Skip to content
TrustedSkillsbeta
Back to all skills

Spark Optimization

Checked
rmyndharis·vlatest·data·MIT

Optimizes Apache Spark jobs using rmyndharis's techniques for faster data processing and reduced resource consumption.

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 rmyndharis/antigravity-skills --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

rmyndharis

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 rmyndharis/antigravity-skills at 3eff4af253.

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

Commit
3eff4af253
Manifest
skills/spark-optimization/SKILL.md
Scanned
1 file, 407 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 rmyndharis-spark-optimization skill optimizes Spark applications for improved performance and resource utilization. It analyzes Spark job configurations, identifies bottlenecks, and suggests adjustments to parameters like executor memory, number of partitions, and shuffle settings. This results in faster processing times and reduced infrastructure costs for Spark workloads.

When to use it

  • Slow Spark Jobs: When existing Spark jobs are taking an unexpectedly long time to complete.
  • High Infrastructure Costs: If you're spending more than expected on the resources needed to run your Spark applications.
  • Resource Contention: When Spark jobs are competing for limited cluster resources and impacting other workloads.
  • Performance Tuning: As part of a proactive effort to continuously improve the efficiency of your Spark deployments.

Key capabilities

  • Spark job configuration analysis
  • Bottleneck identification in Spark applications
  • Parameter optimization recommendations (executor memory, partitions, shuffle settings)
  • Resource utilization improvement

Example prompts

  • "Analyze this Spark application's configuration and suggest optimizations."
  • "What are the potential bottlenecks in my current Spark job?"
  • "How can I reduce the execution time of this Spark query?"

Tips & gotchas

This skill requires access to Spark application logs or configurations for analysis. The recommendations provided should be tested thoroughly in a non-production environment before implementation, as changes to Spark parameters can have unintended consequences.