Skill UI

Automates Langbase operations through the Rube MCP-powered Composio toolkit, guiding connectors, tool discovery, schema validation, and multi-step execution flows while emphasizing session reuse and pagination awareness.

Integrate Langfuse into CI/CD pipelines (e.g., GitHub Actions) to automate the testing and validation of Large Language Models (LLMs). This guide covers prompt regression testing, tracing validation, and setting up experiment-driven quality gates, ensuring reliable and high-performing AI applications throughout the development lifecycle.

Automate the validation and testing of prompts and AI responses within your CI/CD pipeline using Mistral AI. This integration provides robust prompt regression testing, model quality checks, automatic cost estimation for API usage, and deployment gates, ensuring the stability and reliability of your AI features upon every pull request.

This skill provides comprehensive guidance for building production-grade MLOps pipelines. It covers the entire machine learning lifecycle, from initial data ingestion and rigorous feature engineering to model training, advanced validation (A/B testing, performance drift), and robust deployment strategies (Canary, Blue-green). It enables the design and orchestration of scalable, reproducible, and observable ML systems using tools like Airflow and Kubeflow.

Automates guidance for configuring mlflow experiment tracking, covering best practices, code generation, and validation to support ML training workflows.

Automated guidance for managing model checkpoints in ML training workflows, offering best practices, sample code, and validation to keep experiments stable and reproducible.

Automates guidance on identifying and responding to model drift within ML deployment workflows, offering best practices, production-ready code, and validation support so you can keep inference systems stable.

Automates guidance for evaluating machine learning models, covering best practices, sample code, and validation patterns triggered by mentions of model evaluation metrics.

Automated guidance for model explainability tooling within ML training workflows, giving step-by-step best practices, production-ready code, and validation for experiments whenever users mention explainability tool needs.

Automates guidance for model export helper tasks across ML deployment, delivering step-by-step best practices, production-ready code/configuration, and validation against common standards.

Guides ML deployment workflows by surfacing best practices, templated code, and validation steps whenever model pruning helper patterns are mentioned.

This guide outlines the comprehensive reference architecture for building advanced, AI-powered search and research applications, modeled after Perplexity. It covers three core tiers: a searchable service with model routing and caching, a citation pipeline for validation and rendering, and an orchestrator for multi-query research synthesis. Use this when designing complex, production-grade, search-augmented LLM integrations or reviewing advanced system patterns.