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querying-mlflow-metrics

Fetch aggregated MLflow tracking metrics from servers in table or JSON formats.

Updated Jan 30, 2026
Search Tags:#metrics#fetch#latency#traces#mlflow#token-usage
One-click install
npx skills add https://github.com/teegin-g/Slopcast --skill querying-mlflow-metrics-teegin-g
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: querying-mlflow-metrics
Source: https://github.com/teegin-g/Slopcast/tree/main/.agents/skills/querying-mlflow-metrics
Command: npx skills add https://github.com/teegin-g/Slopcast --skill querying-mlflow-metrics-teegin-g

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Aggregates and fetches MLflow tracking metrics (token usage, latency, trace counts, and quality evaluations) from MLflow servers to enable quick assessment of model performance and usage.

Core Features & Use Cases

  • Fetch and summarize metrics across experiments and traces (token usage, latency, trace_count, and assessments).
  • Compare performance and usage trends over time, identify outliers, and support decision-making for model improvements.
  • Use Case: Monitor cost and efficiency by aggregating metrics from multiple MLflow runs across teams.

Quick Start

Run the fetch_metrics.py script against your MLflow server to retrieve and summarize metrics.

Frequently Asked Questions about querying-mlflow-metrics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fetch aggregated MLflow metrics across multiple runs?▼

To fetch aggregated MLflow metrics, you can run a script against your MLflow server URL to retrieve and summarize per-experiment and per-trace metrics like token usage, latency, and trace counts. You can configure experiment IDs, time-bounds, and output formats.

What MLflow tracking metrics can I summarize for model performance?▼

You can summarize MLflow tracking metrics such as token usage, latency, trace counts, and quality evaluations. These metrics help you assess model performance and usage trends across multiple runs and experiments.

Can I filter MLflow metrics by time-bounds and experiment IDs?▼

Yes, you can filter MLflow metrics by specifying time-bounds and experiment IDs. The skill allows you to configure these parameters along with selected metrics and optional dimensions to narrow down the aggregated results.

Does this tool work with MLflow servers to monitor token usage and latency?▼

Yes, this tool works with MLflow servers to monitor token usage and latency. It identifies and fetches aggregated MLflow tracking metrics, enabling you to monitor cost and efficiency by aggregating data from multiple runs.

What is the best way to compare MLflow run metrics over time?▼

The best way to compare MLflow run metrics over time is to fetch and summarize metrics across experiments and traces using configurable time-bounds. This allows you to compare performance and usage trends, identify outliers, and support model improvement decisions.

What output formats are supported for MLflow metrics aggregation?▼

The supported output formats for MLflow metrics aggregation are table and JSON. This allows you to easily integrate the fetched metrics into your existing analysis pipelines or reporting tools.

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