Agent-Skills.md

Agent Skills: 📄 Product Requirements Document (PRD) Skill

Generate production-ready Product Requirements Documents (PRDs) for software systems and AI-powered features. The skill ensures clear problem framing, measurable outcomes, scoped functionality, testable requirements, technical feasibility, risk awareness, and stakeholder alignment.

UncategorizedID: jaktestowac/awesome-copilot-for-testers/prd-generator

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jaktestowac/awesome-copilot-for-testers
jaktestowacLicense: MIT
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skills/prd-generator/SKILL.md

Skill Metadata

Name
prd-generator
Description
Generate production-ready Product Requirements Documents (PRDs) for software systems and AI-powered features. The skill ensures clear problem framing, measurable outcomes, scoped functionality, testable requirements, technical feasibility, risk awareness, and stakeholder alignment.

📄 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:

| Phase | Goals | Dependencies | Exit Criteria | |------|------|-------------|---------------| | MVP | ... | ... | ... | | v1.1 | ... | ... | ... | | Future | ... | ... | ... |

📌 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