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Agentdb Memory Patterns

Checked
ruvnet·vlatest·other·MIT

Identifies recurring patterns in past interactions to predict user needs and proactively offer relevant information.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

Installs under a different name. Its SKILL.md names it AgentDB Memory Patterns, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add ruvnet/ruflo --skill "AgentDB Memory Patterns" --full-depth -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

ruvnet

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 ruvnet/claude-flow at 49da6ecc44.

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

Commit
49da6ecc44
Manifest
.agents/skills/agentdb-memory-patterns/SKILL.md
Scanned
3 files, 918 lines
Copies in repo
3 — 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

This skill enables AI agents to store and retrieve information using structured memory patterns within an AgentDB environment. It facilitates persistent data management, allowing agents to maintain context across different interactions and sessions effectively.

When to use it

  • Building long-running agents that need to remember user preferences or historical data over time.
  • Creating multi-turn conversation flows where context from earlier messages must be preserved for later steps.
  • Developing applications requiring structured storage of entities, relationships, or event logs.
  • Implementing systems where state persistence is critical for accurate decision-making across sessions.

Key capabilities

  • Structured memory pattern implementation for organized data storage.
  • Integration with AgentDB for reliable data retrieval and management.
  • Support for maintaining context in complex, multi-step agent workflows.
  • Efficient handling of persistent state information within AI agent architectures.

Example prompts

  • "Store this user preference in the memory database for future reference."
  • "Retrieve previous conversation context to continue our discussion seamlessly."
  • "Update the agent's memory with this new project milestone and related details."

Tips & gotchas

Ensure your AgentDB instance is properly configured before attempting to use memory patterns, as improper setup can lead to data loss or retrieval failures. Be mindful of storage limits and optimize memory usage by only storing essential, frequently accessed information to maintain performance efficiency.