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M14 Mental Model

Checked
zhanghandong·vlatest·other

Simulates user mental models to predict actions, anticipate needs, and personalize interactions within complex environments.

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 actionbook/rust-skills --skill m14-mental-model -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

zhanghandong

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 zhanghandong/rust-skills at 5c40d3ad78.

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

Commit
5c40d3ad78
Manifest
skills/m14-mental-model/SKILL.md
Scanned
2 files, 421 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 m14-mental-model skill enables AI agents to construct and utilize mental models—internal representations of knowledge, context, or processes—to better understand and respond to complex scenarios. It supports reasoning through structured frameworks, allowing the agent to simulate decision-making, predict outcomes, and adapt strategies dynamically.

When to use it

  • When solving multi-step problems that require strategic thinking and planning.
  • During scenario simulations where predicting consequences is essential.
  • In tasks involving adaptive learning or context-aware responses.
  • For creating structured reasoning paths in ambiguous or incomplete information environments.

Key capabilities

  • Construction of internal knowledge frameworks.
  • Simulation-based decision-making processes.
  • Contextual adaptation for dynamic scenarios.
  • Outcome prediction based on modeled behavior.

Example prompts

  • "Simulate how a user might react to this product launch under different market conditions."
  • "Build a mental model to predict the outcome of this negotiation strategy."
  • "Create a framework to analyze and respond to this complex problem."

Tips & gotchas

  • Ensure input data is sufficiently detailed for accurate modeling.
  • Mental models may require iterative refinement as new information becomes available.