[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6H56GPJ6EAA0YN5WHDFQXRJ":3,"skill-same:01M6H56GPJ6EAA0YN5WHDFQXRJ":80},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":53,"commitSha":54,"skillDirectory":55,"forksCount":56,"repositoryHtmlUrl":57,"repositoryHomepageUrl":6,"ownerHtmlUrl":58,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":59,"install":60,"resources":73},"01M6H56GPJ6EAA0YN5WHDFQXRJ","vertex-agent-builder",null,"Build and deploy production-ready generative AI agents using Vertex AI, Gemini models, and Google Cloud infrastructure with RAG, function calling, and multi-modal capabilities. Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.","micsapp\u002Fmicstec-skills","micsapp","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F82411001?v=4","01M6GWCFQ6D3PV8EHNY62NN5VW","01M6GWCFVE1PA5HG0DKC7AMFJ3",4,"MIT","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fclaude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fjeremy-vertex-ai\u002Fskills\u002Fvertex-agent-builder\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"agent builder","vertex agent builder","builder","write","production-ready","relevant","io","deploy production","ready","edit","google","read","Bash","capabilities","code","rag","google cloud","generative ai","ai agents","openclaw","2026-11-10 10:12:30",false,"---\nname: vertex-agent-builder\ndescription: |\n  Build and deploy production-ready generative AI agents using Vertex AI, Gemini models, and Google Cloud infrastructure with RAG, function calling, and multi-modal capabilities. Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.\nallowed-tools: Read, Write, Edit, Grep, Bash(cmd:*)\nversion: 1.0.0\nauthor: Jeremy Longshore \u003Cjeremy@intentsolutions.io>\nlicense: MIT\ncompatible-with: claude-code, codex, openclaw\n---\n# Vertex AI Agent Builder\n\nBuild and deploy production-ready agents on Vertex AI with Gemini models, retrieval (RAG), function calling, and operational guardrails (validation, monitoring, cost controls).\n\n## Overview\n\n- Produces an agent scaffold aligned with Vertex AI Agent Engine deployment patterns.\n- Helps choose models\u002Fregions, design tool\u002Ffunction interfaces, and wire up retrieval.\n- Includes an evaluation + smoke-test checklist so deployments don’t regress.\n\n## Prerequisites\n\n- Google Cloud project with Vertex AI API enabled\n- Permissions to deploy\u002Foperate Agent Engine runtimes (or a local-only build target)\n- If using RAG: a document source (GCS\u002FBigQuery\u002FFirestore\u002Fetc) and an embeddings\u002Findex strategy\n- Secrets handled via env vars or Secret Manager (never committed)\n\n## Instructions\n\n1. Clarify the agent’s job (user intents, inputs\u002Foutputs, latency and cost constraints).\n2. Choose model + region and define tool\u002Ffunction interfaces (schemas, error contracts).\n3. Implement retrieval (if needed): chunking, embeddings, index, and a “citation-first” response format.\n4. Add evaluation: golden prompts, offline checks, and a minimal online smoke test.\n5. Deploy (optional): provide the exact deployment command\u002Fconfig and verify endpoints + permissions.\n6. Add ops: logs\u002Fmetrics, alerting, quota\u002Fcost guardrails, and rollback steps.\n\n## Output\n\n- A Vertex AI agent scaffold (code\u002Fconfig) with clear extension points\n- A retrieval plan (when applicable) and a validation\u002Fevaluation checklist\n- Optional: deployment commands and post-deploy health checks\n\n## Error Handling\n\n- Quota\u002Fregion issues: detect the failing service\u002Fquota and propose a scoped fix.\n- Auth failures: identify the principal and missing role; prefer least-privilege remediation.\n- Retrieval failures: validate indexing\u002Fembedding dimensions and add fallback behavior.\n- Tool\u002Ffunction errors: enforce structured error responses and add regression tests.\n\n## Examples\n\n**Example: RAG support agent**\n- Request: “Deploy a support bot that answers from our docs with citations.”\n- Result: ingestion plan, retrieval wiring, evaluation prompts, and a smoke test that verifies citations.\n\n**Example: Multimodal intake agent**\n- Request: “Build an agent that extracts structured fields from PDFs\u002Fimages and routes tasks.”\n- Result: schema-first extraction prompts, tool interface contracts, and validation examples.\n\n## Resources\n\n- Full detailed guide (kept for reference): `${CLAUDE_SKILL_DIR}\u002Freferences\u002FSKILL.full.md`\n- Repo standards (source of truth):\n  - `000-docs\u002F6767-a-SPEC-DR-STND-claude-code-plugins-standard.md`\n  - `000-docs\u002F6767-b-SPEC-DR-STND-claude-skills-standard.md`\n- Vertex AI docs: https:\u002F\u002Fcloud.google.com\u002Fvertex-ai\u002Fdocs\n- Agent Engine docs: https:\u002F\u002Fcloud.google.com\u002Fvertex-ai\u002Fdocs\u002Fagent-engine","# Vertex AI Agent Builder\n\nBuild and deploy production-ready agents on Vertex AI with Gemini models, retrieval (RAG), function calling, and operational guardrails (validation, monitoring, cost controls).