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Performance Tuner

Checked
gravito-framework·vlatest·other

Automatically optimizes application resource allocation and configuration for peak performance using Gravito's adaptive algorithms.

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 gravito-framework/gravito --skill performance-tuner -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

gravito-framework

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 gravito-framework/gravito at ae0db3aa40.

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

Commit
ae0db3aa40
Manifest
.skills/performance-tuner/SKILL.md
Scanned
1 file, 29 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 Performance Tuner skill enables AI agents to optimize Gravito applications for speed and efficiency, aiming for response times under 100ms. It focuses on identifying performance bottlenecks through profiling and then applying targeted optimizations such as database indexing, caching, and frontend asset optimization. The skill leverages adaptive algorithms from Gravito to automatically adjust resource allocation and configuration.

When to use it

  • When experiencing slow application response times.
  • After identifying bottlenecks in logs or using profiling tools.
  • To improve the performance of Atlas databases through indexing strategies.
  • For optimizing frontend asset loading, particularly when using Vue.js.
  • To reduce query execution times and minimize data transfer overhead.

Key capabilities

  • Database Indexing: Adds indexes to frequently queried columns in Atlas.
  • Caching: Implements caching solutions using Redis or in-memory caches.
  • Frontend Optimization: Optimizes asset loading and implements lazy-loading for Vue applications.
  • N+1 Query Prevention: Utilizes preload() in Atlas to avoid N+1 query problems.
  • Data Transfer Optimization: Recommends the use of Streams for large data transfers.
  • Bundle Size Reduction: Suggests techniques like tree-shaking and vendor splitting to minimize bundle size.

Example prompts

  • "Analyze this application log file and identify performance bottlenecks."
  • "Suggest database indexing strategies for my Atlas queries."
  • "How can I optimize asset loading in my Vue frontend?"

Tips & gotchas

  • Requires access to application logs and profiling tools to accurately identify bottlenecks.
  • The skill is specifically designed for Gravito applications and may not be applicable elsewhere.
  • Familiarity with Atlas database concepts (e.g., preload()) will help in understanding the recommendations.