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mlflow-agent

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Mis à jour31 mars 2026 à 16:28

Master dispatcher for all MLflow workflows. Use this skill when the user wants to do anything with MLflow — tracing, evaluating, debugging, or improving an agent. Routes to the right MLflow sub-skill automatically. Triggers on: "use mlflow", "help with mlflow", "mlflow agent", "add mlflow to my project", "trace my agent", "evaluate my agent", or any MLflow task without a specific skill in mind.

Installation

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Source

mlflow

mlflow/skills

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Développeurs de logicielsProfessions informatiques et mathématiques·SOC 15-1252

SKILL.md

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name	mlflow-agent
description	Master dispatcher for all MLflow workflows. Use this skill when the user wants to do anything with MLflow — tracing, evaluating, debugging, or improving an agent. Routes to the right MLflow sub-skill automatically. Triggers on: "use mlflow", "help with mlflow", "mlflow agent", "add mlflow to my project", "trace my agent", "evaluate my agent", or any MLflow task without a specific skill in mind.
disable-model-invocation	true

MLflow Agent

Master dispatcher for MLflow workflows. Reads user intent and invokes the right sub-skill.

Trigger

Use when the user wants to do anything with MLflow but hasn't specified which skill to use.

Process

Read the user's request and identify intent
Map to the appropriate skill:
- Tracing / instrumentation → instrumenting-with-mlflow-tracing
- Evaluation / scoring → agent-evaluation
- Debug a trace → analyze-mlflow-trace
- Debug a chat session → analyze-mlflow-chat-session
- Search traces → retrieving-mlflow-traces
- Metrics / costs → querying-mlflow-metrics
- Getting started → mlflow-onboarding
- Docs / API questions → searching-mlflow-docs
If intent is unclear, ask ONE clarifying question, then dispatch
Invoke the matched skill using the Skill tool

Key Rules

Never do the work yourself — always dispatch to the appropriate sub-skill
One clarifying question maximum before dispatching
If the user says "evaluate AND trace", dispatch tracing first, then evaluation
If the user's request spans multiple skills, handle them in logical order (setup → instrument → evaluate)

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Drives 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, or policy. Grounds the diagnosis in MLflow traces, codifies the desired behavior as a regression test suite (`mlflow.genai.evaluate` assertions in `@mlflow.test` pytest tests), and iterates the agent — not the test — until green, resisting quick system-prompt patches when the real fix is upstream (missing tool, retrieval source, or capability). Use whenever the user wants to fix or change how an agent behaves — e.g. "fix this issue in my agent", "this answer is wrong", "the agent is hallucinating", "improve my agent based on this trace", "make the agent do X instead of Y", "I want the agent to lead with/prioritize/recommend X", "new business rule: the agent should X", "always/never do X", "change the agent's default behavior" — or shares a trace they want addressed.

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Use 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 the agent gives wrong/incomplete responses. Evaluates agents systematically using MLflow evaluation with datasets, scorers, and tracing. IMPORTANT - Always also load the instrumenting-with-mlflow-tracing skill before starting any work. Covers end-to-end evaluation workflow or individual components (tracing setup, dataset creation, scorer definition, evaluation execution).

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Instruments Python and TypeScript code with MLflow Tracing for observability. Must be loaded when setting up tracing as part of any workflow including agent evaluation. Triggers on adding tracing, instrumenting agents/LLM apps, getting started with MLflow tracing, tracing specific frameworks (LangGraph, LangChain, OpenAI, Gemini, DSPy, CrewAI, AutoGen), or when another skill references tracing setup. Examples - "How do I add tracing?", "Instrument my agent", "Trace my LangChain app", "Set up tracing for evaluation"

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Onboards 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. If an experiment ID is available, it should be supplied as input to help determine the use case. Use when the user asks to get started with MLflow, set up tracking, add observability, or integrate MLflow into their project. Triggers on "get started with MLflow", "set up MLflow", "onboard to MLflow", "add MLflow to my project", "how do I use MLflow".

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analyzing-mlflow-session

mlflow/skills

Analyzes an MLflow session — a sequence of traces from a multi-turn chat conversation or interaction. Use when the user asks to debug a chat conversation, review session or chat history, find where a multi-turn chat went wrong, or analyze patterns across turns. Triggers on "analyze this session", "what happened in this conversation", "debug session", "review chat history", "where did this chat go wrong", "session traces", "analyze chat", "debug this chat".

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analyzing-mlflow-trace

mlflow/skills

Analyzes a single MLflow trace to answer a user query about it. Use when the user provides a trace ID and asks to debug, investigate, find issues, root-cause errors, understand behavior, or analyze quality. Triggers on "analyze this trace", "what went wrong with this trace", "debug trace", "investigate trace", "why did this trace fail", "root cause this trace".

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name	mlflow-agent
description	Master dispatcher for all MLflow workflows. Use this skill when the user wants to do anything with MLflow — tracing, evaluating, debugging, or improving an agent. Routes to the right MLflow sub-skill automatically. Triggers on: "use mlflow", "help with mlflow", "mlflow agent", "add mlflow to my project", "trace my agent", "evaluate my agent", or any MLflow task without a specific skill in mind.
disable-model-invocation	true

MLflow Agent

Master dispatcher for MLflow workflows. Reads user intent and invokes the right sub-skill.

Trigger

Use when the user wants to do anything with MLflow but hasn't specified which skill to use.

Process

Read the user's request and identify intent
Map to the appropriate skill:
- Tracing / instrumentation → instrumenting-with-mlflow-tracing
- Evaluation / scoring → agent-evaluation
- Debug a trace → analyze-mlflow-trace
- Debug a chat session → analyze-mlflow-chat-session
- Search traces → retrieving-mlflow-traces
- Metrics / costs → querying-mlflow-metrics
- Getting started → mlflow-onboarding
- Docs / API questions → searching-mlflow-docs
If intent is unclear, ask ONE clarifying question, then dispatch
Invoke the matched skill using the Skill tool

Key Rules

Never do the work yourself — always dispatch to the appropriate sub-skill
One clarifying question maximum before dispatching
If the user says "evaluate AND trace", dispatch tracing first, then evaluation
If the user's request spans multiple skills, handle them in logical order (setup → instrument → evaluate)