[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6NT5C5VW5YD6HAE6ZPWV02N":3,"skill-same:01M6NT5C5VW5YD6HAE6ZPWV02N":56},{"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":29,"linkOnly":30,"markdownContent":31,"markdownBody":32,"frontMatter":33,"refName":34,"commitSha":35,"skillDirectory":36,"forksCount":14,"repositoryHtmlUrl":37,"repositoryHomepageUrl":6,"ownerHtmlUrl":38,"ownerBlogUrl":39,"ownerDescription":40,"updatedAt":41,"install":42,"resources":55},"01M6NT5C5VW5YD6HAE6ZPWV02N","agentic-workflow",null,"Agentic Workflow Pattern","rubicanjr\u002FFinCognis","rubicanjr","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F234912519?v=4","01M6NT156NND8J474G34SB3923","Rubi","01M6NT15B70RWXE1N3YV0BZY8A",0,"MIT","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fagentic-workflow\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],"agentic","agentic workflow","workflow","pattern","false","2026-11-10 10:12:30",false,"---\nname: agentic-workflow\ndescription: Agentic Workflow Pattern\nuser-invocable: false\n---\n\n# Agentic Workflow Pattern\n\nStandard multi-agent pipeline for implementation tasks.\n\n## Architecture Principles\n\n- Use `run_in_background: true` for all agents to keep main context minimal\n- Use `Task` tool (never `TaskOutput`) to avoid receiving full agent transcripts\n- Agents write outputs to `.claude\u002Fcache\u002Fagents\u002F\u003Cstage>\u002F` for injection into subsequent agents\n- Main conversation is pure orchestration — no heavy lifting, only coordination\n\n## Workflow Stages\n\n### 1. Research Agent\n```\nTask(subagent_type=\"oracle\", run_in_background=true, prompt=\"\"\"\nQuery NIA Oracle (via \u002Fnia-docs skill) to verify approach and gather best practices.\n\nOutput to: .claude\u002Fcache\u002Fagents\u002Foracle\u002F\u003Ctask>-research.md\n\"\"\")\n```\n- Enforce NIA as the research layer\n- Output: Research findings\n\n### 2. Planning Agent\n```\nTask(subagent_type=\"plan-agent\", run_in_background=true, prompt=\"\"\"\nRead: .claude\u002Fcache\u002Fagents\u002Foracle\u002F\u003Ctask>-research.md\nUse RP-CLI to analyze the target codebase section.\nGenerate implementation plan informed by research.\n\nOutput to: .claude\u002Fcache\u002Fagents\u002Fplan-agent\u002F\u003Ctask>-plan.md\n\"\"\")\n```\n- Receives: Research agent output as context\n- Output: Implementation plan\n\n### 3. Validation Agent\n```\nTask(subagent_type=\"validate-agent\", run_in_background=true, prompt=\"\"\"\nRead: .claude\u002Fcache\u002Fagents\u002Fplan-agent\u002F\u003Ctask>-plan.md\nRead: .claude\u002Fcache\u002Fagents\u002Foracle\u002F\u003Ctask>-research.md\nReview plan against research findings and best practices.\n\nOutput to: .claude\u002Fcache\u002Fagents\u002Fvalidate-agent\u002F\u003Ctask>-validated.md\n\"\"\")\n```\n- Reviews plan against research\n- Output: Validated plan with amendments\n\n### 4. Implementation Agent\n```\nTask(subagent_type=\"agentica-agent\", run_in_background=true, prompt=\"\"\"\nRead: .claude\u002Fcache\u002Fagents\u002Fvalidate-agent\u002F\u003Ctask>-validated.md\nRead: .claude\u002Fcache\u002Fagents\u002Foracle\u002F\u003Ctask>-research.md\n\nTDD approach: Write failing tests FIRST, then implement.\nRun tests to verify.\n\nOutput summary to: .claude\u002Fcache\u002Fagents\u002Fimplement-agent\u002F\u003Ctask>-implementation.md\n\"\"\")\n```\n- Receives: Validated plan + research context\n- **TDD**: Failing tests first\n- Output: Implementation + tests\n\n### 5. Review Agent\n```\nTask(subagent_type=\"review-agent\", run_in_background=true, prompt=\"\"\"\nRead: .claude\u002Fcache\u002Fagents\u002Fimplement-agent\u002F\u003Ctask>-implementation.md\nRead: .claude\u002Fcache\u002Fagents\u002Fvalidate-agent\u002F\u003Ctask>-validated.md\nRead: .claude\u002Fcache\u002Fagents\u002Foracle\u002F\u003Ctask>-research.md\n\nCross-reference implementation against plan and research.