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

.claude/skills/databricks-mlflow-evaluation/SKILL.md

databricks-mlflow-evaluation is a skill for Claude Code from alessandro9110/Speech-To-Text-With-Databricks. It costs 92 tokens per session (2,208 once invoked), scanned A, original, MIT.

A guide for evaluating generative-AI agents with MLflow 3. MLflow is a tool for tracking machine-learning work; here it measures agent answers with datasets, scoring rules, and production traces, which record how an agent ran.

In plain words
What is it for?
Use it to write MLflow evaluations, create scorer functions, build datasets from traces, monitor agents, and assess retrieval-based answers.
Why use it?
It provides workflows and exact interface guidance for turning real agent runs into evaluation data and checking qualities such as correctness, safety, and grounded answers.

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 →

not rated 4repo 8mo ago A scan 92 tokens original 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/databricks-mlflow-evaluation/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/alessandro9110/Speech-To-Text-With-Databricks

Made for: Claude Code.

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<a href="https://agentmods.dev/skills/alessandro9110/speech-to-text-with-databricks/databricks-mlflow-evaluation"><img src="https://agentmods.dev/badge/skills/alessandro9110/speech-to-text-with-databricks/databricks-mlflow-evaluation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 92 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,208 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 original No closer match found 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 $0.00092 $0.02208
Opus 5.5 $0.00037 $0.00883
Sonnet 5.5 $0.00018 $0.00442
Haiku 4.5 $0.00009 $0.00221

Measured 1mo ago against content hash c93d1888371d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

databricks-mlflow-evaluation 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.

.claude/skills/databricks-mlflow-evaluation/SKILL.md · 149 lines

How it starts

The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.

MLflow 3 GenAI Evaluation

Before Writing Any Code

  1. Read GOTCHAS.md - 15+ common mistakes that cause failures
  2. Read CRITICAL-interfaces.md - Exact API signatures and data schemas

End-to-End Workflows

Follow these workflows based on your goal. Each step indicates which reference files to read.

Workflow 1: First-Time Evaluation Setup

For users new to MLflow GenAI evaluation or setting up evaluation for a new agent.

Step Action Reference Files
1 Understand what to evaluate user-journeys.md (Journey 0: Strategy)
2 Learn API patterns GOTCHAS.md + CRITICAL-interfaces.md
3 Build initial dataset patterns-datasets.md (Patterns 1-4)
4 Choose/create scorers patterns-scorers.md + CRITICAL-interfaces.md (built-in list)
5 Run evaluation patterns-evaluation.md (Patterns 1-3)

Workflow 2: Production Trace -> Evaluation Dataset

For building evaluation datasets from production traces.

Step Action Reference Files
1 Search and filter traces patterns-trace-analysis.md (MCP tools section)
2 Analyze trace quality patterns-trace-analysis.md (Patterns 1-7)
3 Tag traces for inclusion patterns-datasets.md (Patterns 16-17)
4 Build dataset from traces patterns-datasets.md (Patterns 6-7)
5 Add expectations/ground truth patterns-datasets.md (Pattern 2)

Workflow 3: Performance Optimization

For debugging slow or expensive agent execution.

Step Action Reference Files
1 Profile latency by span patterns-trace-analysis.md (Patterns 4-6)
2 Analyze token usage patterns-trace-analysis.md (Pattern 9)
3 Detect context issues patterns-context-optimization.md (Section 5)
4 Apply optimizations patterns-context-optimization.md (Sections 1-4, 6)
5 Re-evaluate to measure impact patterns-evaluation.md (Pattern 6-7)

Read the full file on GitHub · 149 lines

Files

What ships with it

11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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 · 149 lines · 92 tokens per session scan A c93d1888371d

Subscribe to this mod's changes

databricks-mlflow-evaluation is a skill published in the GitHub repository alessandro9110/Speech-To-Text-With-Databricks (4 stars, last pushed 8mo ago), licensed MIT. It adds 92 tokens to every session and 2,208 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-02.

Analytics and metricsData and AIRAG and vectorsdatabricksdelta-lakegenerative-ailakehousemachinespeech-to-text
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