[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71Q99BJZDYA7N58CT5HGX2D":3,"skill-same:01M71Q99BJZDYA7N58CT5HGX2D":67},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":42,"linkOnly":43,"markdownContent":44,"markdownBody":45,"frontMatter":46,"refName":47,"commitSha":48,"skillDirectory":49,"forksCount":13,"repositoryHtmlUrl":50,"repositoryHomepageUrl":6,"ownerHtmlUrl":51,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":52,"install":53,"resources":66},"01M71Q99BJZDYA7N58CT5HGX2D","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.","Thamielis\u002F.agents","Thamielis","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F52717133?v=4","01M71NG040Z4RAKTDW6JVQTZA0","01M71NG04N9WFE1H3RH0BF6VDY",0,"MIT","https:\u002F\u002Fgithub.com\u002FThamielis\u002F.agents\u002Fblob\u002Fmain\u002Fskills\u002Fprd\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000031","产品经理","PROFESSION","适用职业",[5,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"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":14,"description":7},"main","dd673a28ba66191245f0e384c525b1dcff1c427a","skills\u002Fprd","https:\u002F\u002Fgithub.com\u002FThamielis\u002F.agents","https:\u002F\u002Fgithub.com\u002FThamielis","2026-06-08 04:03:12",{"source":8,"skillName":5,"commands":54,"prompt":64,"zipAvailable":65},[55,58,61],{"tool":56,"command":57},"npx","npx skills add Thamielis\u002F.agents --skill prd",{"tool":59,"command":60},"bunx","bunx skills add Thamielis\u002F.agents --skill prd",{"tool":62,"command":63},"pnpm","pnpm dlx skills add Thamielis\u002F.agents --skill prd","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FThamielis\u002F.agents\u002Fblob\u002Fmain\u002Fskills\u002Fprd\u002FSKILL.md",true,[],[68,101,127,155,186,211],{"skillNo":69,"name":70,"nameZh":6,"description":71,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":72,"licenseFileUrl":73,"sourceHtmlUrl":74,"categories":75,"keywords":84,"publishedAt":42},"01M71QFD7835D0JG8MS43NRRVE","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FThamielis\u002F.agents\u002Fblob\u002Fmain\u002Fskills\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FThamielis\u002F.agents\u002Fblob\u002Fmain\u002Fskills\u002Fwebapp-testing\u002FSKILL.md",[76,81],{"categoryNo":77,"categoryName":78,"taxonomyType":79,"taxonomyTypeDesc":80},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":82,"categoryName":83,"taxonomyType":79,"taxonomyTypeDesc":80},"01JBSEEDCAT000000000000009","前端开发",[85,86,87,88,89,90,91,92,93,94,40,95,96,97,98,99,100],"webapp","testing","webapp testing","local","frontend","debugging","logs","browser","ui","screenshots","applications","toolkit","with","playwright","web","behavior",{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":72,"licenseFileUrl":105,"sourceHtmlUrl":106,"categories":107,"keywords":109,"publishedAt":42},"01M71QF1RVYQPZ2HPHXYWBC27K","web-artifacts-builder","Suite of tools for creating elaborate, multi-component Codex.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","https:\u002F\u002Fgithub.com\u002FThamielis\u002F.agents\u002Fblob\u002Fmain\u002Fskills\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FThamielis\u002F.agents\u002Fblob\u002Fmain\u002Fskills\u002Fweb-artifacts-builder\u002FSKILL.md",[108],{"categoryNo":82,"categoryName":83,"taxonomyType":79,"taxonomyTypeDesc":80},[110,111,112,99,113,114,115,40,116,117,89,118,119,120,121,122,123,124,125,126],"builder","artifacts builder","artifacts","web artifacts builder","single-file","css","Tailwind","ui components","single","use","file","components","react","multi","state","routing","state management",{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":131,"categories":132,"keywords":137,"publishedAt":42},"01M71QEMXGG7MTTMSRVE3YFCXC","visual-explainer","Generate beautiful, self-contained HTML pages that visually explain systems, code changes, plans, and data. Use when the user asks for a diagram, architecture overview, diff review, plan review, project recap, comparison table, or any visual explanation of technical concepts. Also