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Play Learn Lift

Checked
simhacker·vlatest·ai-ml·MIT

Play Learn Lift helps you quickly generate creative content ideas by combining prompts for playful concepts, learning insights, and uplifting themes.

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 simhacker/moollm --skill play-learn-lift -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

simhacker

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 simhacker/moollm at 8f6d840b2f.

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

Commit
8f6d840b2f
Manifest
skills/play-learn-lift/SKILL.md
Scanned
1 file, 321 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 play-learn-lift skill allows AI agents to iteratively improve their performance on a task through repeated attempts, learning from mistakes and refining strategies. It facilitates a cycle of play (attempting the task), learn (analyzing results), and lift (adjusting approach). This enables agents to adapt and optimize their actions over time without explicit programming for every scenario.

When to use it

  • Complex tasks with no clear solution: For scenarios where optimal behavior isn't immediately obvious, like navigating a maze or playing a game.
  • Simulating human learning: To mimic how humans learn through trial and error in dynamic environments.
  • Automated optimization: When you need an agent to automatically improve its performance on a defined task.
  • Exploring new strategies: To allow the AI to discover novel approaches that might not be initially considered.

Key capabilities

  • Iterative improvement loop (play, learn, lift)
  • Performance analysis and feedback integration
  • Strategy refinement based on results
  • Adaptation to dynamic environments

Example prompts

  • "Use play-learn-lift to improve your performance at the 'Space Invaders' game."
  • "Apply play-learn-lift to optimize my route through this delivery area, considering traffic and time constraints."
  • "Using play-learn-lift, learn how to best negotiate a price for this item based on market data."

Tips & gotchas

The effectiveness of play-learn-lift depends heavily on the quality of feedback provided. Ensure that the agent receives clear and actionable information about its performance after each attempt.