Topic: claude
14,433 skills in this topic.
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model-serving
LLM and ML model deployment for inference. Use when serving models in production, building AI APIs, or optimizing inference. Covers vLLM (LLM serving), TensorRT-LLM (GPU optimization), Ollama (local), BentoML (ML deployment), Triton (multi-model), LangChain (orchestration), LlamaIndex (RAG), and streaming patterns.
ancoleman/ai-design-components 333
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implementing-tls
Configure TLS certificates and encryption for secure communications. Use when setting up HTTPS, securing service-to-service connections, implementing mutual TLS (mTLS), or debugging certificate issues.
ancoleman/ai-design-components 333
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deploying-on-gcp
Implement applications using Google Cloud Platform (GCP) services. Use when building on GCP infrastructure, selecting compute/storage/database services, designing data analytics pipelines, implementing ML workflows, or architecting cloud-native applications with BigQuery, Cloud Run, GKE, Vertex AI, and other GCP services.
ancoleman/ai-design-components 333
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managing-incidents
Guide incident response from detection to post-mortem using SRE principles, severity classification, on-call management, blameless culture, and communication protocols. Use when setting up incident processes, designing escalation policies, or conducting post-mortems.
ancoleman/ai-design-components 333
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implementing-drag-drop
Implements drag-and-drop and sortable interfaces with React/TypeScript including kanban boards, sortable lists, file uploads, and reorderable grids. Use when building interactive UIs requiring direct manipulation, spatial organization, or touch-friendly reordering.
ancoleman/ai-design-components 333
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configuring-nginx
Configure nginx for static sites, reverse proxying, load balancing, SSL/TLS termination, caching, and performance tuning. When setting up web servers, application proxies, or load balancers, this skill provides production-ready patterns with modern security best practices for TLS 1.3, rate limiting, and security headers.
ancoleman/ai-design-components 333
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using-timeseries-databases
Time-series database implementation for metrics, IoT, financial data, and observability backends. Use when building dashboards, monitoring systems, IoT platforms, or financial applications. Covers TimescaleDB (PostgreSQL), InfluxDB, ClickHouse, QuestDB, continuous aggregates, downsampling (LTTB), and retention policies.
ancoleman/ai-design-components 333
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using-relational-databases
Relational database implementation across Python, Rust, Go, and TypeScript. Use when building CRUD applications, transactional systems, or structured data storage. Covers PostgreSQL (primary), MySQL, SQLite, ORMs (SQLAlchemy, Prisma, SeaORM, GORM), query builders (Drizzle, sqlc, SQLx), migrations, connection pooling, and serverless databases (Neon, PlanetScale, Turso).
ancoleman/ai-design-components 333
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using-document-databases
Document database implementation for flexible schema applications. Use when building content management, user profiles, catalogs, or event logging. Covers MongoDB (primary), DynamoDB, Firestore, schema design patterns, indexing strategies, and aggregation pipelines.
ancoleman/ai-design-components 333
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embedding-optimization
Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings.
ancoleman/ai-design-components 333
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swarm-local-e2e
Guide for running local E2E tests with API server, Docker lead/worker containers, task creation, log verification, UI dashboard, and cleanup
desplega-ai/agent-swarm 335
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implement-issue
Implement a GitHub issue or GitLab issue and create a PR/MR
desplega-ai/agent-swarm 335
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user-management
How to manage the user registry — creating users for new Slack/GitHub/GitLab identities, managing aliases, resolving users across platforms. Use when a new human interacts with the swarm or when user identity needs updating.
desplega-ai/agent-swarm 335
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todos
Handle the agent personal todos.md file
desplega-ai/agent-swarm 335
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review-offered-task
Review a task that has been offered to you and decide whether to accept or reject it
desplega-ai/agent-swarm 335
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respond-github
Respond to a GitHub issue/PR or GitLab issue/MR
desplega-ai/agent-swarm 335
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investigate-sentry-issue
Investigate and triage a Sentry error issue
desplega-ai/agent-swarm 335
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start-worker
Start an Agent Swarm Worker
desplega-ai/agent-swarm 335
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start-leader
Start the Agent Swarm Leader
desplega-ai/agent-swarm 335
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swarm-chat
Effective communication within the agent swarm using internal Slack
desplega-ai/agent-swarm 335
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review-pr
Review a pull request (GitHub) or merge request (GitLab) and provide detailed feedback
desplega-ai/agent-swarm 335
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create-pr
Create a pull request (GitHub) or merge request (GitLab) from the current branch
desplega-ai/agent-swarm 335
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work-on-task
Work on a specific task assigned to you in the agent swarm
desplega-ai/agent-swarm 335
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close-issue
Close a GitHub or GitLab issue with a summary comment
desplega-ai/agent-swarm 335