Login
mlflow/skills3 files

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.

Use this skillView repositoryView on skills.sh
Specification
Skill ID
mlflow/skills/querying-mlflow-metrics
Publisher
mlflow
Repository
skills
Installs
0
Files
3
Synced
Sep 23, 2026
How to use it

Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.

mlflow/skills/querying-mlflow-metricsInstalls these files
  • SKILL.md
  • references/api_reference.md
  • scripts/fetch_metrics.py

What this skill tells the agent

MLflow Metrics

Run scripts/fetch_metrics.py to query metrics from an MLflow tracking server.

Examples

Token usage summary:

python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -m total_tokens -a SUM,AVG

Output: AVG: 223.91 SUM: 7613

Hourly token trend (last 24h):

python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -m total_tokens -a SUM \
    -t 3600 --start-time="-24h" --end-time=now

Output: Time-bucketed token sums per hour

Latency percentiles by trace:

python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -m latency -a AVG,P95 -d trace_name

Error rate by status:

python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -m trace_count -a COUNT -d trace_status

Quality scores by evaluator (assessments):

python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -v ASSESSMENTS \
    -m assessment_value -a AVG,P50 -d assessment_name

Output: Average and median scores for each evaluator (e.g., correctness, relevance)

Assessment count by name:

python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -v ASSESSMENTS \
    -m assessment_count -a COUNT -d assessment_name

JSON output: Add -o json to any command.

Arguments

ArgRequiredDescription
-s, --serverYesMLflow server URL
-x, --experiment-idsYesExperiment IDs (comma-separated)
-m, --metricYestrace_count, latency, input_tokens, output_tokens, total_tokens
-a, --aggregationsYesCOUNT, SUM, AVG, MIN, MAX, P50, P95, P99
-d, --dimensionsNoGroup by: trace_name, trace_status
-t, --time-intervalNoBucket size in seconds (3600=hourly, 86400=daily)
--start-timeNo-24h, -7d, now, ISO 8601, or epoch ms
--end-timeNoSame formats as start-time
-o, --outputNotable (default) or json

For SPANS metrics (span_count, latency), add -v SPANS. For ASSESSMENTS metrics, add -v ASSESSMENTS.

See references/api_reference.md for filter syntax and full API details.

More from mlflow

mlflow/skills

Agent Evaluation

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).

mlflow/skills

Analyzing Mlflow Session

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".

mlflow/skills

Analyzing Mlflow Trace

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".

Use this skill in a project

Open any RiverX project, search the Skills panel for mlflow/skills/querying-mlflow-metrics, and install it in one click.

Use this skill