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Found 1,669 Skills
Design ETL/ELT pipelines with proper orchestration, error handling, and monitoring. Use when building data pipelines, designing data workflows, or implementing data transformations.
Retrieval-Augmented Generation (RAG) system design patterns, chunking strategies, embedding models, retrieval techniques, and context assembly. Use when designing RAG pipelines, improving retrieval quality, or building knowledge-grounded LLM applications.
Build AI scientist systems using ToolUniverse Python SDK for scientific research. Use when users need to access 1000++ scientific tools through Python code, create scientific workflows, perform drug discovery, protein analysis, genomics analysis, literature research, or any computational biology task. Triggers include requests to use scientific tools programmatically, build research pipelines, analyze biological data, search literature, predict drug properties, or create AI-powered scientific workflows.
Implement applications using Google Cloud Platform (GCP) services. Use when building on GCP infrastructure, selecting compute/storage/database services, designing data analytics pipelines, implementing ML workflows, or architecting cloud-native applications with BigQuery, Cloud Run, GKE, Vertex AI, and other GCP services.
NestJS 11+ best practices for enterprise Node.js applications with TypeScript. Use when writing, reviewing, or refactoring NestJS controllers, services, modules, or APIs. Triggers on: NestJS modules, controllers, providers, dependency injection, @Injectable, @Controller, @Module, middleware, guards, interceptors, pipes, exception filters, ValidationPipe, class-validator, class-transformer, DTOs, JWT authentication, Passport strategies, @nestjs/passport, TypeORM entities, Prisma client, Drizzle ORM, repository pattern, circular dependencies, forwardRef, @nestjs/swagger, OpenAPI decorators, GraphQL resolvers, @nestjs/graphql, microservices, TCP transport, Redis transport, NATS, Kafka, NestJS 11 breaking changes, Express v5 migration, custom decorators, ConfigService, @nestjs/config, health checks, or NestJS testing patterns.
Exactly-once processing semantics with distributed coordination for file-based data pipelines. Atomic file claiming, status tracking, and automatic retry with in-memory fallback.
Use when the user wants to create, generate, or set up a GitHub Actions workflow. Handles CI/CD pipelines, testing, deployment, linting, security scanning, release automation, Docker builds, scheduled tasks, and any custom workflow for any language or framework.
Documentation pipeline automation and docs-as-code workflows
Expert-level CI/CD with GitHub Actions, Jenkins, deployment pipelines, and automation
Unified test-fix pipeline combining test generation (session, context, analysis, task gen) with iterative test-cycle execution (adaptive strategy, progressive testing, CLI fallback). Triggers on "workflow:test-fix-gen", "workflow:test-cycle-execute", "test fix workflow".
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.
Data Catalog Updater - Auto-activating skill for Data Pipelines. Triggers on: data catalog updater, data catalog updater Part of the Data Pipelines skill category.