[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AE0B8T4SQSSX84DPX8AR4A":3,"skill-same:01M6AE0B8T4SQSSX84DPX8AR4A":78},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":53,"ownerHtmlUrl":54,"ownerBlogUrl":55,"ownerDescription":56,"updatedAt":57,"install":58,"resources":71},"01M6AE0B8T4SQSSX84DPX8AR4A","prd",null,"Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk analysis.","github\u002Fawesome-copilot","github","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F9919?v=4","01M6AC83DS133TPXFSP1DHNCC6","GitHub","01M6AD3PBDYPK7NVFJNJ66Y8RS",41028,"MIT","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fprd\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000031","产品经理","PROFESSION","适用职业",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"systems","stories","features","summaries","for","high","user stories","specifications","Product Requirements","software","documents","requirements","technical","quality","analysis","risk","product","generate","risk analysis","2026-11-10 10:12:30",false,"---\nname: prd\ndescription: 'Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk analysis.'\nlicense: MIT\n---\n\n# Product Requirements Document (PRD)\n\n## Overview\n\nDesign 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.\n\n## When to Use\n\nUse this skill when:\n\n- Starting a new product or feature development cycle\n- Translating a vague idea into a concrete technical specification\n- Defining requirements for AI-powered features\n- Stakeholders need a unified \"source of truth\" for project scope\n- User asks to \"write a PRD\", \"document requirements\", or \"plan a feature\"\n\n---\n\n## Operational Workflow\n\n### Phase 1: Discovery (The Interview)\n\nBefore writing a single line of the PRD, you **MUST** interrogate the user to fill knowledge gaps. Do not assume context.\n\n**Ask about:**\n\n- **The Core Problem**: Why are we building this now?\n- **Success Metrics**: How do we know it worked?\n- **Constraints**: Budget, tech stack, or deadline?\n\n### Phase 2: Analysis & Scoping\n\nSynthesize the user's input. Identify dependencies and hidden complexities.\n\n- Map out the **User Flow**.\n- Define **Non-Goals** to protect the timeline.\n\n### Phase 3: Technical Drafting\n\nGenerate the document using the **Strict PRD Schema** below.\n\n---\n\n## PRD Quality Standards\n\n### Requirements Quality\n\nUse concrete, measurable criteria. Avoid \"fast\", \"easy\", or \"intuitive\".\n\n```diff\n# Vague (BAD)\n- The search should be fast and return relevant results.\n- The UI must look modern and be easy to use.\n\n# Concrete (GOOD)\n+ The search must return results within 200ms for a 10k record dataset.\n+ The search algorithm must achieve >= 85% Precision@10 in benchmark evals.\n+ The UI must follow the 'Vercel\u002FNext.js' design system and achieve 100% Lighthouse Accessibility score.\n```\n\n---\n\n## Strict PRD Schema\n\nYou **MUST** follow this exact structure for the output:\n\n### 1. Executive Summary\n\n- **Problem Statement**: 1-2 sentences on the pain point.\n- **Proposed Solution**: 1-2 sentences on the fix.\n- **Success Criteria**: 3-5 measurable KPIs.\n\n### 2. User Experience & Functionality\n\n- **User Personas**: Who is this for?\n- **User Stories**: `As a [user], I want to [action] so that [benefit].`\n- **Acceptance Criteria**: Bulleted list of \"Done\" definitions for each story.\n- **Non-Goals**: What are we NOT building?\n\n### 3. AI System Requirements (If Applicable)\n\n- **Tool Requirements**: What tools and APIs are needed?\n- **Evaluation Strategy**: How to measure output quality and accuracy.\n\n### 4. Technical Specifications\n\n- **Architecture Overview**: Data flow and component interaction.\n- **Integration Points**: APIs, DBs, and Auth.\n- **Security & Privacy**: Data handling and compliance.\n\n### 5. Risks & Roadmap\n\n- **Phased Rollout**: MVP -> v1.1 -> v2.0.\n- **Technical Risks**: Latency, cost, or dependency failures.\n\n---\n\n## Implementation Guidelines\n\n### DO (Always)\n\n- **Define Testing**: For AI systems, specify how to test and validate output quality.\n- **Iterate**: Present a draft and ask for feedback on specific sections.\n\n### DON'T (Avoid)\n\n- **Skip Discovery**: Never write a PRD without asking at least 2 clarifying questions first.