Prp Generator

Purpose

The prp-generator skill provides structured templates and methods for creating comprehensive Product Requirements Prompt (PRP) documents. PRPs serve as the primary input for Phase 3 (Implementation), containing all design information, library documentation, dependencies, implementation plans, testing strategies, and success criteria needed to implement features.

When to Use

This skill auto-activates when you:

• Generate PRP documents from synthesized design
• Create implementation guidance from architectural design
• Structure design information for implementation phase
• Define implementation plans with step-by-step guidance
• Specify testing strategies and success criteria
• Document architecture, libraries, and dependencies for implementers

Provided Capabilities

1. Structured PRP Generation

• Standard Template: Consistent PRP structure across all features
• Section Templates: Pre-defined templates for each PRP section
• Completeness Checking: Ensure all required sections present
• Format Validation: Validate markdown structure and linking

2. Implementation Planning

• Phased Approach: Break implementation into logical phases
• Step-by-Step Guidance: Detailed steps within each phase
• Dependency Ordering: Ensure dependencies installed before use
• Validation Checkpoints: Define validation at each phase

3. Testing Strategy Documentation

• Test Types: Unit, integration, end-to-end testing approaches
• Test Coverage: Specify coverage targets per component
• Mock Strategies: Document how to mock library dependencies
• Test Data: Provide test data and fixture guidance

4. Success Criteria Definition

• Acceptance Criteria: Map from requirements analysis
• Implementation Criteria: Code-level success criteria
• Testing Criteria: Test coverage and passing requirements
• Documentation Criteria: Required documentation artifacts

5. Architecture Documentation

• Component Diagrams: Visual architecture representations
• Data Models: Pydantic schemas with validations
• API Contracts: Endpoint specifications with examples
• Data Flow: How data moves through system

Usage Guide

Step 1: Load Inputs

Load all required inputs for PRP generation:

Required Inputs:

1. Analysis Document: /docs/implementation/analysis/feature-{issue-number}-analysis.md

   • Requirements (functional and non-functional)
   • Security considerations
   • Technical stack requirements
   • Dependencies
   • Scope definition
   • Identified risks

2. Synthesized Design: Output from design-synthesizer skill

   • Component architecture with library mappings
   • Data models with library validation patterns
   • API contracts with library integration
   • Library integration strategies
   • Dependency resolution
   • Implementation path
   • Design decisions
   • Identified issues/gaps

Loading Pattern:

# Read analysis document
analysis = read_document("/docs/implementation/analysis/feature-{issue-number}-analysis.md")

# Parse synthesized design (from synthesis step)
design = parse_synthesized_design()

# Extract key information
requirements = extract_requirements(analysis)
architecture = extract_architecture(design)
libraries = extract_libraries(design)
dependencies = extract_dependencies(design)
implementation_plan = extract_implementation_plan(design)

Step 2: Create PRP Header

Use prp-template.md header section:

# Product Requirements Prompt: [Feature Name] (Issue #{issue-number})

**Date**: [YYYY-MM-DD]
**Designer**: Design Orchestrator (Claude Code)
**Issue**: #{issue-number} - [Issue Title]
**Analysis Document**: /docs/implementation/analysis/feature-{issue-number}-analysis.md

---

**Status**: Ready for Implementation (Phase 3)
**Complexity**: [Low / Medium / High]
**Estimated Effort**: [Hours/Days]
**Priority**: [P0 / P1 / P2 / P3]

---

Complexity Guidelines:

• Low: Single component, <3 files, well-documented libraries
• Medium: Multiple components, 3-10 files, some custom logic
• High: Complex architecture, >10 files, custom integrations

Step 3: Write Executive Summary

Provide 2-3 paragraph overview of the design:

Executive Summary Template:

## Executive Summary

This document provides comprehensive implementation guidance for [feature name],
which [high-level description of what feature does and why it's valuable].

**Architectural Approach**: [Brief description of architecture - e.g., "Service layer
pattern with FastAPI endpoints, SQLAlchemy ORM, and Redis caching"]

**Key Libraries**: [List 3-5 primary libraries - e.g., "FastAPI 0.95.0 for API
framework, SQLAlchemy 2.0.0 for ORM, PassLib 1.7.4 for password hashing"]

**Implementation Strategy**: [Brief description of implementation phases - e.g.,
"4-phase approach: (1) Data models and repository, (2) Service layer business logic,
(3) API endpoints, (4) Testing and validation"]

**Key Considerations**: [1-2 important considerations - e.g., "Security-first
approach with OWASP compliance, performance target of <200ms API response time"]

Example:

## Executive Summary

This document provides comprehensive implementation guidance for user authentication
system, which enables secure user login, session management, and access control for
the application.

