Topic: typescript
2,004 skills in this topic.
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security-hardening
Reduces attack surface across OS, container, cloud, network, and database layers using CIS Benchmarks and zero-trust principles. Use when hardening production infrastructure, meeting compliance requirements, or implementing defense-in-depth security.
ancoleman/ai-design-components 333
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transforming-data
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
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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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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implementing-api-patterns
API design and implementation across REST, GraphQL, gRPC, and tRPC patterns. Use when building backend services, public APIs, or service-to-service communication. Covers REST frameworks (FastAPI, Axum, Gin, Hono), GraphQL libraries (Strawberry, async-graphql, gqlgen, Pothos), gRPC (Tonic, Connect-Go), tRPC for TypeScript, pagination strategies (cursor-based, offset-based), rate limiting, caching, versioning, and OpenAPI documentation generation. Includes frontend integration patterns for forms, tables, dashboards, and ai-chat skills.
ancoleman/ai-design-components 333
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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
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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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performance-engineering
When validating system performance under load, identifying bottlenecks through profiling, or optimizing application responsiveness. Covers load testing (k6, Locust), profiling (CPU, memory, I/O), and optimization strategies (caching, query optimization, Core Web Vitals). Use for capacity planning, regression detection, and establishing performance SLOs.
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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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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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
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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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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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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
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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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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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ralph
Plan work and generate ralph task files for autonomous execution. Use when the user wants to plan tasks, create a prd, run ralph, or fix a batch of issues. Triggers on: ralph, create tasks, plan this, run ralph, prd.
opentabs-dev/opentabs 335
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build-plugin
Complete plugin development workflow: build, test, icon, troubleshoot, and setup. Use when the user wants to build a plugin, create a plugin, troubleshoot issues, add icons, or install/configure plugins. Triggers on: build plugin, create plugin, develop plugin, new plugin, plugin icon, troubleshoot, debug, setup plugin, install plugin.
opentabs-dev/opentabs 335
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ralph
Plan work and generate ralph task files for autonomous execution. Use when the user wants to plan tasks, create a prd, run ralph, or fix a batch of issues. Triggers on: ralph, create tasks, plan this, run ralph, prd.
opentabs-dev/opentabs 335
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build-plugin
Complete plugin development workflow: build, test, icon, troubleshoot, and setup. Use when the user wants to build a plugin, create a plugin, troubleshoot issues, add icons, or install/configure plugins. Triggers on: build plugin, create plugin, develop plugin, new plugin, plugin icon, troubleshoot, debug, setup plugin, install plugin.
opentabs-dev/opentabs 335
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bump-version
Bump package versions across all platform packages and plugins in lockstep. Use when the user wants to bump versions, update versions, or prepare a release. Triggers on: bump version, bump versions, version bump, update version, prepare release.
opentabs-dev/opentabs 335
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bump-version
Bump package versions across all platform packages and plugins in lockstep. Use when the user wants to bump versions, update versions, or prepare a release. Triggers on: bump version, bump versions, version bump, update version, prepare release.
opentabs-dev/opentabs 335
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report
Investigate bugs comprehensively — cascade through /trace, capture browser evidence, extract observability data, and auto-create a GitHub issue with all findings.
automagik-dev/genie 280
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dream
Batch-execute SHIP-ready wishes overnight — pick wishes, orchestrate workers, review PRs, wake up to results.
automagik-dev/genie 280