computer-vision

Computer vision enables machines to understand visual information from images and videos, powering applications like autonomous driving, medical imaging, and surveillance.

aj-geddes/useful-ai-prompts|Updated Apr 8, 2026

Works with

Claude CodeCursorClineWindsurfCodex

Installation Guide

Select your AI agent

How to use computer-vision on Cursor

AI-first code editor with Composer

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

›Cursor installed and configured on your machine
›Node.js 16+ with npm — verify with node --version
›Active project directory where you want to add computer-vision

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill computer-vision

Fetches computer-vision from aj-geddes/useful-ai-prompts and configures it for Cursor.

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?

│

│ ── Universal (.agents/skills) ────────────────

│ · Cline · Codex · Goose · Windsurf

│ ●Cursor(selected)

│ · Cursor · Aider · Continue

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/computer-vision

Restart Cursor to activate computer-vision. Access via /computer-vision in your agent's command palette.

⚠

Security Notice

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.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

›View source on GitHub ›Skills CLI docs ›About Cursor ›What are agent skills?

Documentation

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

✓

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

✓

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Computer Vision Tasks

Image Classification: Categorizing images into classes

Object Detection: Locating and classifying objects in images

Semantic Segmentation: Pixel-level classification

Instance Segmentation: Detecting individual object instances

Pose Estimation: Identifying human body joints

Face Recognition: Identifying individuals in images

Python Implementation

import numpy as np import pandas as pd import matplotlib.pyplot as plt import matplotlib.patches as patches from PIL import Image, ImageDraw import torch import torch.nn as nn from torch.utils.data import DataLoader, TensorDataset from torchvision import transforms, models, datasets import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers import cv2 from sklearn.metrics import accuracy_score, confusion_matrix import seaborn as sns import warnings warnings.filterwarnings('ignore') print("=== 1. Image Classification CNN ===") # Define image classification model class ImageClassifierCNN(nn.Module): def __init__(self, num_classes=10): super().__init__() self.features = nn.Sequential( nn.Conv2d(3, 32, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.BatchNorm2d(32), nn.MaxPool2d(2, 2), nn.Conv2d(32, 64, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.BatchNorm2d(64), nn.MaxPool2d(2, 2), nn.Conv2d(64, 128, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.BatchNorm2d(128), nn.MaxPool2d(2, 2), ) self.classifier = nn.Sequential( nn.Linear(128 * 4 * 4, 256), nn.ReLU(inplace=True), nn.Dropout(0.5), nn.Linear(256, num_classes) ) def forward(self, x): x = self.features(x) x = x.view(x.size(0), -1) x = self.classifier(x) return x model = ImageClassifierCNN(num_classes=10) print(f"Model parameters: {sum(p.numel() for p in model.parameters()):,}") # 2. Object Detection setup print("\n=== 2. Object Detection Framework ===") class ObjectDetector(nn.Module): def __init__(self): super().__init__() # Backbone self.backbone = nn.Sequential( nn.Conv2d(3, 32, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2, 2), nn.Conv2d(32, 64, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2, 2), ) # Bounding box regression self.bbox_head = nn.Sequential( nn.Linear(64 * 8 * 8, 128), nn.ReLU(), nn.Linear(128, 4) # x, y, w, h ) # Class prediction self.class_head = nn.Sequential( nn.Linear(64 * 8 * 8, 128), nn.ReLU(), nn.Linear(128, 10) # 10 classes ) def forward(self, x): features = self.backbone(x) features_flat = features.view(features.size(0), -1) bboxes = self.bbox_head(features_flat) classes = self.class_head(features_flat) return bboxes, classes detector = ObjectDetector() print(f"Detector parameters: {sum(p.numel() for p in detector.parameters()):,}") # 3. Semantic Segmentation

computer-vision

Installation Guide

How to use computer-vision on Cursor

Prerequisites

Run the install command

Select Cursor when prompted

Verify installation

Security Notice

Documentation

List & Monetize Your Skill

Use Cases

User Story & Requirements Generation

Competitive Analysis

Roadmap Prioritization

Install Skill

Computer Vision

Overview

When to Use

Computer Vision Tasks

Popular Architectures

Python Implementation

`Stakeholder Communication`

`Implementation Guide`

`Best Practices`

`When to Use This`

`Learning Path`

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`Reviews`

`Discussion`