Loading...
Loading...
Found 2,042 Skills
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.
Use when CONFIGURING an existing SDK - NOT for initial generation. Covers gen.yaml configuration for all languages: TypeScript, Python, Go, Java, C#, PHP, Ruby. Also covers runtime overrides (retries, timeouts, server selection) in application code. Triggers on "configure SDK", "gen.yaml", "SDK options", "SDK config", "SDK configuration", "runtime override", "SDK client config", "override timeout", "per-call config". For NEW SDK generation, use start-new-sdk-project instead.
Use when generating PDFs from markdown with Pandoc - covers differences from Python-Markdown, blank line rules, fix scripts for labels/anchors/metadata, and visual testing workflow
Perform language and framework specific security best-practice reviews and suggest improvements. Trigger only when the user explicitly requests security best practices guidance, a security review/report, or secure-by-default coding help. Trigger only for supported languages (python, javascript/typescript, go). Do not trigger for general code review, debugging, or non-security tasks.
System architecture guidance for Python/React full-stack projects. Use during the design phase when making architectural decisions — component boundaries, service layer design, data flow patterns, database schema planning, and technology trade-off analysis. Covers FastAPI layer architecture (Routes/Services/Repositories/Models), React component hierarchy, state management, and cross-cutting concerns (auth, errors, logging). Produces architecture documents and ADRs. Does NOT cover implementation (use python-backend-expert or react-frontend-expert) or API contract design (use api-design-patterns).
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.
Domain-Driven Design system for software development. Use when designing new systems with DDD principles, refactoring existing codebases toward DDD, generating code scaffolding (entities, aggregates, repositories, domain events), facilitating Event Storming sessions, creating bounded context maps, or performing code reviews with a DDD lens. Covers both strategic design (bounded contexts, subdomains, context maps, ubiquitous language) and tactical design (entities, value objects, aggregates, domain services, repositories). Supports all major architecture patterns (Hexagonal/Ports & Adapters, CQRS, Event Sourcing, Clean Architecture) with language-agnostic guidance and concrete examples in Python and TypeScript.
Manage and troubleshoot PATH configuration in zsh. Use when adding tools to PATH (bun, nvm, Python venv, cargo, go), diagnosing "command not found" errors, validating PATH entries, or organizing shell configuration in .zshrc and .zshrc.local files.
Expert guidance for SQLModel - the Python library combining SQLAlchemy and Pydantic for database models. Use when (1) creating database models that work as both SQLAlchemy ORM and Pydantic schemas, (2) building FastAPI apps with database integration, (3) defining model relationships (one-to-many, many-to-many), (4) performing CRUD operations with type safety, (5) setting up async database sessions, (6) integrating with Alembic migrations, (7) handling model inheritance and mixins, or (8) converting between database models and API schemas.
Create, edit, and build Observable Notebooks using Notebook Kit. Use when working with .html notebook files, generating static sites from notebooks, querying databases from notebooks, or using data loaders (Node.js/Python/R) in notebooks. Covers notebook file format, cell types, CLI commands, database connectors, and JavaScript API.
Grey Haven's comprehensive testing strategy - Vitest unit/integration/e2e for TypeScript, pytest markers for Python, >80% coverage requirement, fixture patterns, and Doppler for test environments. Use when writing tests, setting up test infrastructure, running tests, debugging test failures, improving coverage, configuring CI/CD, or when user mentions 'test', 'testing', 'pytest', 'vitest', 'coverage', 'TDD', 'test-driven development', 'unit test', 'integration test', 'e2e', 'end-to-end', 'test fixtures', 'mocking', 'test setup', 'CI testing'.
JSON processing, parsing, and manipulation. STRONGLY PREFERRED for all JSON formatting, filtering, transformations, and analysis. Use instead of Python/Node.js scripts for JSON operations.