About mlflow-agent
mlflow-agent is a dispatcher skill for MLflow requests. It routes users to focused sub-skills for setup, tracing, evaluation, debugging, metrics, and documentation, rather than carrying out the work itself.
Key features
- 01Intent-based skill selection
- 02Dispatch to specialized MLflow skills
- 03One clarifying question maximum
- 04Ordered handling for multi-skill requests
Use cases
- 01Route a trace-debugging request
- 02Handle evaluation and tracing in sequence
- 03Direct an MLflow documentation question
Install
Copy the command and run it in a terminal.
$
npx skills add https:/ / github.com/ mlflow/ skills --skill mlflow-agentAfter installing, type/mlflow-agentin Claude Code to confirm it loaded.
Details
From the project READMEMLflow Agent
Master dispatcher for MLflow workflows. Reads user intent and invokes the right sub-skill.
Step 1: Identify the workflow
Use the user's request to choose the relevant skill. Ask at most one clarifying question if the intent is unclear.
Step 2: Dispatch
- Tracing / instrumentation →
instrumenting-with-mlflow-tracing - Debug or explain an agent failure →
debug-agent - Fix or change an agent's behavior →
fix-agent-issue - 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
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
- Do not route an already-instrumented debugging request back to tracing setup
- If the user's request spans multiple skills, handle them in logical order (setup → instrument → evaluate)
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