# TensorCAD

> TensorCAD — filip-pajalic-tensorcad. Use this tool when you need to design and prototype neural network architectures, as it provides schematic capture for creating models and generates corresponding PyTorch code, streamlining the development process and allowing for version control through git integration. It takes in graphical representations of neural networks and outputs numerical values and PyTorch code. Ideal for use cases where rapid prototyping and testing of neural network architectures are required.

Canonical page: https://skillsregistry.net/skills/filip-pajalic-tensorcad  
JSON: https://api.skillsregistry.net/v1/skills/filip-pajalic-tensorcad

## Description

Schematic capture for neural network architectures. Draw the model, get the numbers, generate the PyTorch.

## Trust

- **Trust score (0–1):** 0.73
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/Filip-Pajalic/TensorCAD)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "filip-pajalic-tensorcad"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/filip-pajalic-tensorcad` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/filip-pajalic-tensorcad/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt