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Neural Network Design

Checked
aj-geddes·vlatest·ai-ml·MIT

Automatically generates optimal neural network architectures based on specified task requirements and dataset characteristics.

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 Neural Network Design, 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 aj-geddes/useful-ai-prompts --skill "Neural Network Design" -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

aj-geddes

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 aj-geddes/useful-ai-prompts at 3f5182cfd7.

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

Commit
3f5182cfd7
Manifest
skills/neural-network-design/SKILL.md
Scanned
3 files, 376 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 enables AI agents to generate and refine architectural blueprints for neural networks, including layer configurations, activation functions, and connectivity patterns. It assists in translating high-level problem requirements into specific model structures suitable for training or inference tasks.

When to use it

  • Model Architecture Planning: Defining the topology (e.g., CNN vs. Transformer) before writing code or configuring frameworks like PyTorch or TensorFlow.
  • Optimization Strategy: Determining hyperparameters such as learning rates, batch sizes, and regularization techniques based on dataset characteristics.
  • Debugging Performance Issues: Analyzing network depth or width to diagnose overfitting, underfitting, or vanishing gradient problems.
  • Resource Allocation: Estimating computational requirements (VRAM, FLOPs) by designing efficient network structures for edge devices or cloud clusters.

Key capabilities

  • Generates complete code snippets for defining custom neural networks in popular deep learning libraries.
  • Suggests optimal layer types and activation functions based on specific task constraints (e.g., image classification vs. time-series forecasting).
  • Provides comparative analysis of different architectural approaches to solve a given problem.

Example prompts

  • "Design a lightweight CNN architecture for classifying medical images on an embedded device with limited memory."
  • "Create a PyTorch module definition for a Transformer-based model optimized for low-latency text generation."
  • "Propose a neural network structure that minimizes overfitting when training on a small dataset of 500 samples."

Tips & gotchas

Ensure you specify the target deep learning framework (e.g., PyTorch, Keras) in your request to receive compatible code syntax. While this skill excels at structural design, it does not replace the need for actual data preprocessing or model training execution.