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Goodvibes Memory

Checked
mgd34msu·vlatest·other·MIT

This AI remembers positive interactions and affirmations to boost your mood and reinforce helpful conversations.

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 mgd34msu/goodvibes-plugin --skill goodvibes-memory -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

mgd34msu

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 mgd34msu/goodvibes-plugin at d19fa3c57e.

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

Commit
d19fa3c57e
Manifest
plugins/goodvibes/skills/goodvibes-memory/SKILL.md
Scanned
1 file, 58 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

The Goodvibes Memory skill enables AI agents to retain information across sessions, preventing repeated errors, reinforcing proven approaches, and ensuring consistency in architectural decisions and project preferences. It captures these learnings in structured JSON files (.goodvibes/memory/) and human-readable markdown logs (.goodvibes/logs/). The agent must read from this memory before starting work and write updates after completing tasks to facilitate cross-session learning.

When to use it

  • When you want an AI agent to avoid repeating past failures encountered during a task.
  • To ensure the consistent application of established patterns and approaches in its work.
  • To maintain adherence to prior architectural decisions made on a project.
  • When needing to consistently apply project-specific preferences across multiple interactions.

Key capabilities

  • Memory Storage: Stores decisions, patterns, failures, and preferences in JSON files.
  • Keyword-Based Search: Allows agents to search memory entries using keywords related to their current task.
  • Precision Read: A method for reading specific memory files with a defined verbosity level.
  • Failure Prevention: Logs past errors along with resolutions and prevention strategies.
  • Pattern Reuse: Provides access to proven approaches that can be applied consistently.
  • Decision Tracking: Records architectural decisions, their rationale, and scope.

Example prompts

  • "Before I start fetching data from the API, check failures.json for any authentication errors."
  • “Read patterns.json for examples of how to handle config updates.”
  • "Review decisions.json to see if there are any architectural guidelines related to building this feature."

Tips & gotchas

  • Mandatory Read Phase: The agent must read the memory files before starting any task.
  • Keyword Precision: Use specific keywords when searching memory entries for relevant information.
  • Active Decisions Only: When reviewing decisions.json, only consider decisions with a status of "active."