[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6K3SD7SCRAX6YJ3ARXA95F5":3,"skill-same:01M6K3SD7SCRAX6YJ3ARXA95F5":71},{"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":6,"ownerHtmlUrl":53,"ownerBlogUrl":54,"ownerDescription":55,"updatedAt":56,"install":57,"resources":70},"01M6K3SD7SCRAX6YJ3ARXA95F5","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.","anjijava16\u002FAgent_Skills","anjijava16","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F5849522?v=4","01M6JZVCV62H56AYM59D11EGAW","Anjaiah Methuku","01M6JZVD04H0QP910VNCZMJFAG",0,"MIT","https:\u002F\u002Fgithub.com\u002Fanjijava16\u002FAgent_Skills\u002Fblob\u002Fdevelop\u002F.github\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},"develop","d85a5e71a5e3e82038a005e5dfa50b6a78cafa7e",".github\u002Fskills\u002Fprd",1,"https:\u002F\u002Fgithub.com\u002Fanjijava16\u002FAgent_Skills","https:\u002F\u002Fgithub.com\u002Fanjijava16","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fanjaiahmethuku\u002F","AI & Data Engineer ■ Real-time Streaming ■ BigData ■ Machine Learning ■ CLOUD ■ JAVA ■ PYTHON ■ Blog ■  Remain Curious And Keep Learning .....","2026-10-08 08:03:12",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add anjijava16\u002FAgent_Skills --skill prd",{"tool":63,"command":64},"bunx","bunx skills add anjijava16\u002FAgent_Skills --skill prd",{"tool":66,"command":67},"pnpm","pnpm dlx skills add anjijava16\u002FAgent_Skills --skill prd","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fanjijava16\u002FAgent_Skills\u002Fblob\u002Fdevelop\u002F.github\u002Fskills\u002Fprd\u002FSKILL.md",true,[],[72,91,121,140,157,186],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":78,"publishedAt":43},"01M6K52P5M72CRGAHJTVAWSNRA","roll-dice","Roll dice using a random number generator. Use when asked to roll a die (d4, d6, d8, d10, d12, d20, d100), roll multiple dice, or generate random dice rolls for tabletop RPGs, board games, or decision making.","https:\u002F\u002Fgithub.com\u002Fanjijava16\u002FAgent_Skills\u002Fblob\u002Fdevelop\u002Froll-dice\u002FSKILL.md",[],[79,80,81,41,82,83,84,85,86,87,88,89,90,28],"roll dice","dice","welcome","tabletop","decision","board","use","decision making","random","games","generator","number",{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":102,"publishedAt":43},"01M6K52JJKRBM0A5EXT12G87RW","pdf-service","Extract text and tables from PDFs, fill PDF forms, merge and split PDF files, and convert PDFs to other formats. Use when the user mentions PDFs, document extraction, form filling, or needs to manipulate PDF files.","https:\u002F\u002Fgithub.com\u002Fanjijava16\u002FAgent_Skills\u002Fblob\u002Fdevelop\u002Fpdf-service\u002FSKILL.md",[97],{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",[103,104,105,106,107,81,108,109,110,111,112,85,113,114,115,116,117,118,119,120],"PDF","service","pdf service","python","merge","extraction","convert","text","user","extract","files","document","forms","form","formats","split","pip","fill",{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":128,"publishedAt":43},"01M6K52EVG86E60AQFMJAZ1G09","pdf-processing","Extract text, tables, and metadata from PDF documents. Parse scanned PDFs with OCR, analyze PDF structure, and convert PDFs to other formats. Use when the user needs to read, parse, or extract content from PDF files, even if they just say \"get the text from this document.\"","https:\u002F\u002Fgithub.com\u002Fanjijava16\u002FAgent_Skills\u002Fblob\u002Fdevelop\u002Fpdf-processing\u002FSKILL.md",[127],{"categoryNo":98,"categoryName":99,"taxonomyType":100,"taxonomyTypeDesc":101},[103,129,130,106,34,131,109,132,110,133,134,85,135,113,114,136,137,138,139,119],"pdf processing","processing","ocr","analyze","just","read","say","structure","get","tesseract","with",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":149,"publishedAt":43},"01M6K52A7EC35DTS48PP0VX5TB","my-skill","A template skill for [describe task]. Use when the user asks to [trigger phrases].","https:\u002F\u002Fgithub.com\u002Fanjijava16\u002FAgent_Skills\u002Fblob\u002Fdevelop\u002Fmy-skill\u002FSKILL.md",[146],{"categoryNo":147,"categoryName":148,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000023","Skill与智能体开发",[150,151,152,153,111,85,154,155,156,28],"my skill","my","template skill","template","task","trigger","name",{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":161,"categories":162,"keywords":172,"publishedAt":43},"01M6K51Y5QVHMDX3F01JJRRV33","data-analysis","Analyze datasets — compute summary statistics, clean messy data, add derived columns, detect outliers, and generate charts. Use when the user has a CSV, TSV, Excel, or JSON data file and wants to explore, transform, visualize, or understand the data, even if they don't explicitly mention \"analysis.\"","https:\u002F\u002Fgithub.com\u002Fanjijava16\u002FAgent_Skills\u002Fblob\u002Fdevelop\u002Fdata-analysis\u002FSKILL.md",[163,166,169],{"categoryNo":164,"categoryName":165,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000004","电子表格",{"categoryNo":167,"categoryName":168,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":170,"categoryName":171,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师",[173,174,38,175,106,176,81,41,177,178,179,180,132,85,181,182,183,184,185,119],"data analysis","data","xlsx","visualize","file","csv","Charts","explore","clean","statistics","add","transform","columns",{"skillNo":187,"name":188,"nameZh":6,"description":189,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":190,"categories":191,"keywords":198,"publishedAt":43},"01M6K51P73MWRNWFM80SYVBST0","code-review","Review code for bugs, security vulnerabilities, performance issues, and style problems. Provide actionable feedback with severity levels and fix suggestions. Use when the user asks to review, audit, or check code quality, or wants a second pair of eyes on a pull request, diff, or code change.","https:\u002F\u002Fgithub.com\u002Fanjijava16\u002FAgent_Skills\u002Fblob\u002Fdevelop\u002Fcode-review\u002FSKILL.md",[192,195],{"categoryNo":193,"categoryName":194,"taxonomyType":100,"taxonomyTypeDesc":101},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":196,"categoryName":197,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000030","开发工程师",[199,200,201,202,203,204,205,206,207,208,209,210,211,85,212,213,214,139,28,215],"code review","review","code","performance","change","code quality","issues","pull","audit","severity","feedback","check","fix","diff","style","actionable","bugs"]