\n- **Hallucinate Constraints**: If the user didn't specify a tech stack, ask or label it as `TBD`.\n\n---\n\n## Example: Intelligent Search System\n\n### 1. Executive Summary\n\n**Problem**: Users struggle to find specific documentation snippets in massive repositories.\n**Solution**: An intelligent search system that provides direct answers with source citations.\n**Success**:\n\n- Reduce search time by 50%.\n- Citation accuracy >= 95%.\n\n### 2. User Stories\n\n- **Story**: As a developer, I want to ask natural language questions so I don't have to guess keywords.\n- **AC**:\n  - Supports multi-turn clarification.\n  - Returns code blocks with \"Copy\" button.\n\n### 3. AI System Architecture\n\n- **Tools Required**: `codesearch`, `grep`, `webfetch`.\n\n### 4. Evaluation\n\n- **Benchmark**: Test with 50 common developer questions.\n- **Pass Rate**: 90% must match expected citations.\n","# Product Requirements Document (PRD)\n\n## Overview\n\nDesign 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.\n\n## When to Use\n\nUse this skill when:\n\n- Starting a new product or feature development cycle\n- Translating a vague idea into a concrete technical specification\n- Defining requirements for AI-powered features\n- Stakeholders need a unified \"source of truth\" for project scope\n- User asks to \"write a PRD\", \"document requirements\", or \"plan a feature\"\n\n---\n\n## Operational Workflow\n\n### Phase 1: Discovery (The Interview)\n\nBefore writing a single line of the PRD, you **MUST** interrogate the user to fill knowledge gaps. Do not assume context.\n\n**Ask about:**\n\n- **The Core Problem**: Why are we building this now?\n- **Success Metrics**: How do we know it worked?\n- **Constraints**: Budget, tech stack, or deadline?\n\n### Phase 2: Analysis & Scoping\n\nSynthesize the user's input. Identify dependencies and hidden complexities.\n\n- Map out the **User Flow**.\n- Define **Non-Goals** to protect the timeline.\n\n### Phase 3: Technical Drafting\n\nGenerate the document using the **Strict PRD Schema** below.\n\n---\n\n## PRD Quality Standards\n\n### Requirements Quality\n\nUse concrete, measurable criteria. Avoid \"fast\", \"easy\", or \"intuitive\".\n\n```diff\n# Vague (BAD)\n- The search should be fast and return relevant results.\n- The UI must look modern and be easy to use.\n\n# Concrete (GOOD)\n+ The search must return results within 200ms for a 10k record dataset.\n+ The search algorithm must achieve >= 85% Precision@10 in benchmark evals.\n+ The UI must follow the 'Vercel\u002FNext.js' design system and achieve 100% Lighthouse Accessibility score.\n```\n\n---\n\n## Strict PRD Schema\n\nYou **MUST** follow this exact structure for the output:\n\n### 1. Executive Summary\n\n- **Problem Statement**: 1-2 sentences on the pain point.\n- **Proposed Solution**: 1-2 sentences on the fix.\n- **Success Criteria**: 3-5 measurable KPIs.\n\n### 2. User Experience & Functionality\n\n- **User Personas**: Who is this for?\n- **User Stories**: `As a [user], I want to [action] so that [benefit].`\n- **Acceptance Criteria**: Bulleted list of \"Done\" definitions for each story.\n- **Non-Goals**: What are we NOT building?\n\n### 3. AI System Requirements (If Applicable)\n\n- **Tool Requirements**: What tools and APIs are needed?\n- **Evaluation Strategy**: How to measure output quality and accuracy.\n\n### 4. Technical Specifications\n\n- **Architecture Overview**: Data flow and component interaction.\n- **Integration Points**: APIs, DBs, and Auth.\n- **Security & Privacy**: Data handling and compliance.\n\n### 5. Risks & Roadmap\n\n- **Phased Rollout**: MVP -> v1.1 -> v2.0.\n- **Technical Risks**: Latency, cost, or dependency failures.\n\n---\n\n## Implementation Guidelines\n\n### DO (Always)\n\n- **Define Testing**: For AI systems, specify how to test and validate output quality.\n- **Iterate**: Present a draft and ask for feedback on specific sections.\n\n### DON'T (Avoid)\n\n- **Skip Discovery**: Never write a PRD without asking at least 2 clarifying questions first.\n- **Hallucinate Constraints**: If the user didn't specify a tech stack, ask or label it as `TBD`.