\nRun tests to confirm passing.\n\nOutput to: .claude\u002Fcache\u002Fagents\u002Freview-agent\u002F\u003Ctask>-review.md\n\"\"\")\n```\n- Cross-references all artifacts\n- Confirms tests pass\n- Output: Review summary\n\n## Agent Progress Monitoring\n\n```bash\n# Watch for system reminders:\n# \"Agent a42a16e progress: 6 new tools used, 88914 new tokens\"\n\n# Poll for output files:\nfind .claude\u002Fcache\u002Fagents -name \"*.md\" -mmin -5\n\n# Check task file size growth:\nwc -c \u002Ftmp\u002Fclaude\u002F...\u002Ftasks\u002F\u003Cid>.output\n```\n\n**Stuck detection:**\n1. Progress reminders stop arriving\n2. Task output file size stops growing\n3. Expected output file not created after reasonable time\n\n## Directory Structure\n\n```\n.claude\u002Fcache\u002Fagents\u002F\n├── oracle\u002F\n│   └── \u003Ctask>-research.md\n├── plan-agent\u002F\n│   └── \u003Ctask>-plan.md\n├── validate-agent\u002F\n│   └── \u003Ctask>-validated.md\n├── implement-agent\u002F\n│   └── \u003Ctask>-implementation.md\n└── review-agent\u002F\n    └── \u003Ctask>-review.md\n```\n\n## Key Rules\n\n1. **Never use TaskOutput** - floods context with 70k+ token transcripts\n2. **Always run_in_background=true** - isolates agent context\n3. **File-based handoff** - each agent reads previous agent's output file\n4. **Poll, don't block** - check file system for outputs, don't wait\n5. **TDD in implementation** - failing tests first, then make them pass\n\n## Source\n- Session 2026-01-01: SDK Phase 3 implementation using this pattern\n","# Agentic Workflow Pattern\n\nStandard multi-agent pipeline for implementation tasks.\n\n## Architecture Principles\n\n- Use `run_in_background: true` for all agents to keep main context minimal\n- Use `Task` tool (never `TaskOutput`) to avoid receiving full agent transcripts\n- Agents write outputs to `.claude\u002Fcache\u002Fagents\u002F\u003Cstage>\u002F` for injection into subsequent agents\n- Main conversation is pure orchestration — no heavy lifting, only coordination\n\n## Workflow Stages\n\n### 1. Research Agent\n```\nTask(subagent_type=\"oracle\", run_in_background=true, prompt=\"\"\"\nQuery NIA Oracle (via \u002Fnia-docs skill) to verify approach and gather best practices.\n\nOutput to: .claude\u002Fcache\u002Fagents\u002Foracle\u002F\u003Ctask>-research.md\n\"\"\")\n```\n- Enforce NIA as the research layer\n- Output: Research findings\n\n### 2. Planning Agent\n```\nTask(subagent_type=\"plan-agent\", run_in_background=true, prompt=\"\"\"\nRead: .claude\u002Fcache\u002Fagents\u002Foracle\u002F\u003Ctask>-research.md\nUse RP-CLI to analyze the target codebase section.\nGenerate implementation plan informed by research.\n\nOutput to: .claude\u002Fcache\u002Fagents\u002Fplan-agent\u002F\u003Ctask>-plan.md\n\"\"\")\n```\n- Receives: Research agent output as context\n- Output: Implementation plan\n\n### 3. Validation Agent\n```\nTask(subagent_type=\"validate-agent\", run_in_background=true, prompt=\"\"\"\nRead: .claude\u002Fcache\u002Fagents\u002Fplan-agent\u002F\u003Ctask>-plan.md\nRead: .claude\u002Fcache\u002Fagents\u002Foracle\u002F\u003Ctask>-research.md\nReview plan against research findings and best practices.