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Speech-To-Text-With-Databricks: Skill for Claude Code

.claude/skills/querying-mlflow-metrics/SKILL.md

querying-mlflow-metrics is a skill for Claude Code from alessandro9110/Speech-To-Text-With-Databricks. It costs 57 tokens per session (741 once invoked), scanned A, a copy of querying-mlflow-metrics, MIT.

A skill for retrieving summary measurements from MLflow tracking servers. It can query token usage, response delay, trace counts, and quality-assessment results; a trace is a recorded run of an AI operation.

In plain words
What is it for?
Use it to inspect token use, AI costs, latency, error rates, usage trends, and evaluator scores from MLflow data.
Why use it?
It removes the need to write separate queries for common usage and performance questions and helps compare results over time or across groups.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is alessandro9110/Speech-To-Text-With-Databricks's own configuration. It tells Claude Code how to work on Speech-To-Text-With-Databricks itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything Speech-To-Text-With-Databricks configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python scripts/fetch_metrics.py -s http://localhost:5000 -x 1 -m total_tokens -a SUM,AVG.

not rated 4repo 8mo ago A scan 57 tokens copy · 100% MIT
View source ↗ alessandro9110/Speech-To-Text-With-Databricks
Reuse

Borrowing it

Nothing to install: this file belongs to alessandro9110/Speech-To-Text-With-Databricks. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/alessandro9110/Speech-To-Text-With-Databricks/main/.claude/skills/querying-mlflow-metrics/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/alessandro9110/Speech-To-Text-With-Databricks

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for querying-mlflow-metrics

README.md
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<a href="https://agentmods.dev/skills/alessandro9110/speech-to-text-with-databricks/querying-mlflow-metrics"><img src="https://agentmods.dev/badge/skills/alessandro9110/speech-to-text-with-databricks/querying-mlflow-metrics/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for querying-mlflow-metrics

Your own site · 80×15
<a href="https://agentmods.dev/skills/alessandro9110/speech-to-text-with-databricks/querying-mlflow-metrics"><img src="https://agentmods.dev/badge/skills/alessandro9110/speech-to-text-with-databricks/querying-mlflow-metrics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 741 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00057 $0.00741
Opus 5.5 $0.00023 $0.00296
Sonnet 5.5 $0.00011 $0.00148
Haiku 4.5 $0.00006 $0.00074

Measured 1mo ago against content hash dae35439982c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

querying-mlflow-metrics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to querying-mlflow-metrics — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.claude/skills/querying-mlflow-metrics/SKILL.md · 68 lines

How it starts

The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

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.

Read the full file on GitHub · 68 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago First seen · 68 lines · 57 tokens per session scan A dae35439982c

Subscribe to this mod's changes

querying-mlflow-metrics is a skill published in the GitHub repository alessandro9110/Speech-To-Text-With-Databricks (4 stars, last pushed 8mo ago), licensed MIT. It adds 57 tokens to every session and 741 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to querying-mlflow-metrics, differing in 0 lines, and is treated as a copy.

Analytics and metricsdatabricksdelta-lakegenerative-ailakehousemachinespeech-to-text
Related

querying-mlflow-metrics compared with skills from other repositories

querying-mlflow-metrics next to 6 skills from other repositories
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datacoolie-provisiondatacoolie/datacoolieSkill1115d agoAGPL-3.0A47Claude Code, Codex