Skill UI

This skill acts as an expert AI-powered code review specialist combining automated static analysis and intelligent pattern recognition. It leverages tools like GitHub Copilot, SonarQube, and CodeQL to identify bugs, vulnerabilities, and performance issues across 30+ languages. It supports CI/CD integration, security vulnerability analysis, architectural coherence checks, and actionable recommendations for maintainability and testability within modern DevOps workflows.

Expert patterns for integrating Plaid API, covering Link token flows, transaction synchronization, identity verification, ACH authentication, balance checks, webhook management, and compliance best practices for financial technology applications.

General-purpose browser automation skill using Playwright. Auto-detects local dev servers, writes custom test scripts to /tmp, and executes them with a visible browser. Supports responsive testing, login flows, form submissions, and link checking.

Automates Postmark transactional email operations through Rube MCP’s Composio toolkit, covering templated batch sends, template management, delivery stats, bounce handling, and server configuration with guided tool sequences and checks.

This skill covers critical PowerShell Windows patterns including operator syntax rules, null checks, error handling, and file path management. It emphasizes avoiding common pitfalls like missing parentheses, Unicode characters, and type mismatches to ensure robust script execution.

Create editable, production-ready PPTX decks with narrative planning, explicit layout specs, asset guidance, and quality checks.

This skill helps plan engaging, structured puzzle-based activities for various settings like classrooms, parties, and corporate team-building events. Users provide the event description, target audience, and overall goal, and the planner generates a comprehensive activity schedule. It includes a minute-by-minute timeline, necessary materials checklist, and generates pre-configured generator links for diverse puzzles (Word Search, Crossword, Sudoku, etc.), ensuring content is theme-appropriate and ready for one-click use.

PydanticAI is a robust Python framework that integrates Pydantic's type-checking and validation capabilities into Large Language Models (LLMs). It allows developers to build production-ready AI agents that guarantee structured, validated outputs, moving beyond simple raw strings. Key features include supported tool calling, dependency injection for testability, and multi-model compatibility across major providers like OpenAI, Anthropic, and Gemini. Use this when reliability and correct data structure are paramount.

Guides an AI agent to give non-judgmental, evidence-informed quit-smoking support: quit planning, craving check-ins, slip debriefs, optional consent-based logging, and escalation to clinicians or quitlines. Not medical care.

Monitor and evaluate Retrieval-Augmented Generation (RAG) systems using measurable metrics. This skill covers retrieval quality checks like Recall@k, hallucination detection, and groundedness scoring. It enables continuous regression testing and production monitoring for AI pipelines using Python libraries like RAGAS and OpenTelemetry.

This skill manages the entire lifecycle of long-running deep learning training and inference jobs deployed on remote, ephemeral computing environments (e.g., AutoDL, RunPod, bare SSH, Slurm, K8s). It provides critical capabilities like resilient checkpointing, managing spot instance preemption, debugging resource failures (OOM, NaN), and ensuring safe billing teardown, making it essential for professional ML Ops on rented resources.

This skill audits and repairs repository hygiene across artifacts, dependencies, CI, docs, Git state, and code-quality signals. It is used for repository maintenance, cleanup, health checks, or pre-release hardening. It ensures repository health through authorized repairs and protected workflows, validating changes before publishing via pull requests to maintain security and stability.