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Performance Scaling

Checked
bejranonda·vlatest·other

Automatically adjusts resource allocation to optimize application performance under varying load conditions.

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 bejranonda/llm-autonomous-agent-plugin-for-claude --skill performance-scaling -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

bejranonda

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 bejranonda/llm-autonomous-agent-plugin-for-claude at d7c47f644e.

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

Commit
d7c47f644e
Manifest
skills/performance-scaling/SKILL.md
Scanned
1 file, 345 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 enables AI agents to optimize their resource usage and improve performance. It dynamically adjusts parameters like context window size, API call frequency, and processing complexity based on task requirements. The goal is to achieve faster completion times and reduce operational costs by efficiently allocating resources.

When to use it

  • Complex tasks: Use when an agent is struggling with lengthy or computationally intensive operations.
  • Resource constraints: Employ this skill when operating within limited budget or API rate limits.
  • Slow response times: Integrate to accelerate task completion and improve user experience.
  • Cost optimization: Leverage for minimizing expenses associated with AI agent execution.

Key capabilities

  • Dynamic parameter adjustment
  • Context window size management
  • API call frequency control
  • Processing complexity adaptation

Example prompts

  • "Optimize the agent's performance for this research task."
  • "Reduce API costs while maintaining acceptable response times."
  • "Adjust processing complexity to handle this large dataset efficiently."

Tips & gotchas

This skill requires careful monitoring of resource usage and performance metrics to ensure optimal results. It’s recommended to start with conservative adjustments and gradually increase optimization levels.