[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71V7TYJZ1C5XD0RRH2P340K":3,"skill-same:01M71V7TYJZ1C5XD0RRH2P340K":68},{"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":14,"repositoryHtmlUrl":51,"repositoryHomepageUrl":6,"ownerHtmlUrl":52,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":53,"install":54,"resources":67},"01M71V7TYJZ1C5XD0RRH2P340K","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.","TorchiaHub\u002FSkills","TorchiaHub","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F125223386?v=4","01M71Q3SNKHMKJYN4HGMSQ2CEY","Mattia Torchia","01M71Q3SP6T1Z9NBADBCFP7GM5",0,"MIT","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\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],"product","Product Requirements","user stories","software","systems","features","quality","risk analysis","specifications","generate","high","summaries","stories","analysis","requirements","documents","technical","for","risk","2026-11-10 13:02:02",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},"master","e3489bc4e504668e9a2ff8fb73dc086da71328ab","01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fprd","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills","https:\u002F\u002Fgithub.com\u002FTorchiaHub","2026-06-21 20:40:46",{"source":8,"skillName":5,"commands":55,"prompt":65,"zipAvailable":66},[56,59,62],{"tool":57,"command":58},"npx","npx skills add TorchiaHub\u002FSkills --skill prd",{"tool":60,"command":61},"bunx","bunx skills add TorchiaHub\u002FSkills --skill prd",{"tool":63,"command":64},"pnpm","pnpm dlx skills add TorchiaHub\u002FSkills --skill prd","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fprd\u002FSKILL.md",true,[],[69,106,136,165,189,210],{"skillNo":70,"name":71,"nameZh":6,"description":72,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":73,"licenseFileUrl":74,"sourceHtmlUrl":75,"categories":76,"keywords":85,"publishedAt":43},"01M71VC09WZH7EYAA8YDKJQ0E1","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\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fresemble-detect\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fresemble-detect\u002FSKILL.md",[77,82],{"categoryNo":78,"categoryName":79,"taxonomyType":80,"taxonomyTypeDesc":81},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":83,"categoryName":84,"taxonomyType":80,"taxonomyTypeDesc":81},"01JBSEEDCAT000000000000016","图像与多媒体",[86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105],"resemble detect","detect","images","Video","SET","app","sources","Media","Audio","detect ai","local","generated","ai api","api","analyze","text","paths","Safety","intelligence","deepfake detection",{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":110,"categories":111,"keywords":118,"publishedAt":43},"01M71VBN3ZPGZT29DK7N7VCT4P","refactor","Surgical code refactoring to improve maintainability without changing behavior. Covers extracting functions, renaming variables, breaking down god functions, improving type safety, eliminating code smells, and applying design patterns. Less drastic than repo-rebuilder; use for gradual improvements.","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Frefactor\u002FSKILL.md",[112,115],{"categoryNo":113,"categoryName":114,"taxonomyType":80,"taxonomyTypeDesc":81},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":116,"categoryName":117,"taxonomyType":80,"taxonomyTypeDesc":81},"01JBSEEDCAT000000000000015","视觉设计",[108,119,120,121,122,123,124,125,126,103,127,128,129,130,41,131,132,133,134,135],"design patterns","extracting","improve","design","Variables","code","refactoring","Type Safety","patterns","surgical","code refactoring","down","repo","use","type","functions","behavior",{"skillNo":137,"name":138,"nameZh":6,"description":139,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":140,"sourceHtmlUrl":141,"categories":142,"keywords":147,"publishedAt":43},"01M71VA75R4MGJXGR0JS727PBV","quality-playbook","Explore any codebase from scratch and generate six quality artifacts: a quality constitution (QUALITY.md), spec-traced functional tests, a code review protocol with regression test generation, an integration testing protocol, a multi-model spec audit (Council of Three), and an AI bootstrap file (AGENTS.md). Includes state machine completeness analysis and missing safeguard detection. Works with any language (Python, Java, Scala, TypeScript, Go, Rust, etc.). Use this skill whenever the user asks to set up a quality playbook, generate functional tests from specifications, create a quality constitution, build testing protocols, audit code against specs, or establish a repeatable quality system for a project. Also trigger when the user mentions 'quality playbook', 'spec audit', 'Council of Three', 'fitness-to-purpose', 'coverage theater', or wants to go beyond basic test generation to build a full quality system grounded in their actual codebase.","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fquality-playbook\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fquality-playbook\u002FSKILL.md",[143,144],{"categoryNo":113,"categoryName":114,"taxonomyType":80,"taxonomyTypeDesc":81},{"categoryNo":145,"categoryName":146,"taxonomyType":80,"taxonomyTypeDesc":81},"01JBSEEDCAT000000000000008","测试与质量",[148,149,30,150,151,152,153,154,155,156,33,157,158,159,160,161,162,163,41,164],"playbook","quality playbook","project","code review","integration testing","explore","testing","create","functional","spec audit","typescript","go","state machine","full","multi-model","three","system",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":169,"categories":170,"keywords":171,"publishedAt":43},"01M71V7JFHSJ61847CEY1EM48A","power-platform-architect","Use this skill when the user needs to transform business requirements, use case descriptions, or meeting transcripts into a technical Power Platform solution architecture, including component selection and Mermaid.js diagrams.","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fpower-platform-architect\u002FSKILL.md",[],[172,173,174,175,176,177,178,40,132,179,180,38,181,182,183,184,185,186,187,188],"power","platform","power platform","architect","power platform architect","diagrams","selection","business","when","architecture","needs","case","solution architecture","component","solution","mermaid","meeting",{"skillNo":190,"name":191,"nameZh":6,"description":192,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":193,"categories":194,"keywords":195,"publishedAt":43},"01M71V65KR84TMS1WARE3R11VZ","plantuml-ascii","Generate ASCII art diagrams using PlantUML text mode. Use when user asks to create ASCII diagrams, text-based diagrams, terminal-friendly diagrams, or mentions plantuml ascii, text diagram, ascii art diagram. Supports: Converting PlantUML diagrams to ASCII art, Creating sequence diagrams, class diagrams, flowcharts in ASCII format, Generating Unicode-enhanced ASCII art with -utxt flag","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fplantuml-ascii\u002FSKILL.md",[],[196,197,198,177,199,200,201,180,202,101,155,203,33,204,205,206,207,208,209,132],"plantuml","ascii","plantuml ascii","Unicode","mode","Art","Bash","diagram","terminal","read","write","sequence","plantuml diagrams","ascii art",{"skillNo":211,"name":212,"nameZh":6,"description":213,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":73,"licenseFileUrl":6,"sourceHtmlUrl":214,"categories":215,"keywords":216,"publishedAt":43},"01M71V5WJ2Q6SRVV7Q4JXCR8EM","phoenix-tracing","OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.","https:\u002F\u002Fgithub.com\u002FTorchiaHub\u002FSkills\u002Fblob\u002Fmaster\u002F01-Inbox\u002Fdownloads\u002Fgithub-awesome-copilot\u002F20260429T140243Z\u002Fsource\u002Fskills\u002Fphoenix-tracing\u002FSKILL.md",[],[217,218,219,220,41,221,222,223,224,225,132,180,226,158,227,228,229,230,231,232],"phoenix tracing","tracing","Phoenix","observability","deploying to production","otel","python","implementing","custom","server","conventions","Production","instrumentation","oss","semantic","Arize"]