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Found 1,567 Skills
Initializes Python projects, manages dependencies, pins Python versions, and runs scripts with uv. Use when adding/removing packages, syncing environments, running tools with uvx, or building distributions.
Comprehensive guide for production-ready Python backend development and software architecture at scale. Use when designing APIs, building backend services, creating microservices, structuring Python projects, implementing database patterns, writing async code, or any Python backend/server-side development task. Covers Clean Architecture, Domain-Driven Design, Event-Driven Architecture, FastAPI/Django patterns, database design, caching strategies, observability, security, testing strategies, and deployment patterns for high-scale production systems.
Build FastAPI applications using Clean Architecture principles with proper layer separation (Domain, Infrastructure, API), dependency injection, repository pattern, and comprehensive testing. Use this skill when designing or implementing Python backend services that require maintainability, testability, and scalability.
Professional Pydantic v2.12 development for data validation, serialization, and type-safe models. Use when working with Pydantic for (1) creating or modifying BaseModel classes, (2) implementing validators and serializers, (3) configuring model behavior, (4) handling JSON schema generation, (5) working with settings management, (6) debugging validation errors, (7) integrating with ORMs or APIs, or (8) any production-grade Python data validation tasks. Includes complete API reference, concept guides, examples, and migration patterns.
Guide for modernizing legacy Python 2 scientific computing code to Python 3 with modern libraries. This skill should be used when migrating scientific scripts involving data processing, numerical computation, or analysis from Python 2 to Python 3, or when updating deprecated scientific computing patterns to modern equivalents (pandas, numpy, pathlib).
Create production-quality data visualizations including charts, dashboards, and infographics. Use when the user asks to visualize data, create charts, build dashboards, make infographics, plot statistics, or transform datasets into visual representations. Supports React/Recharts artifacts, static images (PNG/PDF via Python), and interactive HTML. Triggers include "visualize this data", "create a chart", "build a dashboard", "make a graph", "plot this", "infographic", or any request to represent data visually.
Python best practices for writing production-grade code. This skill should be used when writing, reviewing, or refactoring Python code. Triggers on tasks involving Python development, error handling patterns, dictionary operations, and code quality improvements.
Code refactoring expert for improving code quality, readability, maintainability, and performance. Specializes in Java and Python refactoring patterns, eliminating code smells, and applying clean code principles. Use when refactoring code, improving existing implementations, or cleaning up technical debt.
Execute Python code locally with marketplace API access for 90%+ token savings on bulk operations. Activates when user requests bulk operations (10+ files), complex multi-step workflows, iterative processing, or mentions efficiency/performance.
Generate, edit, or transform images with Gemini Nano Banana using bundled Python scripts (Flash or Pro) including aspect ratio, resolution, image-to-image edits, logo overlays, and reference images. Use when users request image generation, image edits, image-to-image transformations, logo placement, or specific aspect ratios or resolutions.
Python development guidance with code quality standards, error handling, testing practices, and environment management. Use when writing, reviewing, or modifying Python code (.py files) or Jupyter notebooks (.ipynb files).
Deep Python code review of changed files using git diff analysis. Focuses on production quality, security vulnerabilities, performance bottlenecks, architectural issues, and subtle bugs in code changes. Analyzes correctness, efficiency, scalability, and production readiness of modifications. Use for pull request reviews, commit reviews, security audits of changes, and pre-deployment validation. Supports Django, Flask, FastAPI, pandas, and ML frameworks.