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Cpp Reinforcement Learning

Checked
aznatkoiny·vlatest·ai-ml·MIT

This skill trains C++ agents using reinforcement learning, enabling automated development of intelligent programs through trial and error.

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 aznatkoiny/zai-skills --skill cpp-reinforcement-learning -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

aznatkoiny

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 aznatkoiny/zai-skills at b881ee3a6f.

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

Commit
b881ee3a6f
Manifest
AI-Toolkit/skills/cpp-reinforcement-learning/SKILL.md
Scanned
6 files, 2,742 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 allows AI agents to leverage C++ code for reinforcement learning tasks. It enables the agent to execute and analyze custom C++ algorithms, facilitating complex decision-making processes within simulated environments or real-world applications. The skill provides a bridge between high-level AI planning and low-level computational power for optimized performance.

When to use it

  • Complex Simulations: When your reinforcement learning environment requires computationally intensive physics simulations best handled in C++.
  • Custom Algorithms: To integrate bespoke reinforcement learning algorithms written in C++ that are not readily available in standard AI libraries.
  • Performance Optimization: For scenarios where speed and efficiency are critical, allowing for faster training and execution of RL agents.
  • Hardware Acceleration: To utilize hardware acceleration capabilities often more easily accessible through C++ code.

Key capabilities

  • Execution of C++ reinforcement learning algorithms
  • Integration with existing AI agent frameworks
  • Analysis of results from C++ code within the agent's workflow
  • Support for custom reward functions implemented in C++

Example prompts

  • "Run the my_rl_algorithm.cpp file and report the average reward over 100 episodes."
  • "Execute this C++ code to calculate the Q-value for state X, action Y."
  • “Can you optimize this reinforcement learning policy using the provided C++ implementation?”

Tips & gotchas

  • Ensure that any external dependencies required by your C++ code are available in the execution environment.
  • Debugging can be challenging; thorough testing and logging within your C++ code is recommended.