This skill covers designing and implementing neural network architectures including CNNs, RNNs, Transformers, and ResNets using PyTorch and TensorFlow, with focus on architecture selection, layer composition, and optimization techniques.
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AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionneural-network-designExecute the skills CLI command in your project's root directory to begin installation:
Fetches neural-network-design from aj-geddes/useful-ai-prompts and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate neural-network-design. Access via /neural-network-design in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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This skill covers designing and implementing neural network architectures including CNNs, RNNs, Transformers, and ResNets using PyTorch and TensorFlow, with focus on architecture selection, layer composition, and optimization techniques.
import torch
import torch.nn as nn
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
# 1. Feedforward Neural Network (MLP)
print("=== 1. Feedforward Neural Network ===")
class MLPPyTorch(nn.Module):
def __init__(self, input_size, hidden_sizes, output_size):
super().__init__()
layers = []
prev_size = input_size
for hidden_size in hidden_sizes:
layers.append(nn.Linear(prev_size, hidden_size))
layers.append(nn.BatchNorm1d(hidden_size))
layers.append(nn.ReLU())
layers.append(nn.Dropout(0.3))
prev_size = hidden_size
layers.append(nn.Linear(prev_size, output_size))
self.model = nn.Sequential(*layers)
def forward(self, x):
return self.model(x)
mlp = MLPPyTorch(input_size=784, hidden_sizes=[512, 256, 128], output_size=10)
print(f"MLP Parameters: {sum(p.numel() for p in mlp.parameters()):,}")
# 2. Convolutional Neural Network (CNN)
print("\n=== 2. Convolutional Neural Network ===")
class CNNPyTorch(nn.Module):
def __init__(self):
super().__init__()
# Conv blocks
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(32)
self.pool1 = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(64)
self.pool2 = nn.MaxPool2d(2, 2)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.bn3 = nn.BatchNorm2d(128)
self.pool3 = nn.MaxPool2d(2, 2)
# Fully connected layers
self.fc1 = nn.Linear(128 * 4 * 4, 256)
self.dropout = nn.Dropout(0.5)
self.fc2 = nn.Linear(256, 10)
self.relu = nn.ReLU()
def forward(self, x):
x = self.relu(self.bn1(self.conv1(x)))
x = self.pool1(x)
x = self.relu(self.bn2(self.conv2(x)))
x = self.pool2(x)
x = self.relu(self.bn3(self.conv3(x)))
x = self.pool3(x)
x = x.view(x.size(0), -1)
x = self.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
cnn = CNNPyTorch()
print(f"CNN Parameters: {sum(p.numel() for p in cnn.parameters()):,}")
# 3. Recurrent Neural Network (LSTM)
print("\n=== 3. LSTM Network ===")
class LSTMPyTorch(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, output_size):
super().__init__()
self.lstm = nn.LSTM(input_size, hidden_size, num_layers,
batch_first=True, dropout=0.3)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, x):
lstm_out, (h_n, c_n) = self.lstm(x)
last_hidden = h_n[-1]
output = self.fcPrerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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neural-network-design has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for neural-network-design matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in neural-network-design — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
neural-network-design fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for neural-network-design matched our evaluation — installs cleanly and behaves as described in the markdown.
We added neural-network-design from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
neural-network-design reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for neural-network-design matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: neural-network-design is the kind of skill you can hand to a new teammate without a long onboarding doc.
neural-network-design reduced setup friction for our internal harness; good balance of opinion and flexibility.
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