\n\n## Overview\n\n- Produces an agent scaffold aligned with Vertex AI Agent Engine deployment patterns.\n- Helps choose models\u002Fregions, design tool\u002Ffunction interfaces, and wire up retrieval.\n- Includes an evaluation + smoke-test checklist so deployments don’t regress.\n\n## Prerequisites\n\n- Google Cloud project with Vertex AI API enabled\n- Permissions to deploy\u002Foperate Agent Engine runtimes (or a local-only build target)\n- If using RAG: a document source (GCS\u002FBigQuery\u002FFirestore\u002Fetc) and an embeddings\u002Findex strategy\n- Secrets handled via env vars or Secret Manager (never committed)\n\n## Instructions\n\n1. Clarify the agent’s job (user intents, inputs\u002Foutputs, latency and cost constraints).\n2. Choose model + region and define tool\u002Ffunction interfaces (schemas, error contracts).\n3. Implement retrieval (if needed): chunking, embeddings, index, and a “citation-first” response format.\n4. Add evaluation: golden prompts, offline checks, and a minimal online smoke test.\n5. Deploy (optional): provide the exact deployment command\u002Fconfig and verify endpoints + permissions.\n6. Add ops: logs\u002Fmetrics, alerting, quota\u002Fcost guardrails, and rollback steps.\n\n## Output\n\n- A Vertex AI agent scaffold (code\u002Fconfig) with clear extension points\n- A retrieval plan (when applicable) and a validation\u002Fevaluation checklist\n- Optional: deployment commands and post-deploy health checks\n\n## Error Handling\n\n- Quota\u002Fregion issues: detect the failing service\u002Fquota and propose a scoped fix.\n- Auth failures: identify the principal and missing role; prefer least-privilege remediation.\n- Retrieval failures: validate indexing\u002Fembedding dimensions and add fallback behavior.\n- Tool\u002Ffunction errors: enforce structured error responses and add regression tests.\n\n## Examples\n\n**Example: RAG support agent**\n- Request: “Deploy a support bot that answers from our docs with citations.”\n- Result: ingestion plan, retrieval wiring, evaluation prompts, and a smoke test that verifies citations.\n\n**Example: Multimodal intake agent**\n- Request: “Build an agent that extracts structured fields from PDFs\u002Fimages and routes tasks.”\n- Result: schema-first extraction prompts, tool interface contracts, and validation examples.\n\n## Resources\n\n- Full detailed guide (kept for reference): `${CLAUDE_SKILL_DIR}\u002Freferences\u002FSKILL.full.md`\n- Repo standards (source of truth):\n  - `000-docs\u002F6767-a-SPEC-DR-STND-claude-code-plugins-standard.md`\n  - `000-docs\u002F6767-b-SPEC-DR-STND-claude-skills-standard.md`\n- Vertex AI docs: https:\u002F\u002Fcloud.google.com\u002Fvertex-ai\u002Fdocs\n- Agent Engine docs: https:\u002F\u002Fcloud.google.com\u002Fvertex-ai\u002Fdocs\u002Fagent-engine",{"name":5,"author":48,"license":14,"version":49,"description":50,"allowed-tools":51,"compatible-with":52},"Jeremy Longshore \u003Cjeremy@intentsolutions.io>","1.0.0","Build and deploy production-ready generative AI agents using Vertex AI, Gemini models, and Google Cloud infrastructure with RAG, function calling, and multi-modal capabilities. Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.\n","Read, Write, Edit, Grep, Bash(cmd:*)","claude-code, codex, openclaw","master","3f5d983eb4ef375b749adcd6b07ec03bd82ae8a8","claude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fjeremy-vertex-ai\u002Fskills\u002Fvertex-agent-builder",0,"https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills","https:\u002F\u002Fgithub.com\u002Fmicsapp","2026-03-20 11:24:18",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add micsapp\u002Fmicstec-skills --skill vertex-agent-builder",{"tool":66,"command":67},"bunx","bunx skills add micsapp\u002Fmicstec-skills --skill vertex-agent-builder",{"tool":69,"command":70},"pnpm","pnpm dlx skills add micsapp\u002Fmicstec-skills --skill vertex-agent-builder","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fclaude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fjeremy-vertex-ai\u002Fskills\u002Fvertex-agent-builder\u002FSKILL.md",true,[74],{"path":75,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002FSKILL.full.md","REFERENCE","参考文档",26552,"https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fclaude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fjeremy-vertex-ai\u002Fskills\u002Fvertex-agent-builder\u002Freferences\u002FSKILL.full.md",[81,116,143,162,182,206],{"skillNo":82,"name":83,"nameZh":6,"description":84,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":85,"categories":86,"keywords":95,"publishedAt":43},"01M6HBTJ02FXBFG9NYEHHYAP52","azure-apim-architecture","Analyzes and explains Azure API Management architecture decisions for enterprise API marketplace implementations using VNet Internal mode, Front Door, hybrid authentication, and multi-environment strategies. Use when discussing APIM component selection, network topology, cost optimization, or comparing alternatives like workspaces vs instances, VNet Internal vs External mode, or Front Door vs Application Gateway.