\n\nOutput to: .claude\u002Fcache\u002Fagents\u002Fvalidate-agent\u002F\u003Ctask>-validated.md\n\"\"\")\n```\n- Reviews plan against research\n- Output: Validated plan with amendments\n\n### 4. Implementation Agent\n```\nTask(subagent_type=\"agentica-agent\", run_in_background=true, prompt=\"\"\"\nRead: .claude\u002Fcache\u002Fagents\u002Fvalidate-agent\u002F\u003Ctask>-validated.md\nRead: .claude\u002Fcache\u002Fagents\u002Foracle\u002F\u003Ctask>-research.md\n\nTDD approach: Write failing tests FIRST, then implement.\nRun tests to verify.\n\nOutput summary to: .claude\u002Fcache\u002Fagents\u002Fimplement-agent\u002F\u003Ctask>-implementation.md\n\"\"\")\n```\n- Receives: Validated plan + research context\n- **TDD**: Failing tests first\n- Output: Implementation + tests\n\n### 5. Review Agent\n```\nTask(subagent_type=\"review-agent\", run_in_background=true, prompt=\"\"\"\nRead: .claude\u002Fcache\u002Fagents\u002Fimplement-agent\u002F\u003Ctask>-implementation.md\nRead: .claude\u002Fcache\u002Fagents\u002Fvalidate-agent\u002F\u003Ctask>-validated.md\nRead: .claude\u002Fcache\u002Fagents\u002Foracle\u002F\u003Ctask>-research.md\n\nCross-reference implementation against plan and research.\nRun tests to confirm passing.\n\nOutput to: .claude\u002Fcache\u002Fagents\u002Freview-agent\u002F\u003Ctask>-review.md\n\"\"\")\n```\n- Cross-references all artifacts\n- Confirms tests pass\n- Output: Review summary\n\n## Agent Progress Monitoring\n\n```bash\n# Watch for system reminders:\n# \"Agent a42a16e progress: 6 new tools used, 88914 new tokens\"\n\n# Poll for output files:\nfind .claude\u002Fcache\u002Fagents -name \"*.md\" -mmin -5\n\n# Check task file size growth:\nwc -c \u002Ftmp\u002Fclaude\u002F...\u002Ftasks\u002F\u003Cid>.output\n```\n\n**Stuck detection:**\n1. Progress reminders stop arriving\n2. Task output file size stops growing\n3. Expected output file not created after reasonable time\n\n## Directory Structure\n\n```\n.claude\u002Fcache\u002Fagents\u002F\n├── oracle\u002F\n│   └── \u003Ctask>-research.md\n├── plan-agent\u002F\n│   └── \u003Ctask>-plan.md\n├── validate-agent\u002F\n│   └── \u003Ctask>-validated.md\n├── implement-agent\u002F\n│   └── \u003Ctask>-implementation.md\n└── review-agent\u002F\n    └── \u003Ctask>-review.md\n```\n\n## Key Rules\n\n1. **Never use TaskOutput** - floods context with 70k+ token transcripts\n2. **Always run_in_background=true** - isolates agent context\n3. **File-based handoff** - each agent reads previous agent's output file\n4. **Poll, don't block** - check file system for outputs, don't wait\n5. **TDD in implementation** - failing tests first, then make them pass\n\n## Source\n- Session 2026-01-01: SDK Phase 3 implementation using this pattern\n",{"name":5,"description":7,"user-invocable":30},"main","70e1c350500ee750987420bbae48837afa906b7e","skills\u002Fagentic-workflow","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis","https:\u002F\u002Fgithub.com\u002Frubicanjr","linktr.ee\u002Ffinanskodu","Industrial Engineer & CIO @FinansKodu. Architecting AI-driven financial agents and autonomous workflows. 🏗️ Building systems, not just prompts.","2026-06-09 13:50:15",{"source":8,"skillName":5,"commands":43,"prompt":53,"zipAvailable":54},[44,47,50],{"tool":45,"command":46},"npx","npx skills add rubicanjr\u002FFinCognis --skill agentic-workflow",{"tool":48,"command":49},"bunx","bunx skills add rubicanjr\u002FFinCognis --skill agentic-workflow",{"tool":51,"command":52},"pnpm","pnpm dlx skills add rubicanjr\u002FFinCognis --skill