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Found 2,259 Skills
Systematic debugging process for Laravel applications - ensures root cause investigation before attempting fixes. Use for any Laravel issue (test failures, bugs, unexpected behavior, performance problems).
Use when building or debugging WordPress Interactivity API features (data-wp-* directives, @wordpress/interactivity store/state/actions, block viewScriptModule integration, wp_interactivity_*()) including performance, hydration, and directive behavior.
Type-safe ORM for Cloudflare D1 databases using Drizzle. Use when: building D1 database schemas, writing type-safe SQL queries, managing migrations with Drizzle Kit, defining table relations, implementing prepared statements, using D1 batch API, or encountering D1_ERROR, transaction errors, foreign key constraint failures, or schema inference issues. Keywords: drizzle orm, drizzle d1, type-safe sql, drizzle schema, drizzle migrations, drizzle kit, orm cloudflare, d1 orm, drizzle typescript, drizzle relations, drizzle transactions, drizzle query builder, schema definition, prepared statements, drizzle batch, migration management, relational queries, drizzle joins, D1_ERROR, BEGIN TRANSACTION d1, foreign key constraint, migration failed, schema not found, d1 binding error, schema design, database indexes, soft deletes, uuid primary keys, enum constraints, performance optimization, naming conventions, schema testing
Google Agent Development Kit (ADK) for Python. Capabilities: AI agent building, multi-agent systems, workflow agents (sequential/parallel/loop), tool integration (Google Search, Code Execution), Vertex AI deployment, agent evaluation, human-in-the-loop flows. Actions: build, create, deploy, evaluate, orchestrate AI agents. Keywords: Google ADK, Agent Development Kit, AI agent, multi-agent system, LlmAgent, SequentialAgent, ParallelAgent, LoopAgent, tool integration, Google Search, Code Execution, Vertex AI, Cloud Run, agent evaluation, human-in-the-loop, agent orchestration, workflow agent, hierarchical coordination. Use when: building AI agents, creating multi-agent systems, implementing workflow pipelines, integrating LLM agents with tools, deploying to Vertex AI, evaluating agent performance, implementing approval flows.
Analyses and optimises performance across frontend, backend and database interactions. Identifies bottlenecks and implements solutions to enhance speed and efficiency.
Optimize Docker images and containers for size, build speed, and runtime performance
Tests in real browsers. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data via Chrome DevTools MCP.
Use when deploying or managing Kubernetes workloads requiring cluster configuration, security hardening, or troubleshooting. Invoke for Helm charts, RBAC policies, NetworkPolicies, storage configuration, performance optimization.
Build modern full-stack web applications with Next.js (App Router, Server Components, RSC, PPR, SSR, SSG, ISR), Turborepo (monorepo management, task pipelines, remote caching, parallel execution), and RemixIcon (3100+ SVG icons in outlined/filled styles). Use when creating React applications, implementing server-side rendering, setting up monorepos with multiple packages, optimizing build performance and caching strategies, adding icon libraries, managing shared dependencies, or working with TypeScript full-stack projects.
Build production-ready monitoring, logging, and tracing systems. Implements comprehensive observability strategies, SLI/SLO management, and incident response workflows. Use PROACTIVELY for monitoring infrastructure, performance optimization, or production reliability.
Complete unit testing skill for TypeScript/NestJS projects using Jest, @golevelup/ts-jest, and in-memory databases. ALWAYS use this skill when user needs to: **SETUP** - Initialize or configure unit testing: - Set up Jest for a new project - Configure test infrastructure (jest.config.ts) - Install testing dependencies (@nestjs/testing, @golevelup/ts-jest) - Create mock helpers or test utilities - Set up coverage configuration **WRITE** - Create or add unit tests: - Write, create, add, or generate unit tests - Test a service, usecase, controller, guard, interceptor, pipe, or filter - Add tests for new code or features - Improve test coverage or add missing tests - Mock dependencies or set up test fixtures - Working on any file ending in .spec.ts **REVIEW** - Audit or evaluate unit tests: - Review existing tests for quality - Check test coverage and gaps - Audit testing patterns and conventions - Evaluate assertion quality **RUN** - Execute or analyze test results: - Run unit tests - Analyze test results or coverage reports - Understand test failures or successes - Check which tests are passing/failing **DEBUG** - Fix failing or broken tests: - Fix failing unit tests - Debug test errors or exceptions - Resolve mock issues or setup problems - Troubleshoot test timeouts or flaky tests - Diagnose "undefined" or unexpected results **OPTIMIZE** - Improve test performance and maintainability: - Speed up slow tests - Fix open handles preventing clean exit - Improve test organization - Reduce test execution time - Clean up test code Keywords: unit test, spec, jest, typescript, nestjs, mock, DeepMocked, createMock, AAA, test coverage, TDD, .spec.ts, testing, write test, add test, create test, fix test, debug test, run test, review test, optimize test, test setup, jest config
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.