: ".agent-os/specs/{spec-name}/spec.md"
  use_cases: ".agent-os/specs/{spec-name}/sub-specs/use-cases.md"

validation:
  require_acceptance_criteria: true
  require_priority: true
  require_testability: true

Usage Examples

Example 1: API Requirements

# System Requirements Specification

## 1. Introduction
### 1.1 Purpose
This system provides user authentication and authorization...

### 1.2 Scope
- User registration and login
- Role-based access control
- Session management
- Security audit logging

### 1.3 Definitions
- **User**: Any person with system access
- **Role**: Set of permissions assigned to users
- **Session**: Active authentication state

## 2. Functional Requirements

### 2.1 Authentication
- FR-2.1.1: Support email/password login
- FR-2.1.2: Implement OAuth2 providers
- FR-2.1.3: Two-factor authentication

### 2.2 Authorization
- FR-2.2.1: Role-based permissions
- FR-2.2.2: Resource-level access control
- FR-2.2.3: API key management

## 3. Non-Functional Requirements

### 3.1 Performance
- NFR-3.1.1: 99.9% uptime SLA
- NFR-3.1.2: <200ms response time
- NFR-3.1.3: Support 10,000 concurrent users

### 3.2 Security
- NFR-3.2.1: OWASP Top 10 compliance
- NFR-3.2.2: Data encryption (AES-256)
- NFR-3.2.3: Security audit logging

Example 2: Data Model Specification

entities:
  User:
    attributes:
      - id: uuid (primary key)
      - email: string (unique, required)
      - passwordHash: string (required)
      - createdAt: timestamp
      - updatedAt: timestamp
    relationships:
      - has_many: Sessions
      - has_many: UserRoles

  Role:
    attributes:
      - id: uuid (primary key)
      - name: string (unique, required)
      - permissions: json
    relationships:
      - has_many: UserRoles

  Session:
    attributes:
      - id: uuid (primary key)
      - userId: uuid (foreign key)
      - token: string (unique)
      - expiresAt: timestamp
    relationships:
      - belongs_to: User

Execution Checklist

• Gather requirements from stakeholders
• Define functional requirements with IDs
• Define non-functional requirements (performance, security)
• Document technical, business, regulatory constraints
• Create use cases with flows and exceptions
• Write acceptance criteria in Gherkin format
• Define data model entities and relationships
• Validate all requirements are testable
• Get stakeholder approval

Best Practices

1. Be Specific: Avoid ambiguous terms like "fast" or "user-friendly"
2. Make it Testable: Each requirement should have clear pass/fail criteria
3. Consider Edge Cases: What happens when things go wrong?
4. Think End-to-End: Consider the full user journey
5. Version Control: Track specification changes
6. Get Feedback: Validate with stakeholders early

Error Handling

Metrics & Success Criteria

• All requirements have unique IDs
• 100% of requirements have acceptance criteria
• All NFRs have measurable targets
• Stakeholder sign-off obtained
• Zero ambiguous requirements

Integration Points

MCP Tools

// Store specification phase start
mcp__claude-flow__memory_usage {
  action: "store",
  key: "sparc/specification/status",
  namespace: "coordination",
  value: JSON.stringify({
    phase: "specification",
    status: "in_progress",
    timestamp: Date.now()
  })
}

Hooks

# Pre-specification hook
npx claude-flow@alpha hooks pre-task --description "SPARC Specification phase"

# Post-specification hook
npx claude-flow@alpha hooks post-task --task-id "spec-complete"

Related Skills

• sparc-pseudocode - Next phase: algorithm design
• sparc-architecture - System design phase
• sparc-refinement - TDD implementation phase

References

• SPARC Methodology
• Gherkin Syntax
• IEEE 830 SRS Standard

Version History

• 1.0.0 (2026-01-02): Initial release - converted from agent to skill format