prd-generator — Автоматизированное создание документов требований к продукту

--- 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 ```