MLflow Skills

Skills

13 in the Claude listing

• Agent evaluationUse this when you need to EVALUATE OR IMPROVE or OPTIMIZE an existing LLM agent's output quality - including improving tool selection accuracy, answer quality, reducing costs, or fixing issues where t
• Analyzing mlflow sessionAnalyzes an MLflow session — a sequence of traces from a multi-turn chat conversation or interaction.
• Analyzing mlflow traceAnalyzes a single MLflow trace to answer a user query about it.
• Build a scorerHelp the user go from zero to a shipped MLflow evaluation prototype by understanding their app, generalizing a small set of atomic quality criteria, and implementing each criterion with the cheapest r
• Debug agentRoot-causes AI agent failures from MLflow traces without requiring a code change, regression suite, scorer, or evaluation run.
• Fix agent issueDrives a disciplined explore → plan → implement → verify loop for changing an AI agent's behavior with confidence — whether fixing a reported failure or introducing a new requirement, business rule, o
• Instrumenting with mlflow tracingInstruments Python and TypeScript code with MLflow Tracing for observability.
• Mlflow agentMaster dispatcher for all MLflow workflows.
• Mlflow onboardingOnboards users to MLflow by determining their use case (GenAI agents/apps or traditional ML/deep learning) and guiding them through relevant quickstart tutorials and initial integration.
• Querying mlflow metricsFetches aggregated trace metrics (token usage, latency, trace counts, quality evaluations) from MLflow tracking servers.
• Retrieving mlflow tracesRetrieves MLflow traces using CLI or Python API.
• Sagemaker mlflowConnect to SageMaker Managed MLflow (mlflow-app or mlflow-tracking-server ARN) as an MLflow backend, then hand off to the other MLflow skills.
• Searching mlflow docsSearches and retrieves MLflow documentation from the official docs site.