use proactively when you are about to render a complex ASCII table (4+ rows or 3+ columns) — present it as a styled HTML page instead.","https:\u002F\u002Fgithub.com\u002FThamielis\u002F.agents\u002Fblob\u002Fmain\u002Fskills\u002Fvisual-explainer\u002FSKILL.md",[133,134],{"categoryNo":82,"categoryName":83,"taxonomyType":79,"taxonomyTypeDesc":80},{"categoryNo":135,"categoryName":136,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000033","数据分析师",[138,139,140,141,142,143,144,145,146,32,147,148,149,150,151,40,152,153,154,119],"visual","visual explainer","explainer","project","files","image","you","architecture","plan","present","concepts","table","as","view","ascii table","ascii","surf",{"skillNo":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":167,"publishedAt":42},"01M71QDJVMVM82514TRK7VHXC5","transloadit-media-processing","Process media files (video, audio, images, documents) using Transloadit. Use when asked to encode video to HLS\u002FMP4, generate thumbnails, resize or watermark images, extract audio, concatenate clips, add subtitles, OCR documents, or run any media processing pipeline. Covers 86+ processing robots for file transformation at scale.","https:\u002F\u002Fgithub.com\u002FThamielis\u002F.agents\u002Fblob\u002Fmain\u002Fskills\u002Ftransloadit-media-processing\u002FSKILL.md",[161,164],{"categoryNo":162,"categoryName":163,"taxonomyType":79,"taxonomyTypeDesc":80},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":165,"categoryName":166,"taxonomyType":79,"taxonomyTypeDesc":80},"01JBSEEDCAT000000000000023","Skill与智能体开发",[168,169,170,171,172,173,174,175,176,177,142,178,179,38,180,181,182,183,184,185],"Media","processing","transloadit media processing","media processing","transloadit","images","Video","ocr","Audio","pipeline","MCP","free","when","mcp server","thumbnails","server","mcp-server","cli",{"skillNo":187,"name":188,"nameZh":6,"description":189,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":72,"licenseFileUrl":190,"sourceHtmlUrl":191,"categories":192,"keywords":196,"publishedAt":42},"01M71QDEJ4M9TP6VZRNK3J9NKX","theme-factory","Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors\u002Ffonts that you can apply to any artifact that has been creating, or can generate a new theme on-the-fly.","https:\u002F\u002Fgithub.com\u002FThamielis\u002F.agents\u002Fblob\u002Fmain\u002Fskills\u002Ftheme-factory\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FThamielis\u002F.agents\u002Fblob\u002Fmain\u002Fskills\u002Ftheme-factory\u002FSKILL.md",[193],{"categoryNo":194,"categoryName":195,"taxonomyType":79,"taxonomyTypeDesc":80},"01JBSEEDCAT000000000000015","视觉设计",[197,198,199,200,201,40,202,203,204,205,144,112,206,96,207,208,97,209,32,210],"theme","theme factory","factory","SET","slides","landing","pages","new","themes","apply","can","fonts","artifact","fly",{"skillNo":212,"name":213,"nameZh":6,"description":214,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":215,"categories":216,"keywords":220,"publishedAt":42},"01M71QD9QXM9M9RWQX40E981D4","terraform-azurerm-set-diff-analyzer","Analyze Terraform plan JSON output for AzureRM Provider to distinguish between false-positive diffs (order-only changes in Set-type attributes) and actual resource changes. Use when reviewing terraform plan output for Azure resources like Application Gateway, Load Balancer, Firewall, Front Door, NSG, and other resources with Set-type attributes that cause spurious diffs due to internal ordering changes.","https:\u002F\u002Fgithub.com\u002FThamielis\u002F.agents\u002Fblob\u002Fmain\u002Fskills\u002Fterraform-azurerm-set-diff-analyzer\u002FSKILL.md",[217],{"categoryNo":218,"categoryName":219,"taxonomyType":79,"taxonomyTypeDesc":80},"01JBSEEDCAT000000000000011","运维与部署",[200,221,222,223,224,225,226,227,228,40,229,230,231,232,233,234,235,236,119,237],"diff","terraform azurerm set diff analyzer","analyzer","terraform","gateway","firewall","resources","attributes","provider","resource changes","balancer","front","azure","diffs","load balancer","only","load"]