# Prd

> Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk analysis.

- Skill: `shyamsridhar123/prd` (Agent Skill)
- Install (CLI): `npx skillmds@latest add shyamsridhar123/prd`
- Raw SKILL.md: https://api.skillmd.com/api/skills/shyamsridhar123/prd/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- Author: shyamsridhar123 (https://skillmd.com/u/shyamsridhar123)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/shyamsridhar123/prd

---


# Product Requirements Document (PRD)

## Overview

Design comprehensive, production-grade Product Requirements Documents (PRDs)
that bridge the gap between business vision and technical execution. This skill
works for modern software systems, ensuring that requirements are clearly defined.

## When to Use

Use this skill when:

- Starting a new product or feature development cycle
- Translating a vague idea into a concrete technical specification
- Defining requirements for AI-powered features
- Stakeholders need a unified "source of truth" for project scope
- User asks to "write a PRD", "document requirements", or "plan a feature"

---

## Operational Workflow

### Phase 1: Discovery (The Interview)

Before writing a single line of the PRD, you MUST interrogate the user to fill knowledge gaps. Do not assume context.

Ask about:

- **The Core Problem:** Why are we building this now?
- **Success Metrics:** How do we know it worked?
- **Constraints:** Budget, tech stack, or deadline?

### Phase 2: Analysis & Scoping

Synthesize the user's input. Identify dependencies and hidden complexities.

- Map out the User Flow.
- Define Non-Goals to protect the timeline.

### Phase 3: Technical Drafting

Generate the document using the Strict PRD Schema below.

---

## PRD Quality Standards

### Requirements Quality

Use concrete, measurable criteria. Avoid "fast", "easy", or "intuitive".

```markdown
# Vague (BAD)
- The search should be fast and return relevant results.
- The UI must look modern and be easy to use.

# Concrete (GOOD)
- The search must return results within 200ms for a 10k record dataset.
- The search algorithm must achieve >= 85% Precision@10 in benchmark evals.
- The UI must follow the design system and achieve 100% Lighthouse Accessibility score.
```

---

## Strict PRD Schema

You MUST follow this exact structure for the output:

### 1. Executive Summary

- **Problem Statement:** 1-2 sentences on the pain point.
- **Proposed Solution:** 1-2 sentences on the fix.
- **Success Criteria:** 3-5 measurable KPIs.

### 2. User Experience & Functionality

- **User Personas:** Who is this for?
- **User Stories:** `As a [user], I want to [action] so that [benefit].`
- **Acceptance Criteria:** Bulleted list of "Done" definitions for each story.
- **Non-Goals:** What are we NOT building?

### 3. AI System Requirements (If Applicable)

- **Tool Requirements:** What tools and APIs are needed?
- **Evaluation Strategy:** How to measure output quality and accuracy.

### 4. Technical Specifications

- **Architecture Overview:** Data flow and component interaction.
- **Integration Points:** APIs, DBs, and Auth.
- **Security & Privacy:** Data handling and compliance.

### 5. Risks & Roadmap

- **Phased Rollout:** MVP -> v1.1 -> v2.0.
- **Technical Risks:** Latency, cost, or dependency failures.

---

## Implementation Guidelines

### DO (Always)

- **Define Testing:** For AI systems, specify how to test and validate output quality.
- **Iterate:** Present a draft and ask for feedback on specific sections.

### DON'T (Avoid)

- **Skip Discovery:** Never write a PRD without asking at least 2 clarifying questions first.
- **Hallucinate Constraints:** If the user didn't specify a tech stack, ask or label it as `TBD`.

---

## Example: Intelligent Search System

### 1. Executive Summary

**Problem:** Users struggle to find specific documentation snippets in massive repositories.

**Solution:** An intelligent search system that provides direct answers with source citations.

**Success:**
- Reduce search time by 50%.
- Citation accuracy >= 95%.

### 2. User Stories

- **Story:** As a developer, I want to ask natural language questions so I don't have to guess keywords.
- **Acceptance Criteria:**
  - Supports multi-turn clarification.
  - Returns code blocks with "Copy" button.

### 3. AI System Architecture

- **Tools Required:** `codesearch`, `grep`, `webfetch`.

### 4. Evaluation

- **Benchmark:** Test with 50 common developer questions.
- **Pass Rate:** 90% must match expected citations.