\n\n---\n\n## Example: Intelligent Search System\n\n### 1. Executive Summary\n\n**Problem**: Users struggle to find specific documentation snippets in massive repositories.\n**Solution**: An intelligent search system that provides direct answers with source citations.\n**Success**:\n\n- Reduce search time by 50%.\n- Citation accuracy >= 95%.\n\n### 2. User Stories\n\n- **Story**: As a developer, I want to ask natural language questions so I don't have to guess keywords.\n- **AC**:\n  - Supports multi-turn clarification.\n  - Returns code blocks with \"Copy\" button.\n\n### 3. AI System Architecture\n\n- **Tools Required**: `codesearch`, `grep`, `webfetch`.\n\n### 4. Evaluation\n\n- **Benchmark**: Test with 50 common developer questions.\n- **Pass Rate**: 90% must match expected citations.\n",{"name":5,"license":15,"description":7},"main","5269f7ea2d6c3a9bfda5ed878b6726cf509121fd","skills\u002Fprd",5335,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot","https:\u002F\u002Fawesome-copilot.github.com\u002F","https:\u002F\u002Fgithub.com\u002Fgithub","https:\u002F\u002Fgithub.com\u002Fabout","How people build software.","2026-11-11 11:59:04",{"source":8,"skillName":5,"commands":59,"prompt":69,"zipAvailable":70},[60,63,66],{"tool":61,"command":62},"npx","npx skills add github\u002Fawesome-copilot --skill prd",{"tool":64,"command":65},"bunx","bunx skills add github\u002Fawesome-copilot --skill prd",{"tool":67,"command":68},"pnpm","pnpm dlx skills add github\u002Fawesome-copilot --skill prd","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fprd\u002FSKILL.md",true,[72],{"path":73,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":76,"sourceHtmlUrl":77},"scripts\u002Fworktree-inventory.sh","SCRIPT","脚本",14842,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fgit\u002Fworktree-cleaner\u002Fscripts\u002Fworktree-inventory.sh",[79,114,141,169,202,219],{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":83,"categories":84,"keywords":93,"publishedAt":43},"01M70KXZNX9ES38N2A8M58ZZMZ","supabase-rls-policies","Write, review, and test Supabase Row Level Security (RLS) policies so that one user cannot read or change another user's rows. Use when creating or changing tables, policies, views, or security definer functions in a Supabase project, when reviewing a migration that touches RLS, or when asked to prove user isolation with pgTAP tests that sign in as two users.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fsupabase-rls-policies\u002FSKILL.md",[85,90],{"categoryNo":86,"categoryName":87,"taxonomyType":88,"taxonomyTypeDesc":89},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":91,"categoryName":92,"taxonomyType":88,"taxonomyTypeDesc":89},"01JBSEEDCAT000000000000023","Skill与智能体开发",[94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113],"supabase","rls","supabase rls","supabase rls policies","project","when","review","change","read","write","security","isolation","tables","as","tests","prove","use","users","functions","with",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":121,"publishedAt":43},"01M70KWA5YWXDV6V8DZ19E3T2Y","skill-distribution-audit","Audit a skill collection before release or investigate installer omissions, archived\u002Ftemplate leakage, duplicate identities, and manifest drift. Reconcile the intended public source set, distribution manifest, and actual discovery output for a specified consumer version. Includes a deterministic set-comparison helper.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fskill-distribution-audit\u002FSKILL.md",[120],{"categoryNo":91,"categoryName":92,"taxonomyType":88,"taxonomyTypeDesc":89},[122,123,124,125,126,127,28,128,129,130,131,132,133,134,135,136,137,138,139,140],"distribution","skill distribution audit","audit","SET","deterministic","installer","discovery","comparison","helper","manifest","version","collection","drift","output","investigate","duplicate","template","release","reconcile",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":145,"sourceHtmlUrl":146,"categories":147,"keywords":151,"publishedAt":43},"01M70KCJPFVW7PPQ49CC5R8JGK","logicapps-custom-code-net10-migration","Migrate Azure Logic Apps Standard local-function custom-code projects from .NET 8 to .NET 10. Use for legacy Logic Apps custom-code workspace upgrades, project\u002Fpackage configuration, custom-worker settings, publication, and integrated VS Code debugging. Not for ordinary .NET applications, standalone Azure Functions, Consumption workflows, or cloud deployment.