**Architectural Approach**: Service layer pattern with FastAPI RESTful API endpoints,
SQLAlchemy ORM for user persistence, PassLib for secure password hashing, and JWT
tokens for session management.

**Key Libraries**: FastAPI 0.95.0 (API framework), SQLAlchemy 2.0.0 (ORM), PassLib
1.7.4 (password hashing), python-jose 3.3.0 (JWT tokens), pydantic 1.10.7 (validation).

**Implementation Strategy**: 4-phase approach: (1) User data model and repository
with SQLAlchemy, (2) Authentication service with PassLib and JWT, (3) FastAPI
endpoints with pydantic validation, (4) Comprehensive testing with pytest and mocks.

**Key Considerations**: OWASP Top 10 compliance for authentication vulnerabilities,
bcrypt with cost factor 12 for password hashing, rate limiting for brute force
protection, secure session token management with expiration.

Step 4: Document Requirements Reference

Link to analysis document and summarize key requirements:

Requirements Reference Template:

## Requirements Reference

**Source**: [Link to analysis document]

### Functional Requirements (Summary)
- **[FR-001]**: [Brief description]
- **[FR-002]**: [Brief description]
...

### Non-Functional Requirements (Summary)
- **Performance**: [Key performance requirements]
- **Security**: [Key security requirements]
- **Usability**: [Key usability requirements]
- **Scalability**: [Key scalability requirements]

### Acceptance Criteria (Key Points)
- [ ] [Top acceptance criterion 1]
- [ ] [Top acceptance criterion 2]
- [ ] [Top acceptance criterion 3]

**Full Requirements**: See analysis document for complete requirements, security
assessment, and risk analysis.

Step 5: Document Architecture Design

Use synthesized architecture design:

Architecture Design Template:

## Architecture Design

### Component Overview

[ASCII diagram or description of component architecture]

### Components

#### Component: [ComponentName]
**Purpose**: [What this component does]
**Responsibilities**:
- [Responsibility 1]
- [Responsibility 2]

**Library Integration**:
- **Primary Library**: [LibraryName] v[Version]
- **APIs Used**: [Specific library APIs/classes]
- **Pattern**: [Integration pattern from design-patterns.md]
- **Dependencies**: [Required dependencies]

**Implementation Notes**:
- [Important implementation detail 1]
- [Important implementation detail 2]

**Code Example**:
```python
# Example from library documentation (libraries-{issue-number}.md, Example X.Y)
[Code snippet showing library usage pattern]

[Repeat for each component]

Data Models

Model: [ModelName]

Purpose: [What this model represents] Fields:

from pydantic import BaseModel, Field
from [library] import [ValidationMixin]

class [ModelName](BaseModel):
    field1: type = Field(..., description="...")  # Validation
    field2: type = Field(default=..., ge=..., le=...)  # Range validation
    # ... more fields

Validation Rules:

• [Field1]: [Validation description]
• [Field2]: [Validation description]

Relationships:

• [Related Model]: [Relationship type and description]

Library Integration:

• Validation: [Library providing validation - e.g., Pydantic validators]
• Serialization: [How model is serialized - e.g., Pydantic .dict()]
• Storage: [ORM pattern - e.g., SQLAlchemy declarative base]

[Repeat for each model]

API Contracts

Endpoint: [METHOD] [/path]

Purpose: [What this endpoint does]

Request:

class [RequestModel](BaseModel):
    field1: type
    field2: type

Response:

class [ResponseModel](BaseModel):
    field1: type
    field2: type

Status Codes:

• 200: Success - [Description]
• 400: Bad Request - [Description]
• 401: Unauthorized - [Description]
• 404: Not Found - [Description]
• 500: Internal Server Error - [Description]

Implementation:

# FastAPI endpoint pattern from library docs
@app.[method]("/path", response_model=[ResponseModel])
def endpoint([params: RequestModel]):
    # Implementation using service layer
    ...

[Repeat for each endpoint]

Data Flow

[Describe how data flows through system]

Example Flow: User Authentication

1. Client sends POST /auth/login with email/password
2. FastAPI endpoint validates request (Pydantic)
3. AuthenticationService.authenticate() called
4. UserRepository.get_by_email() queries database (SQLAlchemy)
5. PassLib.verify() checks password hash
6. JWT token generated (python-jose)
7. Token returned to client

Error Handling Strategy

Library Exception Mapping:

Error Handling Pattern:

try:
    # Library operation
except LibraryException as e:
    # Map to architectural exception
    raise AppException(...) from e

### Step 6: Document Library Documentation

Use library information from Documentation Researcher:

**Library Documentation Template**:
```markdown
## Library Documentation

