[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADRMS3DB4H0T30DCVR4STA":3,"skill-same:01M6ADRMS3DB4H0T30DCVR4STA":88},{"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":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":64,"commitSha":65,"skillDirectory":66,"forksCount":67,"repositoryHtmlUrl":68,"repositoryHomepageUrl":69,"ownerHtmlUrl":70,"ownerBlogUrl":71,"ownerDescription":72,"updatedAt":73,"install":74,"resources":87},"01M6ADRMS3DB4H0T30DCVR4STA","llm-ops",null,"LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.","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\u002Fai-research\u002Fllm-ops\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"ops","llm ops","2026","cli","de","db","vector databases","vector-db","community","evals","databases","prompt","tuning","Fine-Tuning","engineering","embeddings","vector","code","rag","prompt engineering","codex","antigravity","gemini","safe","2026-11-10 10:12:30",false,"---\nname: llm-ops\ndescription: \"LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.\"\nmetadata:\n  risk: safe\n  source: community\n  date_added: '2026-03-06'\n  author: renat\n  tags:\n  - llm\n  - rag\n  - embeddings\n  - vector-db\n  - fine-tuning\n  tools:\n  - claude-code\n  - antigravity\n  - cursor\n  - gemini-cli\n  - codex-cli\n---\n\n# LLM-OPS -- IA de Producao\n\n## Overview\n\nLLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao. Ativar para: implementar RAG, criar pipeline de embeddings, Pinecone\u002FChroma\u002Fpgvector, fine-tuning, prompt engineering, reducao de custos de LLM, evals, cache semantico, streaming, agents.\n\n## When to Use This Skill\n\n- When you need specialized assistance with this domain\n\n## Do Not Use This Skill When\n\n- The task is unrelated to llm ops\n- A simpler, more specific tool can handle the request\n- The user needs general-purpose assistance without domain expertise\n\n## How It Works\n\n> A diferenca entre um prototipo de IA e um produto de IA e operabilidade.\n> LLM-Ops e a engenharia que torna IA confiavel, escalavel e economica.\n\n---\n\n## Arquitetura Rag Completa\n\n[Documentos] -> [Chunking] -> [Embeddings] -> [Vector DB]\n                                                      |\n    [Query] -> [Embed query] -> [Semantic Search] -> [Top K chunks]\n                                                          |\n                                           [LLM + Context] -> [Resposta]\n\n## Pipeline De Indexacao\n\nfrom anthropic import Anthropic\n    import chromadb\n\n    client = Anthropic()\n    chroma = chromadb.PersistentClient(path=\".\u002Fchroma_db\")\n\n    def chunk_text(text, chunk_size=500, overlap=50):\n        words = text.split()\n        chunks = []\n        for i in range(0, len(words), chunk_size - overlap):\n            chunk = \" \".join(words[i:i + chunk_size])\n            if chunk: chunks.append(chunk)\n        return chunks\n\n    def index_document(doc_id, content_text, metadata=None):\n        chunks = chunk_text(content_text)\n        ids = [f\"{doc_id}_chunk_{i}\" for i in range(len(chunks))]\n        collection.upsert(ids=ids, documents=chunks)\n        return len(chunks)\n\n## Pipeline De Query Com Rag\n\ndef rag_query(query, top_k=5, system=None):\n        results = collection.query(\n            query_texts=[query], n_results=top_k,\n            include=[\"documents\", \"metadatas\", \"distances\"])\n        context_parts = []\n        for doc, meta, dist in zip(results[\"documents\"][0],\n                                    results[\"metadatas\"][0],\n                                    results[\"distances\"][0]):\n            if dist \u003C 1.5:\n                src = meta.get(\"source\", \"doc\")\n                context_parts.append(f\"[Fonte: {src}]\n{doc}\")\n        context = \"\n\n---\n\n\".join(context_parts)\n        response = client.messages.create(\n            model=\"claude-opus-4-20250805\", max_tokens=1024,\n            system=system or \"Responda baseado no contexto.