[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6F8WE8S0WBZZPVB550DC7ME":3,"skill-same:01M6F8WE8S0WBZZPVB550DC7ME":76},{"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":18,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":53,"commitSha":54,"skillDirectory":55,"forksCount":56,"repositoryHtmlUrl":57,"repositoryHomepageUrl":6,"ownerHtmlUrl":58,"ownerBlogUrl":59,"ownerDescription":60,"updatedAt":61,"install":62,"resources":75},"01M6F8WE8S0WBZZPVB550DC7ME","retellai-architecture-variants",null,"Choose and implement Retell AI validated architecture blueprints for different scales.\nUse when designing new Retell AI integrations, choosing between monolith\u002Fservice\u002Fmicroservice\narchitectures, or planning migration paths for Retell AI applications.\nTrigger with phrases like \"retellai architecture\", \"retellai blueprint\",\n\"how to structure retellai\", \"retellai project layout\", \"retellai microservice\".","Dicklesworthstone\u002Fpi_agent_rust","Dicklesworthstone","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F35050222?v=4","01M6CG26AR942CSVMN1M5E3DSE","Jeff Emanuel","01M6CG26EPP71PSHW1VDS71121",1991,"MIT","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fsaas-packs\u002Fretellai-pack\u002Fskills\u002Fretellai-architecture-variants\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"architecture","variants","retellai architecture variants","new","implement","when","for","with","architectures","paths","how to","trigger","io","layout","project","structure","Blueprint","use","read","choose","integrations","how","applications","microservice","planning","2026-11-10 10:12:30",false,"---\nname: retellai-architecture-variants\ndescription: |\n  Choose and implement Retell AI validated architecture blueprints for different scales.\n  Use when designing new Retell AI integrations, choosing between monolith\u002Fservice\u002Fmicroservice\n  architectures, or planning migration paths for Retell AI applications.\n  Trigger with phrases like \"retellai architecture\", \"retellai blueprint\",\n  \"how to structure retellai\", \"retellai project layout\", \"retellai microservice\".\nallowed-tools: Read, Grep\nversion: 1.0.0\nlicense: MIT\nauthor: Jeremy Longshore \u003Cjeremy@intentsolutions.io>\n---\n\n# Retell AI Architecture Variants\n\n## Overview\nThree validated architecture blueprints for Retell AI integrations.\n\n## Prerequisites\n- Understanding of team size and DAU requirements\n- Knowledge of deployment infrastructure\n- Clear SLA requirements\n- Growth projections available\n\n## Variant A: Monolith (Simple)\n\n**Best for:** MVPs, small teams, \u003C 10K daily active users\n\n```\nmy-app\u002F\n├── src\u002F\n│   ├── retellai\u002F\n│   │   ├── client.ts          # Singleton client\n│   │   ├── types.ts           # Types\n│   │   └── middleware.ts      # Express middleware\n│   ├── routes\u002F\n│   │   └── api\u002F\n│   │       └── retellai.ts    # API routes\n│   └── index.ts\n├── tests\u002F\n│   └── retellai.test.ts\n└── package.json\n```\n\n### Key Characteristics\n- Single deployment unit\n- Synchronous Retell AI calls in request path\n- In-memory caching\n- Simple error handling\n\n### Code Pattern\n```typescript\n\u002F\u002F Direct integration in route handler\napp.post('\u002Fapi\u002Fcreate', async (req, res) => {\n  try {\n    const result = await retellaiClient.create(req.body);\n    res.json(result);\n  } catch (error) {\n    res.status(500).json({ error: error.message });\n  }\n});\n```\n\n---\n\n## Variant B: Service Layer (Moderate)\n\n**Best for:** Growing startups, 10K-100K DAU, multiple integrations\n\n```\nmy-app\u002F\n├── src\u002F\n│   ├── services\u002F\n│   │   ├── retellai\u002F\n│   │   │   ├── client.ts      # Client wrapper\n│   │   │   ├── service.ts     # Business logic\n│   │   │   ├── repository.ts  # Data access\n│   │   │   └── types.ts\n│   │   └── index.ts           # Service exports\n│   ├── controllers\u002F\n│   │   └── retellai.ts\n│   ├── routes\u002F\n│   ├── middleware\u002F\n│   ├── queue\u002F\n│   │   └── retellai-processor.ts  # Async processing\n│   └── index.ts\n├── config\u002F\n│   └── retellai\u002F\n└── package.json\n```\n\n### Key Characteristics\n- Separation of concerns\n- Background job processing\n- Redis caching\n- Circuit breaker pattern\n- Structured error handling\n\n### Code Pattern\n```typescript\n\u002F\u002F Service layer abstraction\nclass Retell AIService {\n  constructor(\n    private client: RetellAIClient,\n    private cache: CacheService,\n    private queue: QueueService\n  ) {}\n\n  async createResource(data: CreateInput): Promise\u003CResource> {\n    \u002F\u002F Business logic before API call\n    const validated = this.validate(data);\n\n    \u002F\u002F Check cache\n    const cached = await this.cache.get(cacheKey);\n    if (cached) return cached;\n\n    \u002F\u002F API call with retry\n    const result = await this.withRetry(() =>\n      this.client.create(validated)\n    );\n\n    \u002F\u002F Cache result\n    await this.cache.set(cacheKey, result, 300);\n\n    \u002F\u002F Async follow-up\n    await this.queue.enqueue('retellai.post-create', result);\n\n    return result;\n  }\n}\n```\n\n---\n\n## Variant C: Microservice (Complex)\n\n**Best for:** Enterprise, 100K+ DAU, strict SLAs\n\n```\nretellai-service\u002F              # Dedicated microservice\n├── src\u002F\n│   ├── api\u002F\n│   │   ├── grpc\u002F\n│   │   │   └── retellai.proto\n│   │   └── rest\u002F\n│   │       └── routes.ts\n│   ├── domain\u002F\n│   │   ├── entities\u002F\n│   │   ├── events\u002F\n│   │   └── services\u002F\n│   ├── infrastructure\u002F\n│   │   ├── retellai\u002F\n│   │   │   ├── client.ts\n│   │   │   ├── mapper.ts\n│   │   │   └── circuit-breaker.ts\n│   │   ├── cache\u002F\n│   │   ├── queue\u002F\n│   │   └── database\u002F\n│   └── index.ts\n├── config\u002F\n├── k8s\u002F\n│   ├── deployment.yaml\n│   ├── service.yaml\n│   └── hpa.yaml\n└── package.json\n\nother-services\u002F\n├── order-service\u002F       # Calls retellai-service\n├── payment-service\u002F\n└── notification-service\u002F\n```\n\n### Key Characteristics\n- Dedicated Retell AI microservice\n- gRPC for internal communication\n- Event-driven architecture\n- Database per service\n- Kubernetes autoscaling\n- Distributed tracing\n- Circuit breaker per service\n\n### Code Pattern\n```typescript\n\u002F\u002F Event-driven with domain isolation\nclass Retell AIAggregate {\n  private events: DomainEvent[] = [];\n\n  process(command: Retell AICommand): void {\n    \u002F\u002F Domain logic\n    const result = this.execute(command);\n\n    \u002F\u002F Emit domain event\n    this.events.push(new Retell AIProcessedEvent(result));\n  }\n\n  getUncommittedEvents(): DomainEvent[] {\n    return [...this.events];\n  }\n}\n\n\u002F\u002F Event handler\n@EventHandler(Retell AIProcessedEvent)\nclass Retell AIEventHandler {\n  async handle(event: Retell AIProcessedEvent): Promise\u003Cvoid> {\n    \u002F\u002F Saga orchestration\n    await this.sagaOrchestrator.continue(event);\n  }\n}\n```\n\n---\n\n## Decision Matrix\n\n| Factor | Monolith | Service Layer | Microservice |\n|--------|----------|---------------|--------------|\n| Team Size | 1-5 | 5-20 | 20+ |\n| DAU | \u003C 10K | 10K-100K | 100K+ |\n| Deployment Frequency | Weekly | Daily | Continuous |\n| Failure Isolation | None | Partial | Full |\n| Operational Complexity | Low | Medium | High |\n| Time to Market | Fastest | Moderate | Slowest |\n\n## Migration Path\n\n```\nMonolith → Service Layer:\n1. Extract Retell AI code to service\u002F\n2. Add caching layer\n3. Add background processing\n\nService Layer → Microservice:\n1. Create dedicated retellai-service repo\n2. Define gRPC contract\n3. Add event bus\n4. Deploy to Kubernetes\n5. Migrate traffic gradually\n```\n\n## Instructions\n\n### Step 1: Assess Requirements\nUse the decision matrix to identify appropriate variant.\n\n### Step 2: Choose Architecture\nSelect Monolith, Service Layer, or Microservice based on needs.\n\n### Step 3: Implement Structure\nSet up project layout following the chosen blueprint.\n\n### Step 4: Plan Migration Path\nDocument upgrade path for future scaling.\n\n## Output\n- Architecture variant selected\n- Project structure implemented\n- Migration path documented\n- Appropriate patterns applied\n\n## Error Handling\n| Issue | Cause | Solution |\n|-------|-------|----------|\n| Over-engineering | Wrong variant choice | Start simpler |\n| Performance issues | Wrong layer | Add caching\u002Fasync |\n| Team friction | Complex architecture | Simplify or train |\n| Deployment complexity | Microservice overhead | Consider service layer |\n\n## Examples\n\n### Quick Variant Check\n```bash\n# Count team size and DAU to select variant\necho \"Team: $(git log --format='%ae' | sort -u | wc -l) developers\"\necho \"DAU: Check analytics dashboard\"\n```\n\n## Resources\n- [Monolith First](https:\u002F\u002Fmartinfowler.com\u002Fbliki\u002FMonolithFirst.html)\n- [Microservices Guide](https:\u002F\u002Fmartinfowler.com\u002Fmicroservices\u002F)\n- [Retell AI Architecture Guide](https:\u002F\u002Fdocs.retellai.com\u002Farchitecture)\n\n## Next Steps\nFor common anti-patterns, see `retellai-known-pitfalls`.","# Retell AI Architecture Variants\n\n## Overview\nThree validated architecture blueprints for Retell AI integrations.\n\n## Prerequisites\n- Understanding of team size and DAU requirements\n- Knowledge of deployment infrastructure\n- Clear SLA requirements\n- Growth projections available\n\n## Variant A: Monolith (Simple)\n\n**Best for:** MVPs, small teams, \u003C 10K daily active users\n\n```\nmy-app\u002F\n├── src\u002F\n│   ├── retellai\u002F\n│   │   ├── client.ts          # Singleton client\n│   │   ├── types.ts           # Types\n│   │   └── middleware.ts      # Express middleware\n│   ├── routes\u002F\n│   │   └── api\u002F\n│   │       └── retellai.ts    # API routes\n│   └── index.ts\n├── tests\u002F\n│   └── retellai.test.ts\n└── package.json\n```\n\n### Key Characteristics\n- Single deployment unit\n- Synchronous Retell AI calls in request path\n- In-memory caching\n- Simple error handling\n\n### Code Pattern\n```typescript\n\u002F\u002F Direct integration in route handler\napp.post('\u002Fapi\u002Fcreate', async (req, res) => {\n  try {\n    const result = await retellaiClient.create(req.body);\n    res.json(result);\n  } catch (error) {\n    res.status(500).json({ error: error.message });\n  }\n});\n```\n\n---\n\n## Variant B: Service Layer (Moderate)\n\n**Best for:** Growing startups, 10K-100K DAU, multiple integrations\n\n```\nmy-app\u002F\n├── src\u002F\n│   ├── services\u002F\n│   │   ├── retellai\u002F\n│   │   │   ├── client.ts      # Client wrapper\n│   │   │   ├── service.ts     # Business logic\n│   │   │   ├── repository.ts  # Data access\n│   │   │   └── types.ts\n│   │   └── index.ts           # Service exports\n│   ├── controllers\u002F\n│   │   └── retellai.ts\n│   ├── routes\u002F\n│   ├── middleware\u002F\n│   ├── queue\u002F\n│   │   └── retellai-processor.ts  # Async processing\n│   └── index.ts\n├── config\u002F\n│   └── retellai\u002F\n└── package.json\n```\n\n### Key Characteristics\n- Separation of concerns\n- Background job processing\n- Redis caching\n- Circuit breaker pattern\n- Structured error handling\n\n### Code Pattern\n```typescript\n\u002F\u002F Service layer abstraction\nclass Retell AIService {\n  constructor(\n    private client: RetellAIClient,\n    private cache: CacheService,\n    private queue: QueueService\n  ) {}\n\n  async createResource(data: CreateInput): Promise\u003CResource> {\n    \u002F\u002F Business logic before API call\n    const validated = this.validate(data);\n\n    \u002F\u002F Check cache\n    const cached = await this.cache.get(cacheKey);\n    if (cached) return cached;\n\n    \u002F\u002F API call with retry\n    const result = await this.withRetry(() =>\n      this.client.create(validated)\n    );\n\n    \u002F\u002F Cache result\n    await this.cache.set(cacheKey, result, 300);\n\n    \u002F\u002F Async follow-up\n    await this.queue.enqueue('retellai.post-create', result);\n\n    return result;\n  }\n}\n```\n\n---\n\n## Variant C: Microservice (Complex)\n\n**Best for:** Enterprise, 100K+ DAU, strict SLAs\n\n```\nretellai-service\u002F              # Dedicated microservice\n├── src\u002F\n│   ├── api\u002F\n│   │   ├── grpc\u002F\n│   │   │   └── retellai.proto\n│   │   └── rest\u002F\n│   │       └── routes.ts\n│   ├── domain\u002F\n│   │   ├── entities\u002F\n│   │   ├── events\u002F\n│   │   └── services\u002F\n│   ├── infrastructure\u002F\n│   │   ├── retellai\u002F\n│   │   │   ├── client.ts\n│   │   │   ├── mapper.ts\n│   │   │   └── circuit-breaker.ts\n│   │   ├── cache\u002F\n│   │   ├── queue\u002F\n│   │   └── database\u002F\n│   └── index.ts\n├── config\u002F\n├── k8s\u002F\n│   ├── deployment.yaml\n│   ├── service.yaml\n│   └── hpa.yaml\n└── package.json\n\nother-services\u002F\n├── order-service\u002F       # Calls retellai-service\n├── payment-service\u002F\n└── notification-service\u002F\n```\n\n### Key Characteristics\n- Dedicated Retell AI microservice\n- gRPC for internal communication\n- Event-driven architecture\n- Database per service\n- Kubernetes autoscaling\n- Distributed tracing\n- Circuit breaker per service\n\n### Code Pattern\n```typescript\n\u002F\u002F Event-driven with domain isolation\nclass Retell AIAggregate {\n  private events: DomainEvent[] = [];\n\n  process(command: Retell AICommand): void {\n    \u002F\u002F Domain logic\n    const result = this.execute(command);\n\n    \u002F\u002F Emit domain event\n    this.events.push(new Retell AIProcessedEvent(result));\n  }\n\n  getUncommittedEvents(): DomainEvent[] {\n    return [...this.events];\n  }\n}\n\n\u002F\u002F Event handler\n@EventHandler(Retell AIProcessedEvent)\nclass Retell AIEventHandler {\n  async handle(event: Retell AIProcessedEvent): Promise\u003Cvoid> {\n    \u002F\u002F Saga orchestration\n    await this.sagaOrchestrator.continue(event);\n  }\n}\n```\n\n---\n\n## Decision Matrix\n\n| Factor | Monolith | Service Layer | Microservice |\n|--------|----------|---------------|--------------|\n| Team Size | 1-5 | 5-20 | 20+ |\n| DAU | \u003C 10K | 10K-100K | 100K+ |\n| Deployment Frequency | Weekly | Daily | Continuous |\n| Failure Isolation | None | Partial | Full |\n| Operational Complexity | Low | Medium | High |\n| Time to Market | Fastest | Moderate | Slowest |\n\n## Migration Path\n\n```\nMonolith → Service Layer:\n1. Extract Retell AI code to service\u002F\n2. Add caching layer\n3. Add background processing\n\nService Layer → Microservice:\n1. Create dedicated retellai-service repo\n2. Define gRPC contract\n3. Add event bus\n4. Deploy to Kubernetes\n5. Migrate traffic gradually\n```\n\n## Instructions\n\n### Step 1: Assess Requirements\nUse the decision matrix to identify appropriate variant.