querying-mlflow-metrics — Claude Code skill: MLflow metrics
Pulls aggregated trace metrics from MLflow: token usage, latency, trace counts and quality evaluations. Useful when you want experiment stats without leaving the chat.
npx skills add mlflow/skills --skill querying-mlflow-metrics -a claude-codeWhat querying-mlflow-metrics does
The skill ships a script at scripts/fetch_metrics.py. It queries an MLflow tracking server and returns aggregates for an experiment: SUM, AVG, MIN, MAX, P50, P95, P99 or COUNT. Available metrics are trace_count, latency, input_tokens, output_tokens and total_tokens.
Results can be grouped by trace_name or trace_status; for SPANS metrics add -v SPANS. Time bucketing uses -t in seconds, so 3600 gives hourly buckets and 86400 daily ones. The --start-time and --end-time flags accept -24h, -7d, now, ISO 8601 or epoch ms. Output is a table by default, and -o json switches to JSON.
Three arguments are required: -s for the server URL, -x for experiment IDs and -m for the metric. Skip any of them and the script stops, so confirm the server is reachable and the IDs are right first. Quality scores sit behind a separate flag: use -v ASSESSMENTS with assessment_value or assessment_count.
Filter syntax and the full API live in references/api_reference.md.
What it helps with
- check total token spend for an experiment
- chart an hourly token trend over the last day
- pull latency percentiles broken down by trace
- count errors grouped by trace status
- compare average quality scores across evaluators
Example prompts for the agent
- show total token usage for experiment 1
- plot the hourly total_tokens trend for the last 24 hours
- give me P95 latency by trace_name as JSON
How to install querying-mlflow-metrics
1Skills CLI (Vercel)
npx skills add mlflow/skills --skill querying-mlflow-metrics -a claude-code2Manually: put the files in place
~/.claude/skills/querying-mlflow-metrics/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/querying-mlflow-metrics/
1Skills CLI (Vercel)
npx skills add mlflow/skills --skill querying-mlflow-metrics -a codex2Manually: put the files in place
.agents/skills/querying-mlflow-metrics/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add mlflow/skills --skill querying-mlflow-metrics -a cursor2Manually: put the files in place
.cursor/skills/querying-mlflow-metrics/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install mlflow/skills querying-mlflow-metrics2Manually: put the files in place
.github/skills/querying-mlflow-metrics/SKILL.md1Skills CLI (Vercel)
npx skills add mlflow/skills --skill querying-mlflow-metrics -a gemini-cli2Platform CLI
gemini skills install https://github.com/mlflow/skills --path querying-mlflow-metricsManually: put the files in place
.devin/skills/querying-mlflow-metrics/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add mlflow/skills --skill querying-mlflow-metrics -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about querying-mlflow-metrics
How do I install querying-mlflow-metrics in Claude?
Run npx skills add mlflow/skills --skill querying-mlflow-metrics -a claude-code. The skill file then lands at ~/.claude/skills/querying-mlflow-metrics/SKILL.md.
What does querying-mlflow-metrics do?
It fetches aggregated trace metrics from an MLflow tracking server: token usage, latency, trace counts and quality evaluations. The work is done by scripts/fetch_metrics.py.
Which arguments does the script require?
The server URL -s, experiment IDs -x and a metric -m. Aggregations, grouping, time ranges and output format are optional.
Source text
SKILL.md · Apache-2.0
---
name: querying-mlflow-metrics
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.
---
# MLflow Metrics
Run `scripts/fetch_metrics.py` to query metrics from an MLflow tracking server.
## Examples
**Token usage summary:**
```bash
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):**
```bash
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:**
```bash
python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -m latency -a AVG,P95 -d trace_name
```
**Error rate by status:**
```bash
python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -m trace_count -a COUNT -d trace_status
```
**Quality scores by evaluator (assessments):**
```bash
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:**
```bash
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
| Arg | Required | Description |
|-----|----------|-------------|
| `-s, --server` | Yes | MLflow server URL |
| `-x, --experiment-ids` | Yes | Experiment IDs (comma-separated) |
| `-m, --metric` | Yes | `trace_count`, `latency`, `input_tokens`, `output_tokens`, `total_tokens` |
| `-a, --aggregations` | Yes | `COUNT`, `SUM`, `AVG`, `MIN`, `MAX`, `P50`, `P95`, `P99` |
| `-d, --dimensions` | No | Group by: `trace_name`, `trace_status` |
| `-t, --time-interval` | No | Bucket size in seconds (3600=hourly, 86400=daily) |
| `--start-time` | No | `-24h`, `-7d`, `now`, ISO 8601, or epoch ms |
| `--end-time` | No | Same formats as start-time |
| `-o, --output` | No | `table` (default) or `json` |
For SPANS metrics (`span_count`, `latency`), add `-v SPANS`.
For ASSESSMENTS metrics, add `-v ASSESSMENTS`.
See [references/api_reference.md](references/api_reference.md) for filter syntax and full API details.
© mlflow · Apache-2.0 · mlflow/skills
