dqx-code-standards
Quick reference for DQX Python code standards
Skills correspondant au nom, à la description ou à l’auteur GitHub.
Quick reference for DQX Python code standards
Apply Sun Lab Python coding conventions when writing, reviewing, or refactoring code. Covers docstrings, type annotations, naming conventions, error handling, Numba functions, and formatting standards.
Automatically applies when writing Python code to enforce comprehensive type hints. Ensures mypy compatibility, proper generic types, and type safety best practices.
Review ADRs to check they follow testing principles. Use when reviewing ADRs or architecture decisions.
Create Hathor blockchain blueprints (nano contracts) - Python 3.11 smart contracts. Use when building, debugging, or modifying Hathor nano contracts, or when user mentions blueprints, nano contracts, or Hathor...
Create Lunar policy plugins that enforce engineering standards. Use when building policies (Python scripts) that evaluate Component JSON data and produce pass/fail checks. Covers the lunar_policy SDK (Check cla...
Write ADRs for Python architecture decisions. Use when making architecture decisions or writing ADRs.
Master Temporal workflow orchestration using the Python SDK. Use PROACTIVELY for workflow design, microservice orchestration, long-running processes, saga pattern implementation, distributed transactions, or an...
A skill for using Databricks SQL (SQL Warehouse) via SDK. Supports both Python (databricks-sql-connector) and Node.js (@databricks/sql). Use when: - Connecting to SQL Warehouse from Databricks Apps to execute...
Testing patterns for Prowler SDK (Python). Trigger: When writing tests for the Prowler SDK (checks/services/providers), including provider-specific mocking rules (moto for AWS only).
Pytest testing patterns for Python. Trigger: When writing Python tests - fixtures, mocking, markers.
Build MCP servers for LLM-external service integration. Use for FastMCP (Python), MCP SDK (Node/TypeScript), tool design, API integration, resource providers.
Build AI agents with Google ADK Python (Agent Development Kit). Use for multi-agent systems, workflow agents (sequential/parallel/loop), Vertex AI deployment, tool integration, human-in-the-loop.
Modern backend patterns, API design, OWASP security checklist, testing pyramid. For Node.js, Python, Go backends.
Expert Unreal Engine 5 developer and technical artist for complete game development via agentic coding. Enables AI-driven control of Unreal Editor through MCP, Python scripting, Blueprints, and C++ for level de...
Build custom chat API backends for OpenAI ChatKit frontend using FastAPI. Covers chat endpoint implementation, SSE streaming, conversation persistence, and integration with OpenAI Agents SDK + Gemini via LiteLL...
Persistent Python REPL server that avoids reloading heavy packages (torch, transformers, nnterp, vllm). Use when running multiple Python commands with heavy imports, loading ML models, or doing iterative testin...
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or...
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.
Knowledge management for AI-assisted development. Use this skill when: - Starting a new session or resuming work - User asks about tasks, bugs, notes, or project status - User mentions: "what's next", "where di...
Knowledge management for AI-assisted development. Use this skill when: - Starting a new session or resuming work - User asks about tasks, bugs, notes, or project status - User mentions: "what's next", "where di...
Knowledge management for AI-assisted development. Use this skill when: - Starting a new session or resuming work - User asks about tasks, bugs, notes, or project status - User mentions: "what's next", "where di...
Comprehensive guide to CPU and memory profiling in Node.js and Python for identifying bottlenecks and optimizing performance
This skill should be used when the user asks to "deploy to Railway", "deploy backend", "deploy Python app", "deploy Node.js server", "railway deployment", or mentions Railway hosting workflows.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.