\n\n### Step 2: Choose Architecture\nSelect Monolith, Service Layer, or Microservice based on needs.\n\n### Step 3: Implement Structure\nSet up project layout following the chosen blueprint.\n\n### Step 4: Plan Migration Path\nDocument upgrade path for future scaling.\n\n## Output\n- Architecture variant selected\n- Project structure implemented\n- Migration path documented\n- Appropriate patterns applied\n\n## Error Handling\n| Issue | Cause | Solution |\n|-------|-------|----------|\n| Over-engineering | Wrong variant choice | Start simpler |\n| Performance issues | Wrong layer | Add caching\u002Fasync |\n| Team friction | Complex architecture | Simplify or train |\n| Deployment complexity | Microservice overhead | Consider service layer |\n\n## Examples\n\n### Quick Variant Check\n```bash\n# Count team size and DAU to select variant\necho \"Team: $(git log --format='%ae' | sort -u | wc -l) developers\"\necho \"DAU: Check analytics dashboard\"\n```\n\n## Resources\n- [Monolith First](https:\u002F\u002Fmartinfowler.com\u002Fbliki\u002FMonolithFirst.html)\n- [Microservices Guide](https:\u002F\u002Fmartinfowler.com\u002Fmicroservices\u002F)\n- [Retell AI Architecture Guide](https:\u002F\u002Fdocs.retellai.com\u002Farchitecture)\n\n## Next Steps\nFor common anti-patterns, see `retellai-known-pitfalls`.",{"name":5,"author":49,"license":15,"version":50,"description":51,"allowed-tools":52},"Jeremy Longshore \u003Cjeremy@intentsolutions.io>","1.0.0","Choose and implement Retell AI validated architecture blueprints for different scales.\nUse when designing new Retell AI integrations, choosing between monolith\u002Fservice\u002Fmicroservice\narchitectures, or planning migration paths for Retell AI applications.\nTrigger with phrases like \"retellai architecture\", \"retellai blueprint\",\n\"how to structure retellai\", \"retellai project layout\", \"retellai microservice\".\n","Read, Grep","main","90b50ed0778745a80fd5bfb13b1843d45b9e82f7","tests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fsaas-packs\u002Fretellai-pack\u002Fskills\u002Fretellai-architecture-variants",234,"https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust","https:\u002F\u002Fgithub.com\u002FDicklesworthstone","https:\u002F\u002Fwww.jeffreyemanuel.com\u002F","Building in NY","2026-11-13 11:50:27",{"source":8,"skillName":5,"commands":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add Dicklesworthstone\u002Fpi_agent_rust --skill retellai-architecture-variants",{"tool":68,"command":69},"bunx","bunx skills add Dicklesworthstone\u002Fpi_agent_rust --skill retellai-architecture-variants",{"tool":71,"command":72},"pnpm","pnpm dlx skills add Dicklesworthstone\u002Fpi_agent_rust --skill retellai-architecture-variants","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Fplugins-community\u002Fplugins\u002Fsaas-packs\u002Fretellai-pack\u002Fskills\u002Fretellai-architecture-variants\u002FSKILL.md",true,[],[77,116,139,165,190,212],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":81,"licenseFileUrl":82,"sourceHtmlUrl":83,"categories":84,"keywords":90,"publishedAt":44},"01M6FEGPPK0ZTKCCM0R3E3KS33","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). 