\",\n            messages=[{\"role\": \"user\", \"content\": f\"Contexto:\n{context}\n\n{query}\"}])\n        return response.content[0].text\n\n---\n\n## Escolha Do Vector Db\n\n| DB | Melhor Para | Hosting | Custo |\n|----|------------|---------|-------|\n| Chroma | Desenvolvimento, local | Self-hosted | Gratis |\n| pgvector | Ja usa PostgreSQL | Self\u002FCloud | Gratis |\n| Pinecone | Producao gerenciada | Cloud | USD 70+\u002Fmes |\n| Weaviate | Multi-modal | Self\u002FCloud | Gratis+ |\n| Qdrant | Alta performance | Self\u002FCloud | Gratis+ |\n\n## Pgvector\n\nCREATE EXTENSION IF NOT EXISTS vector;\n    CREATE TABLE knowledge_embeddings (\n        id UUID PRIMARY KEY DEFAULT gen_random_uuid(),\n        content TEXT NOT NULL,\n        embedding vector(1536),\n        metadata JSONB,\n        created_at TIMESTAMPTZ DEFAULT NOW()\n    );\n    CREATE INDEX ON knowledge_embeddings\n    USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);\n    SELECT content, 1 - (embedding \u003C=> QUERY_VECTOR) AS similarity\n    FROM knowledge_embeddings ORDER BY similarity DESC LIMIT 5;\n\n---\n\n## Estrutura De Prompt De Elite\n\nComponentes do system prompt Auri:\n\n- Identidade: Nome (Auri), Tom (Natural, caloroso, direto), Plataforma (Amazon Alexa)\n- Regras: Maximo 3 paragrafos curtos, sem markdown, linguagem conversacional\n- Capacidades: analise de negocios, conselho baseado em dados, criatividade\n- Limitacoes: sem internet tempo real, sem transacoes financeiras\n- Personalizacao: {user_name}, {user_preferences}, {relevant_history}\n\n## Chain-Of-Thought\n\ndef cot_analysis(problem: str) -> str:\n        steps = [\n            \"1. O que exatamente esta sendo pedido?\",\n            \"2. Que informacoes sao criticas para resolver?\",\n            \"3. Quais abordagens possiveis existem?\",\n            \"4. Qual abordagem e melhor e por que?\",\n            \"5. Quais riscos ou limitacoes existem?\",\n        ]\n        prompt = f\"Analise passo a passo:\n\nPROBLEMA: {problem}\n\n\"\n        prompt += \"\n\".join(steps) + \"\n\nResposta final (concisa, para voz):\"\n        return call_claude(prompt)\n\n---\n\n## Cache Semantico\n\nclass SemanticCache:\n        def __init__(self, similarity_threshold=0.95):\n            self.threshold = similarity_threshold\n            self.cache = {}\n\n        def get_cached(self, query, embedding):\n            for cached_emb, (response, _) in self.cache.items():\n                if cosine_similarity(embedding, cached_emb) >= self.threshold:\n                    return response\n            return None\n\n        def set_cache(self, query, embedding, response):\n            self.cache[tuple(embedding)] = (response, query)\n\n## Estimativa De Custos Claude\n\nPRICING = {\n        \"claude-opus-4-20250805\": {\"input\": 15.00, \"output\": 75.00},\n        \"claude-sonnet-4-5\": {\"input\": 3.00, \"output\": 15.00},\n        \"claude-haiku-3-5\": {\"input\": 0.80, \"output\": 4.00},\n    }\n\n    def estimate_monthly_cost(model, avg_input, avg_output, req_per_day):\n        p = PRICING[model]\n        daily = (avg_input + avg_output) * req_per_day \u002F 1e6\n        monthly = daily * p[\"input\"] * 30\n        return {\"model\": model, \"monthly_cost\": \"USD %.2f\" % monthly}\n\n---\n\n## Framework De Avaliacao\n\nfrom anthropic import Anthropic\n    client = Anthropic()\n\n    def evaluate_response(question, expected, actual, criteria):\n        criteria_text = \"\n\".join(f\"- {c}\" for c in criteria)\n        eval_prompt = (\n            f\"Avalie a resposta do assistente de IA.\n\n\"\n            f\"PERGUNTA: {question}\nRESPOSTA ESPERADA: {expected}\n\"\n            f\"RESPOSTA ATUAL: {actual}\n\nCriterios:\n{criteria_text}\n\n\"\n            \"Nota 0-10 e justificativa para cada criterio. Formato JSON.\"\n        )\n        response = client.messages.create(\n            model=\"claude-haiku-3-5\", max_tokens=1024,\n            messages=[{\"role\": \"user\", \"content\": eval_prompt}]\n        )\n        import json\n        return json.loads(response.content[0].text)\n\n    AURI_EVALS = [\n        {\n            \"question\": \"Quais sao os principais riscos de abrir startup agora?