Skip to content
TrustedSkillsbeta
Back to all skills

Spark Optimization

Checked
wshobson·vlatest·data·MIT

Analyzes Spark job configurations to identify bottlenecks and suggest optimizations for faster data processing.

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 wshobson/agents --skill spark-optimization -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

wshobson

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 wshobson/agents at 46891e7e60.

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

Commit
46891e7e60
Manifest
plugins/data-engineering/skills/spark-optimization/SKILL.md
Scanned
2 files, 398 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

The spark-optimization skill helps improve the performance of Apache Spark applications by analyzing code and suggesting optimizations. It identifies inefficiencies such as suboptimal data partitioning, unnecessary transformations, or poor memory usage and provides actionable recommendations to enhance execution speed and resource utilization.

When to use it

  • When tuning a Spark job for better runtime performance.
  • Before deploying a Spark application in production to ensure optimal resource usage.
  • When encountering slow query execution or high memory consumption in Spark jobs.
  • During the development phase of a data pipeline to preemptively identify bottlenecks.

Key capabilities

  • Analyzes Spark code for inefficiencies
  • Recommends data partitioning strategies
  • Identifies redundant transformations
  • Suggests memory and cache optimization techniques

Example prompts

  • "Analyze this Spark job for performance improvements."
  • "What are the best practices to optimize this DataFrame transformation?"
  • "How can I reduce the execution time of my Spark application?"

Tips & gotchas

  • Ensure your code is well-formatted and includes relevant comments for accurate analysis.
  • The skill may not account for specific cluster configurations or data distributions, so manual tuning might still be required.