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fcopilot-agent-skills\u002F.github\u002Fskills\u002Fazure-apim-architecture\u002FSKILL.md",[87,90],{"categoryNo":88,"categoryName":89,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115],"architecture","azure","azure apim architecture","component","authentication","gateway","topology","Optimization","network","api","team","environment","multi-environment","workspaces","instances","selection","strategies","azure api management","mode","for",{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":128,"publishedAt":43},"01M6HBT7J4H082JEEA670B3EQ9","apiops-deployment","Guides deployment of Azure API Management infrastructure using Infrastructure as Code (Bicep\u002FTerraform), CI\u002FCD pipelines (GitHub Actions\u002FAzure DevOps), and APIOps workflows. Use when deploying APIM, creating pipelines, or implementing dev→test→prod promotion strategies.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fcopilot-agent-skills\u002F.github\u002Fskills\u002Fapiops-deployment\u002FSKILL.md",[122,123,126,127],{"categoryNo":88,"categoryName":89,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":124,"categoryName":125,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},[129,130,131,132,97,133,37,134,135,136,137,138,105,106,112,139,140,113,141,142],"deployment","apiops deployment","workflows","azure devops","infrastructure","infrastructure as code","actions","ci","github actions","github-actions","as","guides","CI\u002FCD","implementing",{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":150,"publishedAt":43},"01M6HBT2HEA23THNE9E00GYQBE","apim-policy-authoring","Creates production-ready Azure API Management policy XML for authentication (OAuth 2.0, JWT validation, subscription keys), rate limiting, CORS configuration, error handling, and API transformations. Use when implementing API security, access control, or request\u002Fresponse processing logic.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fcopilot-agent-skills\u002F.github\u002Fskills\u002Fapim-policy-authoring\u002FSKILL.md",[149],{"categoryNo":88,"categoryName":89,"taxonomyType":20,"taxonomyTypeDesc":21},[151,152,153,154,97,100,105,106,155,156,31,157,158,159,160,113,161,27,142,115],"policy","authoring","apim policy authoring","security","access","use","api security","Access Control","error","processing","cors configuration",{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":166,"categories":167,"keywords":170,"publishedAt":43},"01M6HBSY65SB29J9WE9KYD0K6T","api-security-review","Reviews Azure API Management configurations for security vulnerabilities, OWASP API Security Top 10 compliance, VNet Internal mode validation, Private Link verification, and Azure Security Benchmark alignment. Use when performing security audits, pre-deployment validation, or compliance reviews.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fcopilot-agent-skills\u002F.github\u002Fskills\u002Fapi-security-review\u002FSKILL.md",[168,169],{"categoryNo":88,"categoryName":89,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":91,"categoryName":92,"taxonomyType":93,"taxonomyTypeDesc":94},[171,154,172,105,157,173,97,129,174,175,176,177,106,178,113,179,180,181,114,115],"review","security review","api security review","benchmark","verification","owasp","owasp-api","alignment","azure security","reviews","top",{"skillNo":183,"name":184,"nameZh":6,"description":185,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":186,"categories":187,"keywords":188,"publishedAt":43},"01M6HBSRW4PDFE6K4F8JYPZQ05","jira-cli","Interact with Jira from the command line to create, list, view, edit, and transition issues, manage sprints and epics, and perform common Jira workflows. Use when the user asks about Jira tasks, tickets, issues, sprints, or needs to manage project work items.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fawesome-copilot-agents\u002Fskills\u002Fjira-cli\u002FSKILL.md",[],[189,190,191,131,192,193,194,195,105,196,197,198,32,199,200,201,202,203,204,205],"cli","jira","jira cli","init","tasks","create","issues","common","work","command","list","view","project","project work","token","line","sprints",{"skillNo":207,"name":208,"nameZh":6,"description":209,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":210,"categories":211,"keywords":212,"publishedAt":43},"01M6HBSNS3JHMW603G47TWE31H","calculator","Performs arbitrary-precision arithmetic calculations including addition, subtraction, multiplication, division, and exponents. Use when the user asks to calculate, compute, or evaluate math expressions, or when precise decimal arithmetic is needed to avoid floating-point errors.","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fcopilot-cli\u002Fawesome-copilot-agents\u002Fskills\u002Fcalculator\u002FSKILL.md",[],[208,213,214,215,216,217,218,156,219,220],"npm","js","calculate","node","math","user","compute","errors"]