Prd Generator

📄 Product Requirements Document (PRD) Skill

This skill enables an AI agent to produce high-quality, professional PRDs that serve as a single source of truth for product, design, engineering, QA, and leadership teams.

The PRD balances business goals, user needs, and technical execution, and supports both traditional software systems and AI-driven products.

🧠 What This Skill Does

When invoked, this skill:

• Elicits missing context through structured discovery
• Translates ambiguous ideas into clear, actionable requirements
• Produces a complete, testable, and measurable PRD
• Makes assumptions and risks explicit
• Adapts depth and rigor to product maturity and risk
• Treats the PRD as a living, versioned artifact

🎯 When to Use

Use this skill when the user wants to:

• "Write a PRD", "define requirements", or "plan a feature"
• Turn a vague idea into an implementation-ready specification
• Align multiple stakeholders before development
• Document requirements for AI / ML-enabled systems
• Create a reference document that evolves with the product

🧩 Operational Workflow

A PRD must never be generated immediately from a single prompt. The agent must first reduce uncertainty and align expectations.

Phase 0: PRD Strategy Selection

Before discovery, classify the PRD to adapt structure and rigor:

• Product Stage: MVP / Growth / Scale
• Risk Level: Low / Medium / High
• AI Criticality: None / Supporting / Core
• Primary Audience: Engineering / Product / Exec / External

The chosen strategy determines depth, level of detail, and validation rigor.

Phase 1: Discovery - Structured Elicitation

The agent must ask clarifying questions before drafting.

Use a structured approach (Who / What / Why / When / How):

1. Problem & Context
   • What problem are we solving?
   • Why does it matter now?
2. Users & Value
   • Who are the primary users?
   • What outcome do they care about?
3. Success & Measurement
   • How will success be measured?
   • What does "good" look like?
4. Constraints
   • Deadlines, budget, tech stack, compliance?
5. Stakeholders
   • Who needs alignment or approval?

Do not proceed until at least 3 major uncertainties are resolved.

🧾 PRD Structure - Mandatory Output Schema

The PRD output must follow this exact structure and order.

1️⃣ Executive Summary

Purpose: Provide a concise, decision-friendly overview.

• Problem Statement
  1–3 sentences describing the core pain or opportunity.
• Proposed Solution
  1–3 sentences describing the approach (not implementation details).
• Success Criteria
  3–5 measurable KPIs (business, technical, or quality).

2️⃣ Context & Strategic Alignment

Purpose: Explain why this work matters.

• Business or product context
• Strategic goals supported by this initiative
• Relevant constraints or market considerations

3️⃣ User Experience & Functional Scope

Purpose: Anchor requirements in user value.

• User Personas
  Primary personas with goals and pain points.
• User Scenarios / Flows
  High-level description of how users interact with the system.
• User Stories
  As a [persona], I want to [action] so that [benefit].
• Acceptance Criteria
  Clear, testable "done" conditions per story.
• Out of Scope / Non-Goals
  Explicit exclusions to prevent scope creep.

4️⃣ Success Metrics & Release Criteria

Purpose: Define outcomes and readiness.

• Business KPIs
  Adoption, retention, revenue, efficiency.
• Technical KPIs
  Latency, throughput, error rates.
• Quality KPIs
  Availability, reliability, correctness.
• Release Readiness Checklist
  Conditions required for MVP and subsequent releases.

5️⃣ Technical Requirements & Constraints

Purpose: Enable engineering execution.

• High-Level Architecture Overview
  Text or ASCII-based description of components and data flow.
• Component Breakdown
  Services, APIs, data stores, integrations.
• Non-Functional Requirements
  Performance, security, scalability, privacy, compliance.
• Integration Points & Dependencies
  External systems, internal services, third parties.

6️⃣ AI / ML Requirements (If Applicable)

Include only if AI is a core or supporting capability.

• Models, tools, or services used
• Input and output specifications
• Evaluation and quality measurement strategy
• Monitoring, drift detection, and fallback behavior
• Data privacy and safety considerations

7️⃣ Risks, Assumptions & Dependencies

Purpose: Surface uncertainty explicitly.

• Risks
  • Description
  • Impact
  • Likelihood
  • Mitigation strategy
• Assumptions
  • Unvalidated conditions treated as true
• Dependencies
  • Teams, systems, vendors, or approvals

8️⃣ Roadmap & Phased Delivery

Break delivery into incremental phases:

📌 PRD Quality Standards

Requirements Must Be Measurable

Avoid subjective language.

Bad

• "Fast"
• "Easy to use"
• "High quality"

Good

• "P95 latency ≤ 200ms for 10k records"
• "100% Lighthouse accessibility score"
• "≥90% precision on benchmark queries"

🧪 Testability by Design

Every major requirement must indicate:

• How it will be validated
• What can be automated
• What signals indicate failure

AI systems must define offline evaluation and runtime monitoring.

🔁 Iteration & Collaboration Rules

• Treat the PRD as a living document
• Track versions and changes
• Incorporate feedback from product, engineering, QA, and stakeholders
• Revisit assumptions as new information emerges

🧠 AI Self-Review Checklist

Before finalizing, the agent must verify:

• All success metrics are measurable
• Assumptions are explicitly listed
• Non-goals are clearly stated
• Risks include mitigation strategies
• Requirements are testable
• No undefined terms remain

🧪 Example Snippet (Intelligent Search System)

### Document Metadata

- Version: 0.1
- Status: Draft
- Last Updated: YYYY-MM-DD
- Owner: TBD

### Change Log

v0.1 – Initial draft

### 1. Executive Summary
Problem: Developers struggle to find code snippets in large repos.
Solution: AI-enabled code search with natural language interface.
Success KPIs:
- ≤200ms P95 query latency
- ≥90% relevance on benchmark queries
- 30% increase in daily active users

### 2. User Stories
As a developer, I want to ask plain-English questions so I find code faster.
Acceptance:
- Multi-turn refinement
- Code snippets with citations

### 4. Technical Specs
Architecture:
- NLP Service -> Vector DB -> Search API
Performance:
- Search P95 ≤ 200ms under 10k docs
...

### Risks
- Model drift
- Cost of embeddings
...

### PRD Quality Review (AI Self-Check)

- [ ] All success metrics are measurable
- [ ] No undefined technical terms
- [ ] Assumptions explicitly listed
- [ ] Non-goals clearly stated
- [ ] Risks have mitigation strategies

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