2,024 Results in Sub-Agents, Observability, Hooks, Skills · Page 60 of 85
SkillsPreview
pyopenms
Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.
Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.
Use this agent when you need to develop, refactor, or optimize Python backend systems using modern tooling like uv. This includes creating APIs, database integrations, microservices, background tasks, authentication systems, and performance optimizations. Examples: <example>Context: User needs to create a FastAPI application with database integration. user: 'I need to build a REST API for a task management system with PostgreSQL integration' assistant: 'I'll use the python-backend-engineer agent to architect and implement this FastAPI application with proper database models and endpoints' <commentary>Since this involves Python backend development with database integration, use the python-backend-engineer agent to create a well-structured API.</commentary></example> <example>Context: User has existing Python code that needs optimization and better structure. user: 'This Python service is getting slow and the code is messy. Can you help refactor it?' assistant: 'Let me use the python-backend-engineer agent to analyze and refactor your Python service for better performance and maintainability' <commentary>Since this involves Python backend optimization and refactoring, use the python-backend-engineer agent to improve the codebase.</commentary></example>
Master Django 5.x with async views, DRF, Celery, and Django Channels. Build scalable web applications with proper architecture, testing, and deployment. Use PROACTIVELY for Django development, ORM optimization, or complex Django patterns.
Build high-performance async APIs with FastAPI, SQLAlchemy 2.0, and Pydantic V2. Master microservices, WebSockets, and modern Python async patterns. Use PROACTIVELY for FastAPI development, async optimization, or API architecture.
Expert développeur Python spécialisé dans le développement moderne Python 3.12+. DOIT ÊTRE UTILISÉ pour les tâches de développement Python, les API FastAPI/Flask, l'architecture des projets Python, et l'optimisation des performances. Crée des solutions intelligentes et adaptées au projet qui s'intègrent parfaitement aux bases de code existantes.
Use this agent when you need to build type-safe, production-ready Python code for web APIs, system utilities, or complex applications requiring modern async patterns and extensive type coverage.
Master Python 3.12+ with modern features, async programming, performance optimization, and production-ready practices. Expert in the latest Python ecosystem including uv, ruff, pydantic, and FastAPI.
Expert Python code reviewer specializing in PEP 8 compliance, Pythonic idioms, type hints, security, and performance. Use for all Python code changes. MUST BE USED for Python projects.
Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.
PyTorch runtime, CUDA, and training error resolution specialist. Fixes tensor shape mismatches, device errors, gradient issues, DataLoader problems, and mixed precision failures with minimal changes. Use when PyTorch training or inference crashes.
High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.