Skill UI

Automates creation of A/B test configurations for ML deployment, guiding users through best practices, code samples, and validation steps so production models are served with monitoring and rollout control.

Generates production-ready Claude-powered agents with Google’s ADK, bundling React-style loops, tool-augmented capabilities, multi-agent orchestration, workflow automation, and test scaffolding.

A comprehensive framework for rigorously testing and benchmarking LLM agents. It moves beyond simple pass/fail checks to assess complex behaviors, reliability metrics, and capability consistency across multiple runs. Ideal for production monitoring and identifying subtle failure modes in advanced AI agents.

A systematic workflow designed to significantly improve the reliability and performance of existing AI agents. It covers detailed performance analysis, including gathering metrics (e.g., task success rate, latency), classifying failure modes, and advanced prompt engineering techniques (like Chain-of-Thought and Constitutional AI). The process culminates in structured A/B testing and controlled deployment, ensuring measurable, robust improvements.

Guide for automating Agentql tasks through Composio’s Rube MCP toolkit, covering prerequisites, setup, discovery, execution, and error avoidance so you can safely run workflows using the latest schemas.

Use Composio’s Botpress toolkit through Rube MCP to orchestrate Botpress workflows: always search tools for the latest schemas, confirm the Botpress connection, then execute discovered operations via RUBE_MULTI_EXECUTE_TOOL with schema-compliant inputs.

Automates Dreamstudio tasks through Rube MCP and Composio by discovering the latest tool schemas, validating active connections, and driving execution with RUBE_SEARCH_TOOLS, RUBE_MANAGE_CONNECTIONS, and RUBE_MULTI_EXECUTE_TOOL workflows.

Provides comprehensive guidance on building robust evaluation frameworks for complex agent systems. It addresses the inherent challenges of non-determinism and multiple valid paths, enabling systematic performance testing, validating context engineering choices, and measuring continuous improvements across multiple quality dimensions (e.g., factual accuracy, tool efficiency).

A comprehensive guide demonstrating how to generate your first AI image using the Ideogram API. It provides runnable examples in cURL, TypeScript, and Python, covering both legacy and V3 endpoints. Users will learn essential API parameters, such as prompt structuring, style types, and aspect ratios, making it ideal for initial integration testing and setup.

This advanced skill is a multi-agent research framework designed for executing deep, extensive research in parallel. It orchestrates a team of 10 specialized agents to gather, synthesize, and analyze information simultaneously. It is ideal for complex topics that require rapid, broad, and battle-tested investigation across multiple dimensions, significantly accelerating the knowledge discovery process.

This tool allows developers to integrate advanced AI video generation (using Kling AI) directly into continuous integration and continuous deployment pipelines (e.g., GitHub Actions, GitLab CI). Automate the creation of marketing videos, product demos, or regression video testing whenever code changes or a release occurs, ensuring consistent and timely video asset updates.

This comprehensive guide helps developers safely migrate between different versions of the Kling AI video generation model (e.g., v1.x to v2.x). It details breaking changes, parameter differences, feature availability matrices (including native audio and camera control), and provides code examples for parallel A/B testing and robust rollback strategies, ensuring seamless API adoption.