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Memory Optimization

Checked
vanman2024·vlatest·agents

Reduces AI memory usage during complex tasks by intelligently pruning less relevant data for vanman2024 agents.

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 vanman2024/ai-dev-marketplace --skill memory-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

vanman2024

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 vanman2024/ai-dev-marketplace at 35f1f5a5fb.

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

Commit
35f1f5a5fb
Manifest
plugins/mem0/skills/memory-optimization/SKILL.md
Scanned
16 files, 3,830 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 optimizes the memory usage of AI agents, reducing resource consumption and improving performance. It identifies and removes unnecessary data stored in short-term memory, allowing for more efficient processing of subsequent tasks. The skill aims to prevent memory overflow errors and ensure smoother operation during extended interactions.

When to use it

  • Long conversations: When an agent is engaged in lengthy dialogues where memory accumulation could become problematic.
  • Resource-constrained environments: When running agents on devices with limited RAM or processing power.
  • Complex tasks: For agents handling intricate workflows that require significant memory allocation.
  • Error prevention: To proactively avoid "memory full" errors and maintain agent stability.

Key capabilities

  • Memory pruning
  • Unnecessary data removal
  • Resource optimization
  • Overflow error prevention

Example prompts

  • “Optimize my agent’s memory usage.”
  • “Clean up the short-term memory of this conversation.”
  • "Reduce the amount of data stored in memory."

Tips & gotchas

This skill works best when integrated into an agent's core loop. It may require some experimentation to determine optimal pruning frequency based on specific task complexity and available resources.