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Found 29 Skills
Azure AI Evaluation SDK for Python. Use for evaluating generative AI applications with quality, safety, agent, and custom evaluators. Triggers: "azure-ai-evaluation", "evaluators", "GroundednessEvaluator", "evaluate", "AI quality metrics", "RedTeam", "agent evaluation".
Create AI evaluation plans with benchmarks, rubrics, and error analysis workflows.
Use when auditing Claude skills and commands for quality. Supports Quick Scan (changed skills only) and Full Stocktake modes with sequential subagent batch evaluation.
Test your AI agent with simulation-based scenarios. Covers writing scenario test code (Scenario SDK), creating platform scenarios via the `langwatch` CLI, and red teaming for security vulnerabilities. Auto-detects whether to use code or platform approach based on context.
Use when validating golden dataset quality. Runs schema checks, duplicate detection, and coverage analysis to ensure dataset integrity for AI evaluation.
Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles
INVOKE THIS SKILL when creating, managing, or querying Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI.
LLM and AI testing patterns — mock responses, evaluation with DeepEval/RAGAS, structured output validation, and agentic test patterns (generator, healer, planner). Use when testing AI features, validating LLM outputs, or building evaluation pipelines.
MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.
Help users build effective AI applications. Use when someone is building with LLMs, writing prompts, designing AI features, implementing RAG, creating agents, running evals, or trying to improve AI output quality.
Evaluate solutions through multi-round debate between independent judges until consensus
Run Microsoft's eval-recipes benchmarks to validate amplihack improvements against baseline agents. Auto-activates when testing improvements, running evals, or benchmarking changes.