querying-mlflow-metrics
mlflow/skills / querying-mlflow-metrics
shows MLflow tokens, latency and evaluations , which improves: spots cost drift
score 9/10high risk82 repo stars
Details
- judge’s score
- 9/10
- place
- outside the packs
- publisher
- mlflow
- status
- new
- pinned commit
- 3b0d56a on GitHub (new tab)
- skill’s last commit
- Jan 28, 2026
- repo stars
- 82
- main type
- Data
Judgement
Precise trigger conditions and exact commands with expected outputs. MLflow-specific API, hence useful. Score lowered: incomplete argument table and aggregation type inconsistencies with the appendix.
reservations
- The script scripts/fetch_metrics.py is not provided: its contents and actual behavior cannot be verified.
- The argument table lists only some metrics (span_count and assessment_value missing), and the ASSESSMENTS dimensions are not documented in SKILL.md.
- Inconsistency: SKILL.md cites P50, P95, P99 as aggregations, while the appendix describes PERCENTILE with percentile_value.
- No MLflow version or required /api/3.0 endpoint is mentioned, which limits compatibility checks.
sub-scores
- Triggering
- 2/2
- Added value
- 2/2
- Precision
- 2/2
- Scope
- 2/2
- Quality
- 1/2
Alerts
1 alert, by category
Static scan
- high risk
scripts run by the SKILL.md
scripts/fetch_metrics.py
Original description
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.
added on Oct 19, 2026 · license Apache-2.0