agentic-workflow","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fagentic-workflow\u002FSKILL.md",true,[],[57,73,81,101,131,150],{"skillNo":58,"name":59,"nameZh":6,"description":60,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":61,"categories":62,"keywords":64,"publishedAt":29},"01M6NWS9DAXJTADT4T5MB49X8W","workflow-router","Goal-based workflow orchestration - routes tasks to specialist agents based on user goals","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fworkflow-router\u002FSKILL.md",[63],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[65,26,66,67,68,69,70,71,72],"workflow router","router","orchestration","goals","goal","user","workflow orchestration","tasks",{"skillNo":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":77,"categories":78,"keywords":79,"publishedAt":29},"01M6NWS3RETSC0SHP16HP66E14","wiring","Wiring Verification","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fwiring\u002FSKILL.md",[],[75,80,28],"verification",{"skillNo":82,"name":83,"nameZh":6,"description":84,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":85,"categories":86,"keywords":90,"publishedAt":29},"01M6NWS0XW9CVQZBDA1EGW0TWC","websocket-patterns","Connection management, room patterns, reconnection strategies, message buffering, and binary protocol design.","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fwebsocket-patterns\u002Fskill.md",[87],{"categoryNo":88,"categoryName":89,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",[91,92,93,94,95,96,97,98,99,100],"websocket","patterns","websocket patterns","design","protocol","management","message","strategies","binary","connection",{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":111,"publishedAt":29},"01M6NWRDQDX9ZNETAWRRMQWV52","visual-verdict","Screenshot comparison QA for frontend development. Takes a screenshot of the current implementation, scores it across multiple visual dimensions, and returns a structured PASS\u002FREVISE\u002FFAIL verdict with concrete fixes. Use when implementing UI from a design reference or verifying visual correctness.","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fvisual-verdict\u002FSKILL.md",[107,110],{"categoryNo":108,"categoryName":109,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[112,113,114,115,116,117,118,119,120,121,122,123,124,94,125,126,127,128,129,130],"visual","verdict","visual verdict","ui","across","revise","comparison","correctness","implementing","for","fixes","dimensions","frontend","development","qa","screenshot","returns","use","frontend development",{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":137,"publishedAt":29},"01M6NWR8HSNRS7MNDK2J02QYMN","verification-loop","Comprehensive verification system covering build, types, lint, tests, security, and diff review before a PR.","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fverification-loop\u002FSKILL.md",[],[138,139,80,140,141,142,143,144,145,146,147,148,149],"verification loop","loop","lint","review","security","pr","system","diff","build","tests","diff review","types",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":156,"publishedAt":29},"01M6NWR4HHSFR0VR5EXFRWA747","vector-db-patterns","Embedding strategies, ANN algorithms, hybrid search, RAG chunking strategies, and reranking for semantic search and retrieval.","https:\u002F\u002Fgithub.com\u002Frubicanjr\u002FFinCognis\u002Fblob\u002Fmain\u002Fskills\u002Fvector-db-patterns\u002Fskill.md",[],[157,158,159,160,92,161,162,163,164,98,165,166,167,168,169,121,170,171],"vector db patterns","vector db","db","vector","semantic search","search","embedding strategies","algorithms","chunking","semantic","retrieval","embedding","Hybrid Search","hybrid","rag"]