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Topic: skills

17,247 skills in this topic.

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
providing-feedback Implements feedback and notification systems including toasts, alerts, modals, progress indicators, and error states. Use when communicating system state, displaying messages, confirming actions, or showing errors.
ancoleman/ai-design-components 333
implementing-search-filter Implements search and filter interfaces for both frontend (React/TypeScript) and backend (Python) with debouncing, query management, and database integration. Use when adding search functionality, building filter UIs, implementing faceted search, or optimizing search performance.
ancoleman/ai-design-components 333
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
managing-media Implements media and file management components including file upload (drag-drop, multi-file, resumable), image galleries (lightbox, carousel, masonry), video players (custom controls, captions, adaptive streaming), audio players (waveform, playlists), document viewers (PDF, Office), and optimization strategies (compression, responsive images, lazy loading, CDN). Use when handling files, displaying media, or building rich content experiences.
ancoleman/ai-design-components 333
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
generating-documentation Generate comprehensive technical documentation including API docs (OpenAPI/Swagger), code documentation (TypeDoc/Sphinx), documentation sites (Docusaurus/MkDocs), Architecture Decision Records (ADRs), and diagrams (Mermaid/PlantUML). Use when documenting APIs, libraries, systems architecture, or building developer-facing documentation sites.
ancoleman/ai-design-components 333
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
platform-engineering Design and implement Internal Developer Platforms (IDPs) with self-service capabilities, golden paths, and developer experience optimization. Covers platform strategy, IDP architecture (Backstage, Port), infrastructure orchestration (Crossplane), GitOps (Argo CD), and adoption patterns. Use when building developer platforms, improving DevEx, or establishing platform teams.
ancoleman/ai-design-components 333
creating-dashboards Creates comprehensive dashboard and analytics interfaces that combine data visualization, KPI cards, real-time updates, and interactive layouts. Use this skill when building business intelligence dashboards, monitoring systems, executive reports, or any interface that requires multiple coordinated data displays with filters, metrics, and visualizations working together.
ancoleman/ai-design-components 333
ai-data-engineering Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).
ancoleman/ai-design-components 333
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
implementing-navigation Implements navigation patterns and routing for both frontend (React/TS) and backend (Python) including menus, tabs, breadcrumbs, client-side routing, and server-side route configuration. Use when building navigation systems or setting up routing.
ancoleman/ai-design-components 333
displaying-timelines Displays chronological events and activity through timelines, activity feeds, Gantt charts, and calendar interfaces. Use when showing historical events, project schedules, social feeds, notifications, audit logs, or time-based data. Provides implementation patterns for vertical/horizontal timelines, interactive visualizations, real-time updates, and responsive designs with accessibility (WCAG/ARIA).
ancoleman/ai-design-components 333
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
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
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
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
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
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
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
building-ai-chat Builds AI chat interfaces and conversational UI with streaming responses, context management, and multi-modal support. Use when creating ChatGPT-style interfaces, AI assistants, code copilots, or conversational agents. Handles streaming text, token limits, regeneration, feedback loops, tool usage visualization, and AI-specific error patterns. Provides battle-tested components from leading AI products with accessibility and performance built in.
ancoleman/ai-design-components 333
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
administering-linux Manage Linux systems covering systemd services, process management, filesystems, networking, performance tuning, and troubleshooting. Use when deploying applications, optimizing server performance, diagnosing production issues, or managing users and security on Linux servers.
ancoleman/ai-design-components 333