sparc-specification
SPARC Specification phase specialist for requirements analysis, constraint identification, use case definition, and acceptance criteria creation
Best use case
sparc-specification is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
SPARC Specification phase specialist for requirements analysis, constraint identification, use case definition, and acceptance criteria creation
Teams using sparc-specification should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/sparc-specification/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How sparc-specification Compares
| Feature / Agent | sparc-specification | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
SPARC Specification phase specialist for requirements analysis, constraint identification, use case definition, and acceptance criteria creation
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# SPARC Specification Agent
> Requirements analysis specialist focused on creating comprehensive, clear, and testable specifications for the SPARC methodology.
## Quick Start
```bash
# 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
| Type | Purpose | Example |
|------|---------|---------|
| Functional (FR) | What the system does | "System shall authenticate users via OAuth2" |
| Non-Functional (NFR) | Quality attributes | "API response time <200ms for 95% of requests" |
| Constraint | Limitations | "Must use existing PostgreSQL database" |
## Implementation Pattern
### Requirements Document Structure
```yaml
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
```yaml
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
```yaml
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)
```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
```yaml
# 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
```markdown
# 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
```yaml
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
| Issue | Resolution |
|-------|------------|
| Ambiguous requirements | Ask clarifying questions, use specific metrics |
| Missing acceptance criteria | Add testable pass/fail conditions |
| Conflicting requirements | Document and escalate to stakeholders |
| Scope creep | Reference original scope, create change request |
## 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
```javascript
// 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
```bash
# 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](../sparc-pseudocode/SKILL.md) - Next phase: algorithm design
- [sparc-architecture](../sparc-architecture/SKILL.md) - System design phase
- [sparc-refinement](../sparc-refinement/SKILL.md) - TDD implementation phase
## References
- [SPARC Methodology](https://github.com/ruvnet/claude-flow)
- [Gherkin Syntax](https://cucumber.io/docs/gherkin/)
- [IEEE 830 SRS Standard](https://standards.ieee.org/)
## Version History
- **1.0.0** (2026-01-02): Initial release - converted from agent to skill formatRelated Skills
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