### Library: [LibraryName] v[Version]

**Purpose**: [What library does]
**Documentation**: [Link to official docs]
**Repository**: [Link to GitHub/source]

**Installation**:
```bash
pip install [library-name]==[version]

Key APIs Used:

• [API1]: [Description and purpose]
• [API2]: [Description and purpose]

Integration Pattern:

# Example from library documentation
[Code example showing how to use library in this project]

Configuration:

# Configuration required for this library
[Configuration code or settings]

Best Practices (from library docs):

• [Best practice 1]
• [Best practice 2]

Known Issues:

• [Issue 1 and workaround]
• [Issue 2 and workaround]

Testing Notes:

• Mocking: [How to mock this library in tests]
• Test Containers: [If integration tests need real library instance]

[Repeat for each library]

Library Comparison (if alternatives considered)

Selection Rationale: [Why chosen library was selected]

### Step 7: Document Dependencies

Use dependency information from Dependency Manager:

**Dependencies Template**:
```markdown
## Dependencies

### Dependency Tree

[Full dependency tree with versions] [library-a]==[version] ├── [dep-1]==[version] │ └── [sub-dep]==[version] ├── [dep-2]==[version] └── [optional-dep] (optional)

### Installation Commands

**Install all dependencies**:
```bash
# Install using pip
pip install -r requirements.txt

# Or using uv (faster)
uv pip install -r requirements.txt

Install in order (if order matters):

# Step 1: Install base dependencies
pip install [base-deps]

# Step 2: Install main libraries
pip install [main-libs]

# Step 3: Install optional dependencies
pip install [optional-deps]

Dependency Details

Dependency: [dep-name] v[version]

Purpose: [What this dependency provides] Required By: [Libraries that need this dependency] Version Constraint: [Why this specific version] Alternatives: [Alternative versions/libraries considered]

[Repeat for key dependencies]

Compatibility Notes

Python Version: [Required Python version] Operating System: [OS compatibility notes] Database: [Database version requirements] Other System Dependencies: [System-level requirements]

Dependency Conflicts and Resolutions

[If any conflicts were found and resolved]

Conflict: [Description]

• Library A needs: [Constraint A]
• Library B needs: [Constraint B]
• Resolution: [How conflict was resolved]
• Pinned Version: [Final version chosen]

### Step 8: Create Implementation Plan

Use `prp-template.md` implementation plan section:

**Implementation Plan Template**:
```markdown
## Implementation Plan

### Overview

This implementation follows a [N]-phase approach, progressing from foundation
(data models) to core functionality to integration to testing. Each phase builds
on the previous phase and has validation checkpoints.

### Phase 1: Foundation ([Estimated Time])

**Goal**: Create data models, repositories, and core utilities

**Tasks**:
1. **Data Models** ([Time])
   - [ ] Create [Model1] Pydantic schema with validations
   - [ ] Create [Model2] Pydantic schema with validations
   - [ ] Add [Library]-specific validators
   - [ ] Write unit tests for model validation

2. **Repository Layer** ([Time])
   - [ ] Create [Repository1] with CRUD operations
   - [ ] Implement [Library] ORM mappings
   - [ ] Add query methods for common operations
   - [ ] Write repository unit tests with mocks

3. **Configuration** ([Time])
   - [ ] Create configuration classes (Pydantic BaseSettings)
   - [ ] Add environment variable loading
   - [ ] Configure [Library1], [Library2]
   - [ ] Write configuration validation tests

**Validation Checkpoint**:
- [ ] All data models validate correctly
- [ ] All repository operations work with test database
- [ ] Configuration loads from environment
- [ ] Phase 1 tests pass (pytest)

### Phase 2: Core Implementation ([Estimated Time])

**Goal**: Implement business logic services using libraries

**Tasks**:
1. **Service Layer** ([Time])
   - [ ] Create [Service1] with business logic
   - [ ] Integrate [Library1] for [functionality]
   - [ ] Implement error handling and exception mapping
   - [ ] Add logging and monitoring
   - [ ] Write service unit tests with mocked dependencies