\",\n            \"criteria\": [\"precisao_factual\", \"relevancia\", \"clareza_para_voz\"]\n        },\n    ]\n\n---\n\n## 6. Comandos\n\n| Comando | Acao |\n|---------|------|\n| \u002Frag-setup | Configura pipeline RAG completo |\n| \u002Fembed-docs | Indexa documentos no vector DB |\n| \u002Fprompt-optimize | Otimiza prompt para qualidade e custo |\n| \u002Fcost-estimate | Estima custo mensal do LLM |\n| \u002Feval-run | Roda suite de evals de qualidade |\n| \u002Fcache-setup | Configura cache semantico |\n| \u002Fmodel-select | Escolhe modelo ideal para o caso de uso |\n\n## Best Practices\n\n- Provide clear, specific context about your project and requirements\n- Review all suggestions before applying them to production code\n- Combine with other complementary skills for comprehensive analysis\n\n## Common Pitfalls\n\n- Using this skill for tasks outside its domain expertise\n- Applying recommendations without understanding your specific context\n- Not providing enough project context for accurate analysis\n","# LLM-OPS -- IA de Producao\n\n## Overview\n\nLLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao. Ativar para: implementar RAG, criar pipeline de embeddings, Pinecone\u002FChroma\u002Fpgvector, fine-tuning, prompt engineering, reducao de custos de LLM, evals, cache semantico, streaming, agents.\n\n## When to Use This Skill\n\n- When you need specialized assistance with this domain\n\n## Do Not Use This Skill When\n\n- The task is unrelated to llm ops\n- A simpler, more specific tool can handle the request\n- The user needs general-purpose assistance without domain expertise\n\n## How It Works\n\n> A diferenca entre um prototipo de IA e um produto de IA e operabilidade.\n> LLM-Ops e a engenharia que torna IA confiavel, escalavel e economica.\n\n---\n\n## Arquitetura Rag Completa\n\n[Documentos] -> [Chunking] -> [Embeddings] -> [Vector DB]\n                                                      |\n    [Query] -> [Embed query] -> [Semantic Search] -> [Top K chunks]\n                                                          |\n                                           [LLM + Context] -> [Resposta]\n\n## Pipeline De Indexacao\n\nfrom anthropic import Anthropic\n    import chromadb\n\n    client = Anthropic()\n    chroma = chromadb.PersistentClient(path=\".\u002Fchroma_db\")\n\n    def chunk_text(text, chunk_size=500, overlap=50):\n        words = text.split()\n        chunks = []\n        for i in range(0, len(words), chunk_size - overlap):\n            chunk = \" \".join(words[i:i + chunk_size])\n            if chunk: chunks.append(chunk)\n        return chunks\n\n    def index_document(doc_id, content_text, metadata=None):\n        chunks = chunk_text(content_text)\n        ids = [f\"{doc_id}_chunk_{i}\" for i in range(len(chunks))]\n        collection.upsert(ids=ids, documents=chunks)\n        return len(chunks)\n\n## Pipeline De Query Com Rag\n\ndef rag_query(query, top_k=5, system=None):\n        results = collection.query(\n            query_texts=[query], n_results=top_k,\n            include=[\"documents\", \"metadatas\", \"distances\"])\n        context_parts = []\n        for doc, meta, dist in zip(results[\"documents\"][0],\n                                    results[\"metadatas\"][0],\n                                    results[\"distances\"][0]):\n            if dist \u003C 1.5:\n                src = meta.get(\"source\", \"doc\")\n                context_parts.append(f\"[Fonte: {src}]\n{doc}\")\n        context = \"\n\n---\n\n\".join(context_parts)\n        response = client.messages.create(\n            model=\"claude-opus-4-20250805\", max_tokens=1024,\n            system=system or \"Responda baseado no contexto.