Topic: skills
17,247 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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managing-secrets
Managing secrets (API keys, database credentials, certificates) with Vault, cloud providers, and Kubernetes. Use when storing sensitive data, rotating credentials, syncing secrets to Kubernetes, implementing dynamic secrets, or scanning code for leaked secrets.
ancoleman/ai-design-components 333
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debugging-techniques
Debugging workflows for Python (pdb, debugpy), Go (delve), Rust (lldb), and Node.js, including container debugging (kubectl debug, ephemeral containers) and production-safe debugging techniques with distributed tracing and correlation IDs. Use when setting breakpoints, debugging containers/pods, remote debugging, or production debugging.
ancoleman/ai-design-components 333
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guiding-users
Implements onboarding and help systems including product tours, interactive tutorials, tooltips, checklists, help panels, and progressive disclosure patterns. Use when building first-time experiences, feature discovery, guided walkthroughs, contextual help, setup flows, or user activation features. Provides timing strategies, accessibility patterns (keyboard, screen readers, reduced motion), and metrics for measuring onboarding success.
ancoleman/ai-design-components 333
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load-balancing-patterns
When distributing traffic across multiple servers or regions, use this skill to select and configure the appropriate load balancing solution (L4/L7, cloud-managed, self-managed, or Kubernetes ingress) with proper health checks and session management.
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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evaluating-llms
Evaluate LLM systems using automated metrics, LLM-as-judge, and benchmarks. Use when testing prompt quality, validating RAG pipelines, measuring safety (hallucinations, bias), or comparing models for production deployment.
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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designing-apis
Design APIs that are secure, scalable, and maintainable using RESTful, GraphQL, and event-driven patterns. Use when designing new APIs, evolving existing APIs, or establishing API standards for teams.
ancoleman/ai-design-components 333
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architecting-networks
Design cloud network architectures with VPC patterns, subnet strategies, zero trust principles, and hybrid connectivity. Use when planning VPC topology, implementing multi-cloud networking, or establishing secure network segmentation for cloud workloads.
ancoleman/ai-design-components 333
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implementing-mlops
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
ancoleman/ai-design-components 333
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designing-layouts
Designs layout systems and responsive interfaces including grid systems, flexbox patterns, sidebar layouts, and responsive breakpoints. Use when structuring app layouts, building responsive designs, or creating complex page structures.
ancoleman/ai-design-components 333
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architecting-data
Strategic guidance for designing modern data platforms, covering storage paradigms (data lake, warehouse, lakehouse), modeling approaches (dimensional, normalized, data vault, wide tables), data mesh principles, and medallion architecture patterns. Use when architecting data platforms, choosing between centralized vs decentralized patterns, selecting table formats (Iceberg, Delta Lake), or designing data governance frameworks.
ancoleman/ai-design-components 333
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optimizing-sql
Optimize SQL query performance through EXPLAIN analysis, indexing strategies, and query rewriting for PostgreSQL, MySQL, and SQL Server. Use when debugging slow queries, analyzing execution plans, or improving database performance.
ancoleman/ai-design-components 333
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operating-kubernetes
Operating production Kubernetes clusters effectively with resource management, advanced scheduling, networking, storage, security hardening, and autoscaling. Use when deploying workloads to Kubernetes, configuring cluster resources, implementing security policies, or troubleshooting operational issues.
ancoleman/ai-design-components 333
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planning-disaster-recovery
Design and implement disaster recovery strategies with RTO/RPO planning, database backups, Kubernetes DR, cross-region replication, and chaos engineering testing. Use when implementing backup systems, configuring point-in-time recovery, setting up multi-region failover, or validating DR procedures.
ancoleman/ai-design-components 333
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securing-authentication
Authentication, authorization, and API security implementation. Use when building user systems, protecting APIs, or implementing access control. Covers OAuth 2.1/OIDC, JWT patterns, sessions, Passkeys/WebAuthn, RBAC/ABAC/ReBAC, policy engines (OPA, Casbin, SpiceDB), managed auth (Clerk, Auth0), self-hosted (Keycloak, Ory), and API security best practices.
ancoleman/ai-design-components 333
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designing-sdks
Design production-ready SDKs with retry logic, error handling, pagination, and multi-language support. Use when building client libraries for APIs or creating developer-facing SDK interfaces.
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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assembling-components
Assembles component outputs from AI Design Components skills into unified, production-ready component systems with validated token integration, proper import chains, and framework-specific scaffolding. Use as the capstone skill after running theming, layout, dashboard, data-viz, or feedback skills to wire components into working React/Next.js, Python, or Rust projects.
ancoleman/ai-design-components 333
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ingesting-data
Data ingestion patterns for loading data from cloud storage, APIs, files, and streaming sources into databases. Use when importing CSV/JSON/Parquet files, pulling from S3/GCS buckets, consuming API feeds, or building ETL pipelines.
ancoleman/ai-design-components 333
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prompt-engineering
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).
ancoleman/ai-design-components 333
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building-tables
Builds tables and data grids for displaying tabular information, from simple HTML tables to complex enterprise data grids. Use when creating tables, implementing sorting/filtering/pagination, handling large datasets (10-1M+ rows), building spreadsheet-like interfaces, or designing data-heavy components. Provides performance optimization strategies, accessibility patterns (WCAG/ARIA), responsive designs, and library recommendations (TanStack Table, AG Grid).
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