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Agentdb Memory Patterns

Checked
proffesor-for-testing·vlatest·testing·MIT

Helps with testing, patterns as part of testing, debugging, and quality assurance workflows.

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 proffesor-for-testing/agentic-qe --skill "AgentDB Memory Patterns" -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

proffesor-for-testing

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 proffesor-for-testing/agentic-qe at 124d8c735e.

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

Commit
124d8c735e
Manifest
.claude/skills/agentdb-memory-patterns/SKILL.md
Scanned
1 file, 306 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, "proffesor-for-testing-agentdb-memory-patterns," allows AI agents to analyze and understand patterns within AgentDB memory. It's designed for testing purposes, specifically focused on identifying how an agent stores and retrieves information over time. The skill provides insights into the agent’s memory structure and behavior, enabling developers to debug and improve its performance.

When to use it

  • Debugging Memory Issues: Investigate why an AI agent is forgetting crucial details or exhibiting inconsistent behavior across interactions.
  • Testing Agentic Reasoning: Evaluate if an agent's reasoning processes are accurately reflected in its memory patterns.
  • Optimizing Memory Usage: Identify redundant or inefficient data storage within the agent’s memory to improve performance and reduce costs.
  • Verifying Knowledge Retention: Confirm that the agent is retaining and recalling information as expected over extended conversations.

Key capabilities

  • AgentDB memory analysis
  • Pattern identification in AgentDB memory
  • Testing of AI agent memory behavior
  • Insights into agentic reasoning processes

Example prompts

  • "Analyze my agent's AgentDB memory for patterns related to user preferences."
  • "Show me how the agent stores information about past conversations."
  • "Identify any inconsistencies in the agent’s memory regarding product pricing."

Tips & gotchas

This skill requires access to and understanding of your AI agent's AgentDB. The effectiveness of the analysis depends on the quality and structure of data stored within the AgentDB.