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Databricks Expert

Checked
personamanagmentlayer·vlatest·data·Apache-2.0

This Databricks Expert skill provides deep knowledge and guidance on utilizing Databricks for data analysis & engineering workflows, boosting productivity.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add personamanagmentlayer/pcl --skill databricks-expert -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

personamanagmentlayer

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned personamanagmentlayer/pcl at 79ccaa9820.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
79ccaa9820
Manifest
stdlib/data/databricks-expert/SKILL.md
Scanned
2 files, 743 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill provides expert-level guidance on utilizing Databricks for data analysis and engineering workflows. It encompasses deep knowledge of Apache Spark, Delta Lake, MLflow, notebooks, cluster management, and lakehouse architecture. The skill can assist with designing and implementing scalable data pipelines and machine learning workflows within the Databricks platform, including creating optimized cluster configurations.

When to use it

  • You need help configuring a Databricks cluster for specific workloads (e.g., data engineering, SQL analytics).
  • You're looking for best practices regarding Delta Lake optimization or MLflow integration in your Databricks environment.
  • You require assistance designing scalable data pipelines and machine learning workflows on the Databricks platform.
  • You need to understand how to manage Spark configurations within a Databricks cluster.

Key capabilities

  • Expert knowledge of Databricks architecture, including Apache Spark, Delta Lake, and MLflow.
  • Ability to design and implement scalable data pipelines and machine learning workflows.
  • Guidance on configuring various types of Databricks clusters (e.g., data engineering, job cluster, high-concurrency for SQL analytics).
  • Knowledge of instance pool configuration within Databricks.
  • Understanding of Spark configurations like adaptive query execution and Delta Lake optimization settings.

Example prompts

  • "How do I configure a Databricks cluster optimized for cost when running jobs?"
  • "What are the best practices for configuring Delta Lake auto-optimization in my Databricks environment?"
  • "Can you show me an example of how to create a high-concurrency cluster for SQL analytics in Databricks?"

Tips & gotchas

  • The skill's expertise is focused on Databricks platform configuration and architecture, not general programming or data science concepts.
  • It provides guidance based on best practices; specific configurations may need adjustments based on your unique environment.
  • Familiarity with basic Databricks terminology (e.g., cluster, notebook) will improve the quality of interactions.