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Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[123,126],{"categoryNo":124,"categoryName":125,"taxonomyType":88,"taxonomyTypeDesc":89},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":86,"categoryName":87,"taxonomyType":88,"taxonomyTypeDesc":89},[128,129,130,96,131,132,92,133,134,135,106,136,41,137,138,26,25],"testing","webapp testing","webapp","screenshots","playwright","debugging","toolkit","browser","logs","behavior","local",{"skillNo":140,"name":141,"nameZh":6,"description":142,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":143,"sourceHtmlUrl":144,"categories":145,"keywords":146,"publishedAt":44},"01M6FE20F7ZTQBFWKF4MPJ2SBX","neuropixels-analysis","Neuropixels neural recording analysis. Load SpikeGLX\u002FOpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen\u002FIBL curation, AI-assisted visual analysis, for Neuropixels 1.0\u002F2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fneuropixels-analysis\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fneuropixels-analysis\u002FSKILL.md",[],[147,148,149,150,151,152,153,154,36,155,156,157,158,159,160,161,26,162,163,164,25,24],"analysis","neuropixels analysis","spike","metrics","data","open","quality","user","recordings","Neural","motion","visual","quality metrics","unit","sorting","curation","recording","load",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":81,"licenseFileUrl":169,"sourceHtmlUrl":170,"categories":171,"keywords":175,"publishedAt":44},"01M6FDP0PDK6F1GGX395C7625Z","skill-creator","Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FSKILL.md",[172],{"categoryNo":173,"categoryName":174,"taxonomyType":88,"taxonomyTypeDesc":89},"01JBSEEDCAT000000000000023","Skill与智能体开发",[176,177,178,179,180,181,182,183,39,184,185,186,187,188,26,25,189,24,22],"skill creator","creator","workflows","create","update","capabilities","knowledge","tool","specialized","guide","new skill","existing skill","users","Existing",{"skillNo":191,"name":192,"nameZh":6,"description":193,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":194,"sourceHtmlUrl":195,"categories":196,"keywords":198,"publishedAt":44},"01M6FDM8Y7VK7NYG9SM7QPA3PW","notebooklm","Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation, library management, persistent auth. Drastically reduced hallucinations through document-only responses.","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnotebooklm\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fnotebooklm\u002FSKILL.md",[197],{"categoryNo":173,"categoryName":174,"taxonomyType":88,"taxonomyTypeDesc":89},[192,199,200,201,202,203,204,135,205,206,36,207,208,209,210,25,211],"google","browser automation","gemini","automation","auth","code","library","management","only","document","query","Citation","source",{"skillNo":213,"name":214,"nameZh":6,"description":215,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":81,"licenseFileUrl":216,"sourceHtmlUrl":217,"categories":218,"keywords":225,"publishedAt":44},"01M6FDHPXE10J3XGTCH5HBHS1M","slack-gif-creator","Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like \"make me a GIF of X doing Y for Slack.\"","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FDicklesworthstone\u002Fpi_agent_rust\u002Fblob\u002Fmain\u002Ftests\u002Fext_conformance\u002Fartifacts\u002Ftemplates-davila7\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fenterprise-communication\u002Fslack-gif-creator\u002FSKILL.md",[219,222],{"categoryNo":220,"categoryName":221,"taxonomyType":88,"taxonomyTypeDesc":89},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":223,"categoryName":224,"taxonomyType":88,"taxonomyTypeDesc":89},"01JBSEEDCAT000000000000016","图像与多媒体",[226,227,228,177,229,107,230,231,182,36,232,188,233,234,235,25,236,24],"slack gif creator","slack","gif","validation","constraints","Animation","me","make","Utilities","concepts","request"]