Skill UI

This guide serves as a comprehensive reference for writing idiomatic, efficient, and modern Python code. It covers best practices for comprehensions, context managers, error handling (EAFP), data class usage, type hinting, and avoiding common anti-patterns, helping developers write cleaner, more robust, and truly 'Pythonic' code.

This skill generates a highly customized code review checklist for any pull request. Instead of using a generic template, it adapts the review depth and specific checks based on the target language (e.g., Python, TypeScript), the nature of the change (e.g., bug fix, refactor), and the associated risk level (low, medium, high, critical). It ensures comprehensive coverage, guiding reviewers to focus on critical areas like security, correctness, and architectural integrity.

Execute arbitrary PySpark or pure Python code directly on Fabric Spark compute using the Livy API. This method creates ephemeral sessions without generating persistent notebook artifacts. It provides full read/write access to lakehouse Delta tables, making it ideal for agent-driven ETL pipelines, quick data transformations, or analyses where session cleanup is critical. Users must manage the session lifecycle (create, run, and delete).

This skill uses Python and boto3 to analyze AWS CloudTrail logs. It establishes statistical baselines of normal API activity and detects various anomalies, such as unusual event sources, first-time API usage, geographic IP deviations, and high-frequency calls. This is critical for identifying credential compromise, privilege escalation, and unauthorized resource access in a security incident.

This guide details how to deploy OpenCTI, a powerful, open-source threat intelligence platform, and utilize the pycti Python client. It teaches analysts how to model complex cyber threat intelligence, including threat actors, campaigns, and indicators, into a structured STIX 2.1 knowledge graph. Use this for centralized threat-intel correlation, building adversary graphs, and generating STIX bundles for detection engineering.

This skill generates complete, production-grade client SDKs and API wrapper libraries for any REST API, regardless of the language (Python, TypeScript, etc.). It handles boilerplate code like authentication, retry logic (exponential backoff), data modeling, and structured resource management, saving significant development time. Use it whenever you need to consume a REST API via code.

This skill generates robust, production-grade Appium automation scripts for both Android and iOS. It supports multiple languages (Java, Python, JavaScript) and guides users through best practices for cross-platform testing, including proper locator strategies, explicit waiting, and complex gestures. Ideal for automating mobile QA processes on local emulators or cloud real device farms.

This skill guides developers on implementing Conversational Language Understanding (CLU) using the official azure-ai-language-conversations Python SDK. It demonstrates how to analyze conversation intent and entities, build advanced NLP features, and integrate language understanding into applications using ConversationAnalysisClient. Best practices emphasize using DefaultAzureCredential and context managers for robust, production-ready code.

Use this comprehensive skill for interacting with the Gemini API. It supports a wide range of advanced AI tasks, including text generation, multi-turn chat, multimodal understanding, image/video creation, function calling, and structured output. Provides modern SDKs for robust application development in Python and JavaScript/TypeScript.

This skill generates comprehensive, production-grade unit and integration tests using the Python pytest framework. It covers advanced patterns such as fixtures (with scope and teardown), parametrization, custom markers, mocking external dependencies, and best practices for writing robust and maintainable test suites. Use this when implementing detailed test coverage for Python code.

A comprehensive skill for generating production-grade, robust automation scripts and end-to-end tests using Selenium WebDriver. Supports multiple languages (Java, Python, JS, C#, etc.) and execution environments, including local machines and TestMu AI cloud for cross-browser compatibility. It guides users on best practices like explicit waits, Page Object Model (POM), and stable locator strategies, acting as a senior QA automation architect.

Run comprehensive, read-only exposure checks against your local machine or project directories in response to security advisories and CVEs. This tool systematically checks various ecosystems (Node.js, Python, Homebrew, etc.) to generate a structured, non-invasive audit report, helping users understand their actual risk exposure without making any system changes.