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Memory Evolution

Checked
nhadaututtheky·vlatest·other·MIT

Memory Evolution refines and adapts AI’s knowledge recall by dynamically adjusting its internal representations for improved accuracy & relevance.

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 nhadaututtheky/neural-memory --skill memory-evolution -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

nhadaututtheky

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 nhadaututtheky/neural-memory at 2015cb9b09.

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

Commit
2015cb9b09
Manifest
.claude-plugin/skills/memory-evolution/SKILL.md
Scanned
2 files, 588 lines
Copies in repo
2 — all of them scanned
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

Memory Evolution optimizes an AI agent's memory recall by analyzing usage patterns and implementing targeted improvements. It functions like a database performance tuner, focusing on neural memory graphs to enhance accuracy and relevance. The skill identifies inefficiencies in memory retrieval, such as fragmented topics or stale chains, and suggests actions for consolidation, pruning, enrichment, and tag normalization.

When to use it

  • When the AI agent's knowledge recall is inaccurate or inconsistent.
  • To improve the efficiency of retrieving information from long-term memory.
  • When identifying recurring issues in how the AI agent uses its memories.
  • To proactively maintain a healthy and organized memory structure.
  • To address situations where certain topics have low recall confidence or missing information.

Key capabilities

  • Usage Pattern Discovery: Analyzes frequency, health, and workflow patterns of memories.
  • Recall Quality Sampling: Tests recall quality across key topics using representative queries.
  • Pattern Detection: Identifies recurring issues like fragmented topics, missing reasoning, stale chains, tag sprawl, confidence cliffs, and recall dead-ends.
  • Memory Classification: Categorizes memories as Hot, Warm, Cold, Dead, or Zombie based on access patterns.
  • Actionable Recommendations: Provides specific consolidation, pruning, enrichment, and tag normalization operations to improve memory performance.

Example prompts

  • "Run Memory Evolution to analyze my agent's memory usage."
  • "Analyze the 'postgresql' topic for recall quality issues using Memory Evolution."
  • “Optimize memory evolution focusing on tags with low confidence scores.”

Tips & gotchas

  • The skill operates as a "Memory Evolution Specialist" and requires understanding of NeuralMemory concepts.
  • If no specific focus is given, the skill will run a full evolution cycle.
  • Review the Checkpoint Log to understand the decisions made during optimization cycles for future reference.