2. **Library Integration** ([Time])
   - [ ] Set up [Library2] client/connection
   - [ ] Implement [Library2] operations
   - [ ] Add retry logic for transient failures
   - [ ] Write integration tests with test containers

**Validation Checkpoint**:
- [ ] All service methods work correctly
- [ ] Library integrations functioning
- [ ] Error handling covers all cases
- [ ] Phase 2 tests pass (pytest)

### Phase 3: Integration ([Estimated Time])

**Goal**: Connect components and create API endpoints

**Tasks**:
1. **API Endpoints** ([Time])
   - [ ] Create [Endpoint1] with [Method] [/path]
   - [ ] Add request/response validation (Pydantic)
   - [ ] Integrate with service layer
   - [ ] Add authentication/authorization
   - [ ] Write endpoint integration tests

2. **Component Integration** ([Time])
   - [ ] Connect [Component A] to [Component B]
   - [ ] Implement data flow [Flow description]
   - [ ] Add transaction handling
   - [ ] Write integration tests for full flows

3. **Error Handling** ([Time])
   - [ ] Add global exception handlers
   - [ ] Map library exceptions to HTTP status codes
   - [ ] Implement error response formatting
   - [ ] Test error scenarios

**Validation Checkpoint**:
- [ ] All API endpoints respond correctly
- [ ] End-to-end flows work
- [ ] Error handling covers all endpoints
- [ ] Phase 3 tests pass (pytest)

### Phase 4: Testing & Validation ([Estimated Time])

**Goal**: Comprehensive testing and final validation

**Tasks**:
1. **Unit Test Coverage** ([Time])
   - [ ] Achieve [X]% unit test coverage
   - [ ] All components have isolated unit tests
   - [ ] All edge cases covered
   - [ ] Mock all external dependencies

2. **Integration Testing** ([Time])
   - [ ] Test all API endpoints (end-to-end)
   - [ ] Test library integrations with real instances
   - [ ] Test error scenarios and recovery
   - [ ] Performance testing ([targets])

3. **Security Validation** ([Time])
   - [ ] OWASP Top 10 verification
   - [ ] Input validation testing
   - [ ] Authentication/authorization testing
   - [ ] Secrets management verification

4. **Documentation** ([Time])
   - [ ] Update API documentation
   - [ ] Add code comments for complex logic
   - [ ] Create user guide (if needed)
   - [ ] Update README with new feature

**Final Validation**:
- [ ] All acceptance criteria met
- [ ] All tests pass (pytest)
- [ ] Coverage targets achieved
- [ ] Security validation complete
- [ ] Documentation complete
- [ ] Code review passed

### Phase 5: Deployment ([Estimated Time])

**Goal**: Deploy to production environment

**Tasks**:
1. **Pre-Deployment** ([Time])
   - [ ] Review deployment checklist
   - [ ] Verify environment configuration
   - [ ] Database migrations tested
   - [ ] Rollback plan documented

2. **Deployment** ([Time])
   - [ ] Deploy to staging environment
   - [ ] Run smoke tests in staging
   - [ ] Deploy to production
   - [ ] Monitor for errors

3. **Post-Deployment** ([Time])
   - [ ] Verify production functionality
   - [ ] Monitor performance metrics
   - [ ] Check logs for errors
   - [ ] Update documentation

**Deployment Validation**:
- [ ] Feature working in production
- [ ] No critical errors in logs
- [ ] Performance meets targets
- [ ] Monitoring dashboards updated

Step 9: Define Testing Strategy

Use prp-template.md testing section:

Testing Strategy Template:

## Testing Strategy

### Overview

Comprehensive testing approach with [X]% code coverage target, including unit tests
(with mocks), integration tests (with test containers), and end-to-end tests (full
API flow).

### Unit Testing

**Scope**: Individual functions, classes, methods in isolation

**Framework**: pytest

**Coverage Target**: [X]% for all modules

**Mocking Strategy**:
- Mock [Library1] using unittest.mock
- Mock [Library2] using [library-specific test fixtures]
- Mock database using SQLAlchemy in-memory database

**Test Structure**:

tests/ ├── unit/ │ ├── test_models.py # Data model validation tests │ ├── test_repositories.py # Repository logic tests (mocked DB) │ ├── test_services.py # Service logic tests (mocked deps) │ └── test_validators.py # Custom validator tests