\",\n            messages=[{\"role\": \"user\", \"content\": f\"Contexto:\n{context}\n\n{query}\"}])\n        return response.content[0].text\n\n---\n\n## Escolha Do Vector Db\n\n| DB | Melhor Para | Hosting | Custo |\n|----|------------|---------|-------|\n| Chroma | Desenvolvimento, local | Self-hosted | Gratis |\n| pgvector | Ja usa PostgreSQL | Self\u002FCloud | Gratis |\n| Pinecone | Producao gerenciada | Cloud | USD 70+\u002Fmes |\n| Weaviate | Multi-modal | Self\u002FCloud | Gratis+ |\n| Qdrant | Alta performance | Self\u002FCloud | Gratis+ |\n\n## Pgvector\n\nCREATE EXTENSION IF NOT EXISTS vector;\n    CREATE TABLE knowledge_embeddings (\n        id UUID PRIMARY KEY DEFAULT gen_random_uuid(),\n        content TEXT NOT NULL,\n        embedding vector(1536),\n        metadata JSONB,\n        created_at TIMESTAMPTZ DEFAULT NOW()\n    );\n    CREATE INDEX ON knowledge_embeddings\n    USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);\n    SELECT content, 1 - (embedding \u003C=> QUERY_VECTOR) AS similarity\n    FROM knowledge_embeddings ORDER BY similarity DESC LIMIT 5;\n\n---\n\n## Estrutura De Prompt De Elite\n\nComponentes do system prompt Auri:\n\n- Identidade: Nome (Auri), Tom (Natural, caloroso, direto), Plataforma (Amazon Alexa)\n- Regras: Maximo 3 paragrafos curtos, sem markdown, linguagem conversacional\n- Capacidades: analise de negocios, conselho baseado em dados, criatividade\n- Limitacoes: sem internet tempo real, sem transacoes financeiras\n- Personalizacao: {user_name}, {user_preferences}, {relevant_history}\n\n## Chain-Of-Thought\n\ndef cot_analysis(problem: str) -> str:\n        steps = [\n            \"1. O que exatamente esta sendo pedido?\",\n            \"2. Que informacoes sao criticas para resolver?\",\n            \"3. Quais abordagens possiveis existem?\",\n            \"4. Qual abordagem e melhor e por que?\",\n            \"5. Quais riscos ou limitacoes existem?\",\n        ]\n        prompt = f\"Analise passo a passo:\n\nPROBLEMA: {problem}\n\n\"\n        prompt += \"\n\".join(steps) + \"\n\nResposta final (concisa, para voz):\"\n        return call_claude(prompt)\n\n---\n\n## Cache Semantico\n\nclass SemanticCache:\n        def __init__(self, similarity_threshold=0.95):\n            self.threshold = similarity_threshold\n            self.cache = {}\n\n        def get_cached(self, query, embedding):\n            for cached_emb, (response, _) in self.cache.items():\n                if cosine_similarity(embedding, cached_emb) >= self.threshold:\n                    return response\n            return None\n\n        def set_cache(self, query, embedding, response):\n            self.cache[tuple(embedding)] = (response, query)\n\n## Estimativa De Custos Claude\n\nPRICING = {\n        \"claude-opus-4-20250805\": {\"input\": 15.00, \"output\": 75.00},\n        \"claude-sonnet-4-5\": {\"input\": 3.00, \"output\": 15.00},\n        \"claude-haiku-3-5\": {\"input\": 0.80, \"output\": 4.00},\n    }\n\n    def estimate_monthly_cost(model, avg_input, avg_output, req_per_day):\n        p = PRICING[model]\n        daily = (avg_input + avg_output) * req_per_day \u002F 1e6\n        monthly = daily * p[\"input\"] * 30\n        return {\"model\": model, \"monthly_cost\": \"USD %.2f\" % monthly}\n\n---\n\n## Framework De Avaliacao\n\nfrom anthropic import Anthropic\n    client = Anthropic()\n\n    def evaluate_response(question, expected, actual, criteria):\n        criteria_text = \"\n\".join(f\"- {c}\" for c in criteria)\n        eval_prompt = (\n            f\"Avalie a resposta do assistente de IA.\n\n\"\n            f\"PERGUNTA: {question}\nRESPOSTA ESPERADA: {expected}\n\"\n            f\"RESPOSTA ATUAL: {actual}\n\nCriterios:\n{criteria_text}\n\n\"\n            \"Nota 0-10 e justificativa para cada criterio. Formato JSON.\"\n        )\n        response = client.messages.create(\n            model=\"claude-haiku-3-5\", max_tokens=1024,\n            messages=[{\"role\": \"user\", \"content\": eval_prompt}]\n        )\n        import json\n        return json.loads(response.content[0].text)\n\n    AURI_EVALS = [\n        {\n            \"question\": \"Quais sao os principais riscos de abrir startup agora?