PaperclipOrg
Claude Agent Skill · by Github

Prd

The prd skill generates comprehensive Product Requirements Documents that translate business concepts into detailed technical specifications for software system

Install
Terminal · npx
$npx skills add https://github.com/github/awesome-copilot --skill prd
Works with Paperclip

How Prd fits into a Paperclip company.

Prd drops into any Paperclip agent that handles this kind of work. Assign it to a specialist inside a pre-configured PaperclipOrg company and the skill becomes available on every heartbeat — no prompt engineering, no tool wiring.

S
SaaS FactoryPaired

Pre-configured AI company — 18 agents, 18 skills, one-time purchase.

$27$59
Explore pack
Source file
SKILL.md143 lines
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---name: prddescription: 'Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk analysis.'license: MIT--- # 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". ```diff# 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 'Vercel/Next.js' 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.- **AC**:  - 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.