name: prd-generator description: Generate comprehensive Product Requirements Documents with AI PM best practices for new features and products

PRD Generator

Overview

The PRD Generator skill helps AI Product Managers create comprehensive, well-structured Product Requirements Documents (PRDs) that incorporate industry best practices, PM frameworks, and AI-specific considerations. It transforms high-level feature ideas into detailed specifications ready for engineering and stakeholder review.

When to Use This Skill

Starting a new feature or product initiative
Need to document requirements for engineering team
Preparing for stakeholder review or approval
Creating specification for AI/ML features
Transitioning from discovery to delivery phase
Documenting complex features with multiple components

PM Frameworks Applied

Jobs-to-be-Done (JTBD): Frames problem from user's perspective
SMART Goals: Ensures success criteria are Specific, Measurable, Achievable, Relevant, Time-bound
RICE Prioritization: Calculates expected impact (Reach × Impact / Effort)
User Story Format: "As a... I want... So that..." structure
MoSCoW Method: Categorizes requirements as Must/Should/Could/Won't have
RACI Matrix: Defines stakeholder roles and responsibilities
AI Product Canvas: Structured approach for AI feature requirements

Inputs Required

{
  "feature_name": "string",
  "problem_statement": "string",
  "target_users": "string or array",
  "business_goals": "array of strings",
  "user_research_summary": "string (optional)",
  "competitive_landscape": "string (optional)",
  "technical_constraints": "array of strings (optional)",
  "success_metrics": "array of objects (optional)",
  "ai_ml_requirements": {
    "model_type": "string (optional)",
    "data_requirements": "string (optional)",
    "performance_targets": "object (optional)"
  }
}

Outputs Produced

A comprehensive PRD document in Markdown format containing:

Executive Summary - One-page overview
Problem Statement - JTBD-framed user problem
Opportunity Sizing - Market size and impact potential
Success Metrics - Quantifiable KPIs with targets
User Stories - Detailed scenarios with acceptance criteria
Functional Requirements - Must/Should/Could/Won't have features
Technical Requirements - Architecture, APIs, dependencies
AI/ML Specifications - Model requirements, data needs, bias mitigation
User Experience - Key flows and interactions
Risk Assessment - Potential issues and mitigations
Launch Plan - Phased rollout strategy
Stakeholder Matrix - RACI chart
Appendix - Research summary, competitive analysis

Usage Instructions

Basic Invocation

Create a PRD for [feature name] that solves [problem] for [user segment]

Detailed Invocation

Generate a comprehensive PRD with the following details:
- Feature: AI-powered recommendation engine
- Problem: Users spend too much time searching for relevant products
- Target Users: E-commerce shoppers, focus on returning customers
- Business Goals: Increase conversion rate by 15%, improve average order value
- Include AI/ML requirements and ethical considerations

With Structured Input

Provide JSON input matching the schema above for most detailed results.

Best Practices

Start with the problem, not the solution - Focus on user needs first
Be specific about success metrics - Include baseline, target, and timeline
Document assumptions - Make implicit knowledge explicit
Include alternatives considered - Show why this approach was chosen
Address "why now?" - Explain timing and urgency
Consider edge cases - Don't just focus on happy path
Plan for failure - Include rollback strategy
Quantify impact - Use data wherever possible
Keep it living - PRD should evolve as you learn

Composition with Other Skills

Recommended Workflow

Before PRD Creation:
- user-research-analyzer → Extract insights from research
- competitive-analyzer → Understand market positioning
- feature-prioritizer → Validate this should be built now
During PRD Creation:
- Use this skill (prd-generator) → Create initial PRD
- ai-ethics-assessor → For AI features, evaluate ethical implications
- metrics-dashboard-builder → Define measurement approach
After PRD Creation:
- user-story-generator → Break down into development tickets
- stakeholder-communicator → Generate alignment updates
- gtm-strategy-builder → Plan launch approach

Common Pitfalls to Avoid

Solution before problem - Jumping to "how" before establishing "why"
Vague success metrics - "Improve user satisfaction" vs. "Increase NPS from 45 to 60"
Skipping alternatives - Not documenting why other approaches were rejected
Ignoring constraints - Technical, resource, or timeline limitations
Missing dependencies - Other teams, systems, or features required
Unclear scope - What's in v1 vs. future versions
No rollback plan - How to handle if feature underperforms
Stakeholder assumptions - Not validating who needs to approve what

AI/ML Specific Considerations

When generating PRDs for AI features, the skill ensures:

Model Performance Requirements: Accuracy, latency, throughput targets
Data Requirements: Training data size, quality, labeling needs
Bias & Fairness: Evaluation criteria across user demographics
Explainability: How users understand AI decisions
Monitoring: Ongoing model performance tracking
Fallback Behavior: What happens when model fails or is uncertain
Ethical Guidelines: Privacy, transparency, accountability measures
Regulatory Compliance: GDPR, AI Act, industry-specific regulations

Python Functions

This skill uses the following Python functions:

`generate_prd(input_data: dict) -> str`

Main function that orchestrates PRD generation.

Parameters:

input_data: Dictionary containing feature requirements

Returns: Complete PRD in Markdown format

`calculate_opportunity_size(reach: int, impact: float, market_size: float) -> dict`

Estimates market opportunity using TAM/SAM/SOM framework.

`generate_success_metrics(business_goals: list, baseline_data: dict) -> list`

Creates SMART metrics with targets and measurement methods.

`extract_user_stories(requirements: list, user_personas: list) -> list`

Converts functional requirements into user story format with acceptance criteria.

`assess_risks(feature_scope: dict, technical_complexity: str) -> list`

Identifies potential risks and suggests mitigation strategies.

`create_launch_phases(scope: dict, dependencies: list) -> dict`

Designs phased rollout plan based on scope and dependencies.

Output Format Example

# PRD: [Feature Name]

## Executive Summary
[One-page overview with problem, solution, impact, and ask]

## Problem Statement
**Job-to-be-Done**: When [situation], I want to [motivation], so I can [outcome].

**Current Experience**: [Pain points]

**Desired Experience**: [Vision]

## Opportunity Sizing
- **TAM** (Total Addressable Market): [size]
- **SAM** (Serviceable Addressable Market): [size]
- **SOM** (Serviceable Obtainable Market): [size]
- **Expected Impact**: [RICE calculation]

## Success Metrics
| Metric | Baseline | Target | Timeline | Measurement |
|--------|----------|--------|----------|-------------|
| [Metric 1] | [value] | [value] | [date] | [method] |

[... continues with all PRD sections]

ナビゲーション

Skillsとは？

リンク

prd-generator