\",\n            \"criteria\": [\"precisao_factual\", \"relevancia\", \"clareza_para_voz\"]\n        },\n    ]\n\n---\n\n## 6. Comandos\n\n| Comando | Acao |\n|---------|------|\n| \u002Frag-setup | Configura pipeline RAG completo |\n| \u002Fembed-docs | Indexa documentos no vector DB |\n| \u002Fprompt-optimize | Otimiza prompt para qualidade e custo |\n| \u002Fcost-estimate | Estima custo mensal do LLM |\n| \u002Feval-run | Roda suite de evals de qualidade |\n| \u002Fcache-setup | Configura cache semantico |\n| \u002Fmodel-select | Escolhe modelo ideal para o caso de uso |\n\n## Best Practices\n\n- Provide clear, specific context about your project and requirements\n- Review all suggestions before applying them to production code\n- Combine with other complementary skills for comprehensive analysis\n\n## Common Pitfalls\n\n- Using this skill for tasks outside its domain expertise\n- Applying recommendations without understanding your specific context\n- Not providing enough project context for accurate analysis\n",{"name":5,"metadata":53,"description":7},{"risk":47,"tags":54,"tools":57,"author":62,"source":32,"date_added":63},[55,42,39,31,56],"llm","fine-tuning",[58,45,59,60,61],"claude-code","cursor","gemini-cli","codex-cli","renat","2026-03-06","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fllm-ops",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":75,"prompt":85,"zipAvailable":86},[76,79,82],{"tool":77,"command":78},"npx","npx skills add davila7\u002Fclaude-code-templates --skill llm-ops",{"tool":80,"command":81},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill llm-ops",{"tool":83,"command":84},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill llm-ops","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fllm-ops\u002FSKILL.md",true,[],[89,117,143,175,210,234],{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":96,"publishedAt":48},"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",[95],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":124,"publishedAt":48},"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",[123],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[125,126,127,128,129,130,131,132,133,134,135,106,41,136,137,138,139,140,141,142],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","todo","work","plan","focus","signals","audit","dependency",{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":158,"publishedAt":48},"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",[149,152,153],{"categoryNo":150,"categoryName":151,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":154,"categoryName":155,"taxonomyType":156,"taxonomyTypeDesc":157},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[159,160,161,162,163,164,165,102,166,29,167,168,169,170,171,105,172,173,106,174],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","just","tool","calls","only","MCP","access","database","query",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":179,"licenseFileUrl":180,"sourceHtmlUrl":181,"categories":182,"keywords":186,"publishedAt":48},"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",[183],{"categoryNo":184,"categoryName":185,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,102,209],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"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":179,"licenseFileUrl":214,"sourceHtmlUrl":215,"categories":216,"keywords":221,"publishedAt":48},"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",[217,220],{"categoryNo":218,"categoryName":219,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":184,"categoryName":185,"taxonomyType":21,"taxonomyTypeDesc":22},[222,223,224,225,226,188,227,197,198,228,229,230,231,102,232,108,233],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":235,"name":236,"nameZh":6,"description":237,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":179,"licenseFileUrl":238,"sourceHtmlUrl":239,"categories":240,"keywords":248,"publishedAt":48},"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",[241,244,245],{"categoryNo":242,"categoryName":243,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":246,"categoryName":247,"taxonomyType":156,"taxonomyTypeDesc":157},"01JBSEEDCAT000000000000036","内容创作者",[249,250,251,252,253,254,255,256,190,257,200,41,258,259,260,261,262,170,263,106,264,265,266,267,268,114,269],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]