[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CTR4TWCR0F6N331MENZAAP":3,"skill-same:01M6CTR4TWCR0F6N331MENZAAP":80},{"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":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":58,"commitSha":59,"skillDirectory":60,"forksCount":61,"repositoryHtmlUrl":62,"repositoryHomepageUrl":62,"ownerHtmlUrl":63,"ownerBlogUrl":6,"ownerDescription":64,"updatedAt":65,"install":66,"resources":79},"01M6CTR4TWCR0F6N331MENZAAP","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.","Sam-06060\u002Fjarvis-assistant","Sam-06060","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F208600848?v=4","01M6CRKT7C7FXD21V6HXF293S0","Samson Frank Ganta","01M6CRKTBEKYFD8YX5Z1GT99EE",6,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\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],"ops","llm ops","vector-db","cli","community","evals","databases","operations","cursor","prompt","tuning","Fine-Tuning","embeddings","vector","code","rag","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.\"\nrisk: safe\nsource: community\ndate_added: '2026-03-06'\nauthor: renat\ntags:\n- llm\n- rag\n- embeddings\n- vector-db\n- fine-tuning\ntools:\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,"risk":43,"tags":49,"tools":52,"author":56,"source":28,"date_added":57,"description":7},[50,39,36,26,51],"llm","fine-tuning",[53,41,32,54,55],"claude-code","gemini-cli","codex-cli","renat","2026-03-06","main","a50a3aa6b39fb0e1b7db88bef1bfc9c93606e126","modules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fllm-ops",0,"https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant","https:\u002F\u002Fgithub.com\u002FSam-06060","Mobile Application Developer | Kotlin | Flutter, KMP, Swfit, Android,Swift UI | Building at Saar It Resources | Creator of Jarvis (AI Assistant)","2026-06-23 05:13:38",{"source":8,"skillName":5,"commands":67,"prompt":77,"zipAvailable":78},[68,71,74],{"tool":69,"command":70},"npx","npx skills add Sam-06060\u002Fjarvis-assistant --skill llm-ops",{"tool":72,"command":73},"bunx","bunx skills add Sam-06060\u002Fjarvis-assistant --skill llm-ops",{"tool":75,"command":76},"pnpm","pnpm dlx skills add Sam-06060\u002Fjarvis-assistant --skill llm-ops","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fllm-ops\u002FSKILL.md",true,[],[81,92,108,133,152,178],{"skillNo":82,"name":83,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":84,"categories":85,"keywords":89,"publishedAt":44},"01M6GDRVS6FXX3ESH0C9DW01C0","test_skill","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fscratch\u002Ftest_skill\u002FSKILL.md",[86],{"categoryNo":87,"categoryName":88,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[90,91],"Test Skill","test",{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":44},"01M6GDRQ40HX22MA6XWC31RHHK","zustand-store-ts","Create Zustand stores following established patterns with proper TypeScript types and middleware.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzustand-store-ts\u002FSKILL.md",[],[99,100,101,102,103,104,105,28,106,107],"zustand store ts","zustand","store","with","types","middleware","patterns","create","typescript",{"skillNo":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":115,"publishedAt":44},"01M6GDRKEA0WE670SPFWX5GKCN","zoom-automation","Automate Zoom meeting creation, management, recordings, webinars, and participant tracking via Rube MCP (Composio). Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzoom-automation\u002FSKILL.md",[114],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[116,117,118,119,120,121,122,123,124,125,126,127,128,129,28,130,131,132],"automation","zoom automation","zoom","first","Tools","search","MCP","via","meeting","automate","critical","creation","webinars","composio","recordings","management","tracking",{"skillNo":134,"name":135,"nameZh":6,"description":136,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":137,"categories":138,"keywords":140,"publishedAt":44},"01M6GDRFS1452PC71JQ38S24HR","zoho-crm-automation","Automate Zoho CRM tasks via Rube MCP (Composio): create\u002Fupdate records, search contacts, manage leads, and convert leads. Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzoho-crm-automation\u002FSKILL.md",[139],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[141,116,142,143,144,145,146,28,121,147,129,148,126,125,120,149,106,150,122,151],"crm automation","zoho crm","crm","zoho crm automation","zoho","leads","contacts","records","update","tasks","manage",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":161,"publishedAt":44},"01M6GDRCKQBAVTGHX5XBH26KPZ","zod-validation-expert","Expert in Zod — TypeScript-first schema validation. Covers parsing, custom errors, refinements, type inference, and integration with React Hook Form, Next.js, and tRPC.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzod-validation-expert\u002FSKILL.md",[158],{"categoryNo":159,"categoryName":160,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[162,163,164,165,166,107,43,167,168,169,170,171,102,172,173,174,175,28,176,177],"expert","zod","zod validation expert","validation","react","parsing","errors","schema validation","react hook form","hook","trpc","form","type","next","schema","Next.js",{"skillNo":179,"name":180,"nameZh":6,"description":181,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":185,"publishedAt":44},"01M6GDR658B08K4MY0RWD4A6EH","zeroize-audit","Detects missing zeroization of sensitive data in source code and identifies zeroization removed by compiler optimizations, with assembly-level analysis, and control-flow verification. Use for auditing C\u002FC++\u002FRust code handling secrets, keys, passwords, or other sensitive data.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzeroize-audit\u002FSKILL.md",[184],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[186,187,188,189,190,191,192,193,28,194,195,196,197,198,199,200,201,38,202,122],"zeroize audit","audit","write","passwords","get","project","c++","activate","assembly","read","Bash","secrets","analysis","data","flow","verification","rust"]