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Memory Management

Official
anthropics·vlatest·other·Apache-2.0

Anthropics' anthropics-memory-management optimizes long-term knowledge retention and recall for AI agents, improving performance over time.

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 anthropics/knowledge-work-plugins --skill memory-management -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

anthropics

Matched to the vendor's own GitHub organisation.

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 anthropics/knowledge-work-plugins at da38ec1ee8.

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

Commit
da38ec1ee8
Manifest
productivity/skills/memory-management/SKILL.md
Scanned
1 file, 304 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 efficiently manage memory during complex tasks, ensuring optimal performance and resource allocation. It allows for dynamic memory allocation, retention of critical information, and intelligent pruning of irrelevant data.

When to use it

  • When handling large datasets or long-running processes that require sustained memory usage.
  • During multi-step reasoning where retaining context across steps is essential.
  • In environments with limited computational resources, to prevent memory overflow.
  • For tasks requiring selective recall of past interactions or data points.

Key capabilities

  • Dynamic memory allocation based on task requirements
  • Context retention for multi-step workflows
  • Memory pruning to remove irrelevant information
  • Optimization of resource usage during intensive operations

Example prompts

  • "Manage memory while processing this large dataset."
  • "Ensure the AI retains key insights from previous steps in this analysis."
  • "Optimize memory use during this complex simulation."

Tips & gotchas

  • Ensure the system has sufficient baseline resources to support dynamic memory adjustments.
  • Some memory management strategies may require configuration based on specific task types or data sizes.