**Example Unit Test**:
```python
# Test with mocked library
from unittest.mock import Mock
import pytest

def test_service_method():
    # Mock library dependency
    mock_lib = Mock()
    mock_lib.method.return_value = "expected"

    # Test service with mock
    service = MyService(mock_lib)
    result = service.do_something()

    assert result == "processed_expected"
    mock_lib.method.assert_called_once()

Key Test Cases:

• [Component1]: Test [key functionality]
• [Component2]: Test [error handling]
• [Model1]: Test [validation rules]

Integration Testing

Scope: Components working together with real library instances

Framework: pytest with testcontainers

Test Containers Used:

• PostgreSQL: testcontainers.postgres.PostgresContainer
• Redis: testcontainers.redis.RedisContainer

Test Structure:

tests/
├── integration/
│   ├── test_database_operations.py   # Real DB tests
│   ├── test_cache_operations.py      # Real Redis tests
│   └── test_library_integrations.py  # Real library usage

Example Integration Test:

from testcontainers.postgres import PostgresContainer

def test_repository_integration():
    with PostgresContainer("postgres:15") as postgres:
        # Create real database engine
        engine = create_engine(postgres.get_connection_url())
        # Test with real database
        ...

Key Test Cases:

• [Integration1]: Test [data flow]
• [Integration2]: Test [library interaction]

End-to-End Testing

Scope: Complete API flows from request to response

Framework: pytest with TestClient (FastAPI) or requests

Test Structure:

tests/
├── e2e/
│   ├── test_api_flows.py         # Complete API workflows
│   ├── test_authentication.py    # Auth flows
│   └── test_error_scenarios.py   # Error handling

Example E2E Test:

from fastapi.testclient import TestClient

def test_user_creation_flow():
    client = TestClient(app)

    # Full flow test
    response = client.post("/users", json={"email": "test@example.com"})
    assert response.status_code == 201

    user_id = response.json()["id"]
    get_response = client.get(f"/users/{user_id}")
    assert get_response.status_code == 200

Key Test Cases:

• [Flow1]: Test [complete user journey]
• [Flow2]: Test [error recovery]

Performance Testing

Scope: Verify performance meets non-functional requirements

Tools: pytest-benchmark, locust

Target Metrics:

• API response time: [target] (95th percentile)
• Database query time: [target]
• Cache hit rate: [target]

Test Cases:

• Benchmark [operation] performance
• Load test [endpoint] with [N] concurrent requests

Security Testing

Scope: Verify security controls and OWASP compliance

Test Cases:

• SQL injection prevention (parameterized queries)
• XSS prevention (output sanitization)
• CSRF protection (tokens)
• Authentication bypass attempts
• Authorization boundary testing
• Secrets not in logs or responses

Test Data Management

Test Fixtures:

# Shared test fixtures
@pytest.fixture
def sample_user():
    return User(id=1, email="test@example.com", name="Test User")

@pytest.fixture
def mock_database():
    # Create in-memory database for testing
    ...

Test Data Location: tests/fixtures/

Running Tests

All Tests:

pytest

Unit Tests Only:

pytest tests/unit/

Integration Tests Only:

pytest tests/integration/

With Coverage:

pytest --cov=src --cov-report=html

Coverage Report: htmlcov/index.html

Continuous Integration

CI Pipeline (GitHub Actions / GitLab CI):

1. Run linting (black, mypy)
2. Run unit tests
3. Run integration tests (with test containers)
4. Generate coverage report
5. Fail if coverage < [X]%

### Step 10: Document Success Criteria

Use acceptance criteria from analysis and implementation-specific criteria:

**Success Criteria Template**:
```markdown
## Success Criteria

### Acceptance Criteria (from Requirements)

From analysis document (feature-{issue-number}-analysis.md):

- [ ] **AC-001**: [Acceptance criterion from analysis]
- [ ] **AC-002**: [Acceptance criterion from analysis]
- [ ] **AC-003**: [Acceptance criterion from analysis]

### Implementation Criteria

- [ ] **IC-001**: All data models defined with Pydantic validation
- [ ] **IC-002**: All repository CRUD operations implemented with [ORM]
- [ ] **IC-003**: All service methods implemented with error handling
- [ ] **IC-004**: All API endpoints implemented with [Framework]
- [ ] **IC-005**: All library integrations working ([Library1], [Library2])

### Testing Criteria

- [ ] **TC-001**: Unit test coverage ≥ [X]%
- [ ] **TC-002**: All integration tests passing
- [ ] **TC-003**: All end-to-end API tests passing
- [ ] **TC-004**: Performance tests meet targets ([metric] < [target])
- [ ] **TC-005**: Security tests passing (OWASP compliance)

