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Found 1,226 Skills
High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
Python 3.13+ development specialist covering FastAPI, Django, async patterns, data science, testing with pytest, and modern Python features. Use when developing Python APIs, web applications, data pipelines, or writing tests.
Expert Python developer specializing in Python 3.11+ features, type annotations, and async programming patterns. This agent excels at building high-performance applications with FastAPI, leveraging modern Python syntax, and implementing comprehensive type safety across complex systems.
Control Notion via Python SDK. TRIGGERS - Notion API, create page, query database, add blocks.
Use when a user wants to add MCPCat analytics to their Python MCP server, install the mcpcat Python package, or integrate mcpcat.track() into an existing Python MCP server codebase.
Guide for implementing gRPC-based key-value store services in Python. This skill should be used when building gRPC servers with protobuf definitions, implementing KV store operations (Get, Set, Delete), or troubleshooting gRPC service connectivity. Applicable to tasks involving grpcio, protobuf code generation, and background server processes.
Guide for setting up local PyPI servers to host and serve Python packages. This skill should be used when tasks involve creating a local PyPI repository, serving Python packages over HTTP, building distributable Python packages, or testing pip installations from a custom index URL.
Generate, edit, and compose images using Gemini Nano Banana models via portable Python scripts. Handles authentication via API Key or Vertex AI environment variables. Available parameters: prompt, model, aspect-ratio, safety-filter-level. Always confirm parameters with the user or explicitly state defaults before running.
Design ETL workflows with data validation using tools like Pandas, Dask, or PySpark. Use when building robust data processing systems in Python.
Use when designing data ownership, validation boundaries, consistency models, or configuration strategy in Python. Also use when encountering unclear ownership across modules, shared mutable state leaking between layers, validation gaps at ingress, cross-module transactional coupling, or config drift between environments.
Use when building or reviewing service, job, or CLI runtime behavior in Python — designing startup validation, shutdown sequences, observability, and structured logging. Also use when startup crashes from late config, shutdown leaves orphaned processes, terminal states are implicit, or logs lack structure.
Use when designing error handling, retry policies, timeout behavior, or failure classification in Python. Also use when code swallows exceptions, loses error context across boundaries, has unbounded retries, silent failures, or lacks idempotency guarantees on retried writes.