[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CBSY4A0MNR5NMPTS65755P":3,"skill-same:01M6CBSY4A0MNR5NMPTS65755P":87},{"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":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":14,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":60,"ownerDescription":61,"updatedAt":62,"install":63,"resources":76},"01M6CBSY4A0MNR5NMPTS65755P","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.","Gabriellmaia01\u002Fneuro-dose-assist","Gabriellmaia01","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F135177180?v=4","01M6CBRZEZC8G5C400AG3SPFNH","Gabriel","01M6CBRZK8MB7DWW0B4C82TV2G",0,"MIT","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\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],"product","ai product","integration","cost","use","question","prompt","ship","Production","Optimization","engineering","prompt engineering","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.\"\nsource: 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,"source":54,"description":7},"vibeship-spawner-skills (Apache 2.0)","main","eac60cc6e0b6baa2a05a765a851fb10638f4e733",".agent\u002Fskills\u002Fskills\u002Fai-product","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist","https:\u002F\u002Fgithub.com\u002FGabriellmaia01","https:\u002F\u002Fportfolio-bice-theta-81.vercel.app\u002F","💻 Desenvolvimento De Software_\r\n\r\n⚙️ Engenharia De Software\r\n","2026-11-12 08:51:23",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add Gabriellmaia01\u002Fneuro-dose-assist --skill ai-product",{"tool":69,"command":70},"bunx","bunx skills add Gabriellmaia01\u002Fneuro-dose-assist --skill ai-product",{"tool":72,"command":73},"pnpm","pnpm dlx skills add Gabriellmaia01\u002Fneuro-dose-assist --skill ai-product","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fai-product\u002FSKILL.md",true,[77,83],{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"SOURCE.md","REFERENCE","参考文档",180,"https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fdefi\u002Fcurve-finance-agent\u002FSOURCE.md",{"path":84,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":85,"sourceHtmlUrl":86},"TRUST.auto.yaml",2217,"https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fdefi\u002Fcurve-finance-agent\u002FTRUST.auto.yaml",[88,107,137,155,166,186],{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":95,"publishedAt":49},"01M6CCFWA9JJ23708FSKK1QNBW","zapier-make-patterns","No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity - these platforms have their own patterns, pitfalls, and breaking points.  This skill covers when to use which platform, how to build reliable automations, and when to graduate to code-based solutions. Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fzapier-make-patterns\u002FSKILL.md",[94],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[96,97,98,99,45,43,100,101,102,103,33,104,105,106],"zapier make patterns","writing","workflow","automation","how","automate","integrations","business","simplicity","power","complexity",{"skillNo":108,"name":109,"nameZh":6,"description":110,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":111,"categories":112,"keywords":119,"publishedAt":49},"01M6CCFSBPM49S618W7G4NHH13","Cross-Site Scripting and HTML Injection Testing","This skill should be used when the user asks to \"test for XSS vulnerabilities\", \"perform cross-site scripting attacks\", \"identify HTML injection flaws\", \"exploit client-side injection vulnerabilities\", \"steal cookies via XSS\", or \"bypass content security policies\". It provides comprehensive techniques for detecting, exploiting, and understanding XSS and HTML injection attack vectors in web applications.","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fxss-html-injection\u002FSKILL.md",[113,116],{"categoryNo":114,"categoryName":115,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":117,"categoryName":118,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136],"injection","scripting","html","site","Cross Site Scripting and HTML Injection Testing","testing","html injection testing","cookies","applications","xss","exploit","content","web","test","vulnerabilities","user","security",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":150,"publishedAt":49},"01M6CCFJ9XTPXHTE4JR06WY475","writing-skills","Use when creating new skills, editing existing skills, or verifying skills work before deployment","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fwriting-skills\u002FSKILL.md",[143,144,147],{"categoryNo":114,"categoryName":115,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":145,"categoryName":146,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":148,"categoryName":149,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000036","内容创作者",[97,151,152,153,154,33],"writing skills","deployment","work","editing",{"skillNo":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":163,"publishedAt":49},"01M6CCFG98EMZZ43D1CPZZZ54E","writing-plans","Use when you have a spec or requirements for a multi-step task, before touching code","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fwriting-plans\u002FSKILL.md",[161,162],{"categoryNo":145,"categoryName":146,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":148,"categoryName":149,"taxonomyType":26,"taxonomyTypeDesc":27},[97,164,165,45,33],"writing plans","spec",{"skillNo":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":170,"categories":171,"keywords":173,"publishedAt":49},"01M6CCFDPACVC9ZM8TN63MGBP5","workflow-automation","Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off.  This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation.  Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fworkflow-automation\u002FSKILL.md",[172],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[174,99,98,175,176,177,178,179,43,180,37,181,182,183,184,185],"Workflow Automation","network","ai agents","accessibility","workflows","infrastructure","flow","execution","inngest","parallel","orchestrator","resume",{"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":193,"publishedAt":49},"01M6CCFB0KAB0PPNV38XQPX6YX","WordPress Penetration Testing","This skill should be used when the user asks to \"pentest WordPress sites\", \"scan WordPress for vulnerabilities\", \"enumerate WordPress users, themes, or plugins\", \"exploit WordPress vulnerabilities\", or \"use WPScan\". It provides comprehensive WordPress security assessment methodologies.","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fwordpress-penetration-testing\u002FSKILL.md",[192],{"categoryNo":114,"categoryName":115,"taxonomyType":21,"taxonomyTypeDesc":22},[125,188,194,195,136,134,196,197,135,33,130,198,199],"penetration testing","wordpress","assessment","plugins","pentest","security assessment"]