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Topic: skills

17,247 skills in this topic.

amex-travel
borski/travel-hacking-toolkit 387
wheretocredit Look up frequent flyer mileage earning rates by airline and booking class via wheretocredit.com. Shows both redeemable miles and qualifying/status miles across 50+ loyalty programs. Use when crediting flights, comparing programs, or deciding where to credit a ticket.
borski/travel-hacking-toolkit 387
building-clis Build professional command-line interfaces in Python, Go, and Rust using modern frameworks like Typer, Cobra, and clap. Use when creating developer tools, automation scripts, or infrastructure management CLIs with robust argument parsing, interactive features, and multi-platform distribution.
ancoleman/ai-design-components 333
implementing-observability Monitoring, logging, and tracing implementation using OpenTelemetry as the unified standard. Use when building production systems requiring visibility into performance, errors, and behavior. Covers OpenTelemetry (metrics, logs, traces), Prometheus, Grafana, Loki, Jaeger, Tempo, structured logging (structlog, tracing, slog, pino), and alerting.
ancoleman/ai-design-components 333
using-vector-databases Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.
ancoleman/ai-design-components 333
architecting-security Design comprehensive security architectures using defense-in-depth, zero trust principles, threat modeling (STRIDE, PASTA), and control frameworks (NIST CSF, CIS Controls, ISO 27001). Use when designing security for new systems, auditing existing architectures, or establishing security governance programs.
ancoleman/ai-design-components 333
deploying-on-azure Design and implement Azure cloud architectures using best practices for compute, storage, databases, AI services, networking, and governance. Use when building applications on Microsoft Azure or migrating workloads to Azure cloud platform.
ancoleman/ai-design-components 333
managing-git-workflows Manage Git branching strategies, commit conventions, and collaboration workflows. Use when choosing between trunk-based development, GitHub Flow, or GitFlow, implementing conventional commits for automated versioning, setting up Git hooks for quality gates, or organizing monorepos with clear ownership.
ancoleman/ai-design-components 333
managing-dns Manage DNS records, TTL strategies, and DNS-as-code automation for infrastructure. Use when configuring domain resolution, automating DNS from Kubernetes with external-dns, setting up DNS-based load balancing, or troubleshooting propagation issues across cloud providers (Route53, Cloud DNS, Azure DNS, Cloudflare).
ancoleman/ai-design-components 333
writing-dockerfiles Writing optimized, secure, multi-stage Dockerfiles with language-specific patterns (Python, Node.js, Go, Rust), BuildKit features, and distroless images. Use when containerizing applications, optimizing existing Dockerfiles, or reducing image sizes.
ancoleman/ai-design-components 333
managing-configuration Guide users through creating, managing, and testing server configuration automation using Ansible. When automating server configurations, deploying applications with Ansible playbooks, managing dynamic inventories for cloud environments, or testing roles with Molecule, this skill provides idempotency patterns, secrets management with ansible-vault and HashiCorp Vault, and GitOps workflows for configuration as code.
ancoleman/ai-design-components 333
using-message-queues Async communication patterns using message brokers and task queues. Use when building event-driven systems, background job processing, or service decoupling. Covers Kafka (event streaming), RabbitMQ (complex routing), NATS (cloud-native), Redis Streams, Celery (Python), BullMQ (TypeScript), Temporal (workflows), and event sourcing patterns.
ancoleman/ai-design-components 333
visualizing-data Builds dashboards, reports, and data-driven interfaces requiring charts, graphs, or visual analytics. Provides systematic framework for selecting appropriate visualizations based on data characteristics and analytical purpose. Includes 24+ visualization types organized by purpose (trends, comparisons, distributions, relationships, flows, hierarchies, geospatial), accessibility patterns (WCAG 2.1 AA compliance), colorblind-safe palettes, and performance optimization strategies. Use when creating visualizations, choosing chart types, displaying data graphically, or designing data interfaces.
ancoleman/ai-design-components 333
building-forms Builds form components and data collection interfaces including contact forms, registration flows, checkout processes, surveys, and settings pages. Includes 50+ input types, validation strategies, accessibility patterns (WCAG 2.1), multi-step wizards, and UX best practices. Provides decision trees from data type to component selection, validation timing guidance, and error handling patterns. Use when creating forms, collecting user input, building surveys, implementing validation, designing multi-step workflows, or ensuring form accessibility.
ancoleman/ai-design-components 333
writing-github-actions Write GitHub Actions workflows with proper syntax, reusable workflows, composite actions, matrix builds, caching, and security best practices. Use when creating CI/CD workflows for GitHub-hosted projects or automating GitHub repository tasks.
ancoleman/ai-design-components 333
implementing-service-mesh Implement production-ready service mesh deployments with Istio, Linkerd, or Cilium. Configure mTLS, authorization policies, traffic routing, and progressive delivery patterns for secure, observable microservices. Use when setting up service-to-service communication, implementing zero-trust security, or enabling canary deployments.
ancoleman/ai-design-components 333
resource-tagging Apply and enforce cloud resource tagging strategies across AWS, Azure, GCP, and Kubernetes for cost allocation, ownership tracking, compliance, and automation. Use when implementing cloud governance, optimizing costs, or automating infrastructure management.
ancoleman/ai-design-components 333
deploying-on-aws Selecting and implementing AWS services and architectural patterns. Use when designing AWS cloud architectures, choosing compute/storage/database services, implementing serverless or container patterns, or applying AWS Well-Architected Framework principles.
ancoleman/ai-design-components 333
implementing-realtime-sync Real-time communication patterns for live updates, collaboration, and presence. Use when building chat applications, collaborative tools, live dashboards, or streaming interfaces (LLM responses, metrics). Covers SSE (server-sent events for one-way streams), WebSocket (bidirectional communication), WebRTC (peer-to-peer video/audio), CRDTs (Yjs, Automerge for conflict-free collaboration), presence patterns, offline sync, and scaling strategies. Supports Python, Rust, Go, and TypeScript.
ancoleman/ai-design-components 333
designing-distributed-systems When designing distributed systems for scalability, reliability, and consistency. Covers CAP/PACELC theorems, consistency models (strong, eventual, causal), replication patterns (leader-follower, multi-leader, leaderless), partitioning strategies (hash, range, geographic), transaction patterns (saga, event sourcing, CQRS), resilience patterns (circuit breaker, bulkhead), service discovery, and caching strategies for building fault-tolerant distributed architectures.
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
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
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
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