querying-mlflow-metrics
Fetches aggregated trace metrics (token usage, latency, trace counts, quality evaluations) from MLflow tracking servers. Triggers on requests to show metrics, analyze token usage, view LLM costs, check usage trends, or query trace statistics.
Install
With your agent
Install OMGSkills skill "mlflow/skills:querying-mlflow-metrics" into this agent's normal skills directory: use the OMGSkills MCP get_skill tool, install only the pinned_install commit and path, verify SKILL.md against pinned_install.skill_md_sha, and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/mlflow/skills /tmp/skills && ln -s /tmp/skills/querying-mlflow-metrics ~/.claude/skills/querying-mlflow-metricsFrom README
MLflow Metrics Run scripts/fetchmetrics.py to query metrics from an MLflow tracking server. Examples Token summary: Output: AVG: 223.91 SUM: 7613 Hourly token trend (last 24h): Output: Time-bucketed token sums per hour Latency percentiles by trace: Error rate by status: Quality scores by evaluator (assessments): Output: Average and median scores for each evaluator (e.g., correctness, relevance) Assessment count by name: JSON output: Add -o json to any command.
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