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Flogicapps-custom-code-net10-migration\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Flogicapps-custom-code-net10-migration\u002FSKILL.md",[148],{"categoryNo":149,"categoryName":150,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000030","开发工程师",[152,153,154,155,156,157,28,98,158,159,160,161,162,163,164,110,165,166,167,168],"custom","code","logicapps custom code net10 migration","migration","projects","standalone","deployment","applications","local","azure","worker","upgrades","package","apps","Legacy","debugging","workflows",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":173,"sourceHtmlUrl":174,"categories":175,"keywords":180,"publishedAt":43},"01M6AEBN37RQMXJ3298PEY7PG1","winmd-api-search","Find and explore Windows desktop APIs. Use when building features that need platform capabilities — camera, file access, notifications, UI controls, AI\u002FML, sensors, networking, etc. Discovers the right API for a task and retrieves full type details (methods, properties, events, enumeration values).","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fwinmd-api-search\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fwinmd-api-search\u002FSKILL.md",[176,179],{"categoryNo":177,"categoryName":178,"taxonomyType":88,"taxonomyTypeDesc":89},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":149,"categoryName":150,"taxonomyType":21,"taxonomyTypeDesc":22},[181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,99,26,196,197,198,199,28,200,201],"winmd api search","search","api","windows","desktop","file access","enumeration","ui","ml","file","networking","capabilities","notifications","explore","access","full","camera","events","platform","methods","controls",{"skillNo":203,"name":204,"nameZh":6,"description":205,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":206,"licenseFileUrl":207,"sourceHtmlUrl":208,"categories":209,"keywords":215,"publishedAt":43},"01M6AE571R3SC1ZGE202B3NB5S","resemble-detect","Deepfake detection and media safety — detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using Resemble AI","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fresemble-detect\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fresemble-detect\u002FSKILL.md",[210,211,214],{"categoryNo":177,"categoryName":178,"taxonomyType":88,"taxonomyTypeDesc":89},{"categoryNo":212,"categoryName":213,"taxonomyType":88,"taxonomyTypeDesc":89},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":91,"categoryName":92,"taxonomyType":88,"taxonomyTypeDesc":89},[216,125,190,217,218],"resemble detect","https","identity",{"skillNo":220,"name":221,"nameZh":6,"description":222,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":206,"licenseFileUrl":223,"sourceHtmlUrl":224,"categories":225,"keywords":230,"publishedAt":43},"01M6AE30BSK2HCY4TQQ22Z8JRD","quality-playbook","Run a complete quality engineering audit on any codebase. Derives behavioral requirements from the code, generates spec-traced functional tests, runs a three-pass code review with regression tests, executes a multi-model spec audit (Council of Three), and produces a consolidated bug report with TDD-verified patches. Finds the 35% of real defects that structural code review alone cannot catch. Works with any language. Trigger on 'quality playbook', 'spec audit', 'Council of Three', 'fitness-to-purpose', or 'coverage theater'.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fquality-playbook\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fquality-playbook\u002FSKILL.md",[226,229],{"categoryNo":227,"categoryName":228,"taxonomyType":88,"taxonomyTypeDesc":89},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":91,"categoryName":92,"taxonomyType":88,"taxonomyTypeDesc":89},[231,232,37,233,153,100,9,124,234,235,217,236,237,238,239,240,241,242,243,244,245,246,108,247,248,249,250],"playbook","quality playbook","code review","tdd","run","report","spec","bug report","council","engineering","functional","spec audit","bug","verified","multi-model","three","coverage","model","regression","structural"]