### Code Quality Criteria

- [ ] **QC-001**: Linting passes (black, mypy)
- [ ] **QC-002**: No critical security vulnerabilities (bandit/safety)
- [ ] **QC-003**: Code review approved
- [ ] **QC-004**: All functions have docstrings
- [ ] **QC-005**: Complex logic has explanatory comments

### Documentation Criteria

- [ ] **DC-001**: API endpoints documented (OpenAPI/Swagger)
- [ ] **DC-002**: README updated with new feature
- [ ] **DC-003**: User guide created (if user-facing)
- [ ] **DC-004**: Code comments for complex logic
- [ ] **DC-005**: CHANGELOG updated

### Deployment Criteria

- [ ] **DEP-001**: Feature deployed to staging
- [ ] **DEP-002**: Smoke tests passing in staging
- [ ] **DEP-003**: Feature deployed to production
- [ ] **DEP-004**: Production monitoring dashboards updated
- [ ] **DEP-005**: No critical errors in first 24 hours

### Definition of Done

All of the following must be true:
- ✅ All acceptance criteria met
- ✅ All implementation criteria met
- ✅ All testing criteria met
- ✅ All code quality criteria met
- ✅ All documentation criteria met
- ✅ All deployment criteria met
- ✅ Feature reviewed and approved
- ✅ Feature in production and stable

Step 11: Validate PRP Completeness

Use prp-template.md validation checklist:

PRP Completeness Checklist:

• Header with metadata (date, designer, issue, analysis link)
• Executive summary (2-3 paragraphs)
• Requirements reference with key points
• Architecture design (components, data models, APIs, data flow, error handling)
• Library documentation (all libraries with examples and best practices)
• Dependencies (tree, installation, compatibility)
• Implementation plan (phased with tasks and validation checkpoints)
• Testing strategy (unit, integration, e2e, performance, security)
• Documentation requirements (what docs to create)
• Success criteria (acceptance, implementation, testing, quality, docs, deployment)
• Risks & mitigations (from synthesis)

Step 12: Write PRP to File

Save PRP to designated location:

File Path: /docs/implementation/prp/feature-{issue-number}-prp.md

Final PRP Structure:

# Product Requirements Prompt: [Feature Name] (Issue #{issue-number})
[Header with metadata]
## Executive Summary
[2-3 paragraphs]
## Requirements Reference
[Key requirements from analysis]
## Architecture Design
[Components, models, APIs, data flow, error handling]
## Library Documentation
[All libraries with examples]
## Dependencies
[Tree, installation, compatibility]
## Implementation Plan
[Phases with tasks and validation]
## Testing Strategy
[Unit, integration, e2e, performance, security]
## Documentation Requirements
[What to document]
## Success Criteria
[Acceptance, implementation, testing, quality, docs, deployment]
## Risks & Mitigations
[From synthesis and analysis]
---
**PRP Complete**: [Date/Time]
**Ready for Phase 3**: Implementation

Best Practices

1. Be Comprehensive But Concise

• Include all necessary information
• Avoid redundancy
• Link to other documents for details
• Use examples liberally

2. Make It Actionable

• Every section should guide implementer
• Provide code examples
• Specify exact commands
• Define clear validation checkpoints

3. Ensure Traceability

• Link requirements to design elements
• Link design to implementation tasks
• Link tasks to test cases
• Link tests to acceptance criteria

4. Validate Completeness

• Use prp-template.md checklist
• Verify all required sections present
• Check all links work
• Ensure all code examples valid

5. Consider the Implementer

• Write for someone unfamiliar with design discussions
• Provide context and rationale
• Explain trade-offs and decisions
• Anticipate questions

Resources

prp-template.md

Complete PRP template with:

• Standard structure
• Section templates
• Markdown formatting
• Validation checklist

prp-examples.md

Example PRPs showing:

• Simple feature PRP
• Complex feature PRP
• Multi-component PRP
• API-focused PRP
• Data-focused PRP

Example Usage

See prp-examples.md for complete PRP examples.

Integration

This skill is used by:

• design-orchestrator agent during Phase 2: Design & Planning
• Activates automatically when generating PRP from synthesized design
• Output feeds into Phase 3: Implementation (Implementation Specialist)

Version: 2.0.0 Auto-Activation: Yes (when generating PRP documents) Phase: 2 (Design & Planning) Created: 2025-10-29

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