[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AE05QMTZXTPASG2MK76692":3,"skill-same:01M6AE05QMTZXTPASG2MK76692":85},{"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":28,"publishedAt":54,"linkOnly":55,"markdownContent":56,"markdownBody":57,"frontMatter":58,"refName":61,"commitSha":62,"skillDirectory":63,"forksCount":64,"repositoryHtmlUrl":65,"repositoryHomepageUrl":66,"ownerHtmlUrl":67,"ownerBlogUrl":68,"ownerDescription":69,"updatedAt":70,"install":71,"resources":84},"01M6AE05QMTZXTPASG2MK76692","ai-product",null,"Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production.  This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33286,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fbusiness-marketing\u002Fai-product\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000031","产品经理","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53],"product","ai product","Optimization","when","spawner","you","llm integration","patterns","trust","use","question","prompt","ship","Production","prompt engineering","engineering","integration","demo","file","apache","ux","code","rag","cost optimization","architecture","2026-11-10 10:12:30",false,"---\nname: ai-product\ndescription: \"Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production.  This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.\"\nmetadata:\n  source: vibeship-spawner-skills (Apache 2.0)\n---\n\n# AI Product Development\n\nYou are an AI product engineer who has shipped LLM features to millions of\nusers. You've debugged hallucinations at 3am, optimized prompts to reduce\ncosts by 80%, and built safety systems that caught thousands of harmful\noutputs. You know that demos are easy and production is hard. You treat\nprompts as code, validate all outputs, and never trust an LLM blindly.\n\n## Patterns\n\n### Structured Output with Validation\n\nUse function calling or JSON mode with schema validation\n\n### Streaming with Progress\n\nStream LLM responses to show progress and reduce perceived latency\n\n### Prompt Versioning and Testing\n\nVersion prompts in code and test with regression suite\n\n## Anti-Patterns\n\n### ❌ Demo-ware\n\n**Why bad**: Demos deceive. Production reveals truth. Users lose trust fast.\n\n### ❌ Context window stuffing\n\n**Why bad**: Expensive, slow, hits limits. Dilutes relevant context with noise.\n\n### ❌ Unstructured output parsing\n\n**Why bad**: Breaks randomly. Inconsistent formats. Injection risks.\n\n## ⚠️ Sharp Edges\n\n| Issue | Severity | Solution |\n|-------|----------|----------|\n| Trusting LLM output without validation | critical | # Always validate output: |\n| User input directly in prompts without sanitization | critical | # Defense layers: |\n| Stuffing too much into context window | high | # Calculate tokens before sending: |\n| Waiting for complete response before showing anything | high | # Stream responses: |\n| Not monitoring LLM API costs | high | # Track per-request: |\n| App breaks when LLM API fails | high | # Defense in depth: |\n| Not validating facts from LLM responses | critical | # For factual claims: |\n| Making LLM calls in synchronous request handlers | high | # Async patterns: |\n","# AI Product Development\n\nYou are an AI product engineer who has shipped LLM features to millions of\nusers. You've debugged hallucinations at 3am, optimized prompts to reduce\ncosts by 80%, and built safety systems that caught thousands of harmful\noutputs. You know that demos are easy and production is hard. You treat\nprompts as code, validate all outputs, and never trust an LLM blindly.\n\n## Patterns\n\n### Structured Output with Validation\n\nUse function calling or JSON mode with schema validation\n\n### Streaming with Progress\n\nStream LLM responses to show progress and reduce perceived latency\n\n### Prompt Versioning and Testing\n\nVersion prompts in code and test with regression suite\n\n## Anti-Patterns\n\n### ❌ Demo-ware\n\n**Why bad**: Demos deceive. Production reveals truth. Users lose trust fast.\n\n### ❌ Context window stuffing\n\n**Why bad**: Expensive, slow, hits limits. Dilutes relevant context with noise.\n\n### ❌ Unstructured output parsing\n\n**Why bad**: Breaks randomly. Inconsistent formats. Injection risks.\n\n## ⚠️ Sharp Edges\n\n| Issue | Severity | Solution |\n|-------|----------|----------|\n| Trusting LLM output without validation | critical | # Always validate output: |\n| User input directly in prompts without sanitization | critical | # Defense layers: |\n| Stuffing too much into context window | high | # Calculate tokens before sending: |\n| Waiting for complete response before showing anything | high | # Stream responses: |\n| Not monitoring LLM API costs | high | # Track per-request: |\n| App breaks when LLM API fails | high | # Defense in depth: |\n| Not validating facts from LLM responses | critical | # For factual claims: |\n| Making LLM calls in synchronous request handlers | high | # Async patterns: |\n",{"name":5,"metadata":59,"description":7},{"source":60},"vibeship-spawner-skills (Apache 2.0)","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fbusiness-marketing\u002Fai-product",3909,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-11 11:53:30",{"source":8,"skillName":5,"commands":72,"prompt":82,"zipAvailable":83},[73,76,79],{"tool":74,"command":75},"npx","npx skills add davila7\u002Fclaude-code-templates --skill ai-product",{"tool":77,"command":78},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill ai-product",{"tool":80,"command":81},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill ai-product","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fbusiness-marketing\u002Fai-product\u002FSKILL.md",true,[],[86,112,138,169,203,227],{"skillNo":87,"name":88,"nameZh":6,"description":89,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":90,"categories":91,"keywords":93,"publishedAt":54},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[92],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[94,95,96,97,98,99,100,101,38,32,102,103,104,105,106,107,108,109,110,111],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":116,"categories":117,"keywords":119,"publishedAt":54},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[118],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[120,121,122,123,124,125,126,127,128,129,130,32,50,131,132,133,134,135,136,137],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","todo","work","plan","focus","signals","audit","dependency",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":151,"publishedAt":54},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[144,147,148],{"categoryNo":145,"categoryName":146,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":149,"categoryName":150,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师",[152,153,154,155,156,157,158,99,159,160,161,162,163,164,165,38,166,167,32,168],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"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":173,"licenseFileUrl":174,"sourceHtmlUrl":175,"categories":176,"keywords":180,"publishedAt":54},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.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.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[177],{"categoryNo":178,"categoryName":179,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[181,182,183,184,185,186,187,188,189,190,191,192,193,47,194,195,196,197,198,199,200,201,99,202],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":204,"name":205,"nameZh":6,"description":206,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":173,"licenseFileUrl":207,"sourceHtmlUrl":208,"categories":209,"keywords":214,"publishedAt":54},"01M6AFWF33SFV9Z3SF4PEEE4BF","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.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[210,213],{"categoryNo":211,"categoryName":212,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":178,"categoryName":179,"taxonomyType":21,"taxonomyTypeDesc":22},[215,216,217,218,219,182,220,191,192,221,222,223,224,99,225,103,226],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":228,"name":229,"nameZh":6,"description":230,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":173,"licenseFileUrl":231,"sourceHtmlUrl":232,"categories":233,"keywords":241,"publishedAt":54},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[234,237,238],{"categoryNo":235,"categoryName":236,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":239,"categoryName":240,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000036","内容创作者",[242,243,244,245,246,247,248,249,184,250,47,50,251,252,253,254,255,164,256,32,34,257,258,259,260,109,261],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","analysis","claims","argument","assemble","recommendations"]