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

.claude/skills/instrumenting-with-mlflow-tracing/SKILL.md

instrumenting-with-mlflow-tracing is a skill for Claude Code from alessandro9110/Speech-To-Text-With-Databricks. It costs 111 tokens per session (647 once invoked), scanned A, original, MIT.

A project-status command that reads saved files and reports the current phase, budget use, and paused tasks.

In plain words
What is it for?
Use it to inspect the session, model budget, and paused-task files without changing them.
Why use it?
It brings scattered project-state details into one concise report and identifies missing or unreadable files.

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 111 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/instrumenting-with-mlflow-tracing/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 instrumenting-with-mlflow-tracing

README.md
[![agentmods](https://agentmods.dev/badge/skills/alessandro9110/speech-to-text-with-databricks/instrumenting-with-mlflow-tracing/github.svg)](https://agentmods.dev/skills/alessandro9110/speech-to-text-with-databricks/instrumenting-with-mlflow-tracing)
Your own site
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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 instrumenting-with-mlflow-tracing

Your own site · 80×15
<a href="https://agentmods.dev/skills/alessandro9110/speech-to-text-with-databricks/instrumenting-with-mlflow-tracing"><img src="https://agentmods.dev/badge/skills/alessandro9110/speech-to-text-with-databricks/instrumenting-with-mlflow-tracing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 111 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 647 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 $0.00111 $0.00647
Opus 5.5 $0.00044 $0.00259
Sonnet 5.5 $0.00022 $0.00129
Haiku 4.5 $0.00011 $0.00065

Measured 1mo ago against content hash fbe76e4579cc, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

instrumenting-with-mlflow-tracing 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/instrumenting-with-mlflow-tracing/SKILL.md · 78 lines

How it starts

The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.

MLflow Tracing Instrumentation Guide

Language-Specific Guides

Based on the user's project, load the appropriate guide:

  • Python projects: Read references/python.md
  • TypeScript/JavaScript projects: Read references/typescript.md

If unclear, check for package.json (TypeScript) or requirements.txt/pyproject.toml (Python) in the project.

What to Trace

Trace these operations (high debugging/observability value):

Operation Type Examples Why Trace
Root operations Main entry points, top-level pipelines, workflow steps End-to-end latency, input/output logging
LLM calls Chat completions, embeddings Token usage, latency, prompt/response inspection
Retrieval Vector DB queries, document fetches, search Relevance debugging, retrieval quality
Tool/function calls API calls, database queries, web search External dependency monitoring, error tracking
Agent decisions Routing, planning, tool selection Understand agent reasoning and choices
External services HTTP APIs, file I/O, message queues Dependency failures, timeout tracking

Skip tracing these (too granular, adds noise):

  • Simple data transformations (dict/list manipulation)
  • String formatting, parsing, validation
  • Configuration loading, environment setup
  • Logging or metric emission
  • Pure utility functions (math, sorting, filtering)

Rule of thumb: Trace operations that are important for debugging and identifying issues in your application.

Feedback Collection

Log user feedback on traces for evaluation, debugging, and fine-tuning. Essential for identifying quality issues in production.

See references/feedback-collection.md for:

  • Recording user ratings and comments with mlflow.log_feedback()
  • Capturing trace IDs to return to clients
  • LLM-as-judge automated evaluation

Reference Documentation

Production Deployment

See references/production.md for:

  • Environment variable configuration
  • Async logging for low-latency applications
  • Sampling configuration (MLFLOW_TRACE_SAMPLING_RATIO)
  • Lightweight SDK (mlflow-tracing)
  • Docker/Kubernetes deployment

Read the full file on GitHub · 78 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 · 78 lines · 111 tokens per session scan A fbe76e4579cc

Subscribe to this mod's changes

instrumenting-with-mlflow-tracing is a skill published in the GitHub repository alessandro9110/Speech-To-Text-With-Databricks (4 stars, last pushed 8mo ago), licensed MIT. It adds 111 tokens to every session and 647 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 metricsProject managementdatabricksdelta-lakegenerative-ailakehousemachinespeech-to-text
Related

instrumenting-with-mlflow-tracing compared with skills from other repositories

instrumenting-with-mlflow-tracing next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
instrumenting-with-mlflow-tracingalessandro9110/Speech-To-Text-With-DatabricksSkill48mo agoMITA111Claude Code
databricks-expertpersonamanagmentlayer/pclSkill4411d agoApache-2.0A74Claude Code
data-lake-architecturej4flmao/agent-skillsSkill383d agoMITA170Claude Code, Codex
datacoolie-builddatacoolie/datacoolieSkill1115d agoAGPL-3.0A57Codex
datacoolie-designdatacoolie/datacoolieSkill1115d agoAGPL-3.0A46Claude Code, Codex
datacoolie-discoverdatacoolie/datacoolieSkill1115d agoAGPL-3.0A49Claude Code, Codex
datacoolie-provisiondatacoolie/datacoolieSkill1115d agoAGPL-3.0A47Claude Code, Codex