Initial release of Claude Vision Auto v1.0.0

Vision-based auto-approval system for Claude Code CLI using MiniCPM-V vision model.

Features:
- Automatic detection and response to approval prompts
- Screenshot capture and vision analysis via Ollama
- Support for multiple screenshot tools (scrot, gnome-screenshot, etc.)
- Configurable timing and behavior
- Debug mode for troubleshooting
- Comprehensive documentation

Generated with Claude Code

Co-Authored-By: Claude <noreply@anthropic.com>
Co-Authored-By: Jean-Philippe Brule <jp@svrnty.io>
This commit is contained in:
Svrnty 2025-10-29 10:09:01 -04:00
commit 41cecca0e2
18 changed files with 1916 additions and 0 deletions

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# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
# Virtual environments
venv/
ENV/
env/
# IDE
.vscode/
.idea/
*.swp
*.swo
*~
# Testing
.pytest_cache/
.coverage
htmlcov/
# Cache
.cache/
*.log
# Screenshots
screenshots/
*.png

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# Changelog
All notable changes to Claude Vision Auto will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [1.0.0] - 2025-10-29
### Added
- Initial release of Claude Vision Auto
- Vision-based auto-approval using MiniCPM-V
- Support for multiple screenshot tools (scrot, gnome-screenshot, ImageMagick, maim)
- Configurable timing and behavior via environment variables
- Debug mode for troubleshooting
- Comprehensive documentation (README, INSTALLATION, USAGE)
- MIT License
- Example usage scripts
- Basic test suite
### Features
- Automatic detection of approval prompts
- Screenshot capture of terminal window
- Vision analysis via Ollama API
- Intelligent response submission (1, y, WAIT)
- Configurable idle threshold and response delay
- Support for multiple vision models (MiniCPM-V, Llama 3.2 Vision, LLaVA)
- Automatic screenshot cleanup
- Connection testing and validation
### Supported Platforms
- Linux (Debian/Ubuntu tested)
- X11 display server
- Python 3.8+
## [Unreleased]
### Planned
- Wayland support
- macOS support
- Headless mode (API-only)
- Configurable response patterns
- Multi-terminal support
- Session recording and replay
- Windows support (WSL)

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MIT License
Copyright (c) 2025 Svrnty (Jean-Philippe Brule)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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.PHONY: install install-dev uninstall test clean help
help:
@echo "Claude Vision Auto - Makefile"
@echo ""
@echo "Available targets:"
@echo " install - Install the package"
@echo " install-dev - Install in development mode"
@echo " uninstall - Uninstall the package"
@echo " test - Run tests"
@echo " clean - Clean build artifacts"
@echo " deps - Install system dependencies (Debian/Ubuntu)"
@echo ""
install:
pip3 install -e .
install-dev:
pip3 install -e ".[dev]"
uninstall:
pip3 uninstall -y claude-vision-auto
deps:
@echo "Installing system dependencies..."
sudo apt-get update
sudo apt-get install -y scrot python3-pip
test:
python3 -m pytest tests/
clean:
rm -rf build/
rm -rf dist/
rm -rf *.egg-info
find . -type d -name __pycache__ -exec rm -rf {} +
find . -type f -name "*.pyc" -delete

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# Claude Vision Auto
Vision-based auto-approval system for Claude Code CLI using MiniCPM-V vision model.
## Overview
Claude Vision Auto automatically detects and responds to approval prompts in Claude Code by:
1. Monitoring terminal output for approval keywords
2. Taking screenshots when idle (waiting for input)
3. Analyzing screenshots with MiniCPM-V vision model via Ollama
4. Automatically submitting appropriate responses
## Features
- **Zero Pattern Matching**: Uses vision AI instead of fragile regex patterns
- **Universal Compatibility**: Works with any Claude Code prompt format
- **Intelligent Detection**: Only activates when approval keywords are present
- **Configurable**: Environment variables for all settings
- **Lightweight**: Minimal dependencies (only `requests`)
- **Debug Mode**: Verbose logging for troubleshooting
## Prerequisites
### Required
1. **Claude Code CLI** - Anthropic's official CLI tool
```bash
npm install -g @anthropic-ai/claude-code
```
2. **Ollama with MiniCPM-V** - Vision model server
```bash
docker pull ollama/ollama
docker run -d -p 11434:11434 --name ollama ollama/ollama
docker exec ollama ollama pull minicpm-v:latest
```
3. **Screenshot Tool** (one of):
- `scrot` (recommended)
- `gnome-screenshot`
- `imagemagick` (import command)
- `maim`
### Install Screenshot Tool
```bash
# Debian/Ubuntu (recommended)
sudo apt-get install scrot
# Alternative options
sudo apt-get install gnome-screenshot
sudo apt-get install imagemagick
sudo apt-get install maim
```
## Installation
### Quick Install
```bash
cd claude-vision-auto
make deps # Install system dependencies
make install # Install the package
```
### Manual Install
```bash
# Install system dependencies
sudo apt-get update
sudo apt-get install -y scrot python3-pip
# Install Python package
pip3 install -e .
```
### Verify Installation
```bash
# Check if command is available
which claude-vision
# Test Ollama connection
curl http://localhost:11434/api/tags
```
## Usage
### Basic Usage
Replace `claude` with `claude-vision`:
```bash
# Instead of:
claude
# Use:
claude-vision
```
### With Prompts
```bash
# Pass prompts directly
claude-vision "create a test file in /tmp"
# Interactive session
claude-vision
```
### Configuration
Set environment variables to customize behavior:
```bash
# Ollama URL (default: http://localhost:11434/api/generate)
export OLLAMA_URL="http://custom-host:11434/api/generate"
# Vision model (default: minicpm-v:latest)
export VISION_MODEL="llama3.2-vision:latest"
# Idle threshold in seconds (default: 3.0)
export IDLE_THRESHOLD="5.0"
# Response delay in seconds (default: 1.0)
export RESPONSE_DELAY="2.0"
# Enable debug mode
export DEBUG="true"
# Run with custom settings
claude-vision
```
## How It Works
1. **Launch**: Spawns `claude` as subprocess
2. **Monitor**: Watches output for approval keywords (Yes, No, Approve, etc.)
3. **Detect Idle**: When output stops for `IDLE_THRESHOLD` seconds
4. **Screenshot**: Captures terminal window with `scrot`
5. **Analyze**: Sends to MiniCPM-V via Ollama API
6. **Respond**: Vision model returns "1", "y", or "WAIT"
7. **Submit**: Automatically sends response if not "WAIT"
## Configuration Options
| Variable | Default | Description |
|----------|---------|-------------|
| `OLLAMA_URL` | `http://localhost:11434/api/generate` | Ollama API endpoint |
| `VISION_MODEL` | `minicpm-v:latest` | Vision model to use |
| `IDLE_THRESHOLD` | `3.0` | Seconds of idle before screenshot |
| `RESPONSE_DELAY` | `1.0` | Seconds to wait before responding |
| `OUTPUT_BUFFER_SIZE` | `4096` | Bytes of output to buffer |
| `SCREENSHOT_TIMEOUT` | `5` | Screenshot capture timeout |
| `VISION_TIMEOUT` | `30` | Vision analysis timeout |
| `DEBUG` | `false` | Enable verbose logging |
## Troubleshooting
### Ollama Not Connected
```bash
# Check if Ollama is running
docker ps | grep ollama
# Check if model is available
curl http://localhost:11434/api/tags
```
### Screenshot Fails
```bash
# Test screenshot tool
scrot /tmp/test.png
ls -lh /tmp/test.png
# Install if missing
sudo apt-get install scrot
```
### Debug Mode
```bash
# Run with verbose logging
DEBUG=true claude-vision "test command"
```
### Vision Model Not Found
```bash
# Pull the model
docker exec ollama ollama pull minicpm-v:latest
# Or use alternative vision model
export VISION_MODEL="llava:latest"
claude-vision
```
## Development
### Setup Development Environment
```bash
# Clone repository
git clone https://git.openharbor.io/svrnty/claude-vision-auto.git
cd claude-vision-auto
# Install in development mode
pip3 install -e .
# Run tests
make test
```
### Project Structure
```
claude-vision-auto/
├── README.md # This file
├── LICENSE # MIT License
├── setup.py # Package setup
├── requirements.txt # Python dependencies
├── Makefile # Build automation
├── .gitignore # Git ignore rules
├── claude_vision_auto/ # Main package
│ ├── __init__.py # Package initialization
│ ├── main.py # CLI entry point
│ ├── config.py # Configuration
│ ├── screenshot.py # Screenshot capture
│ └── vision_analyzer.py # Vision analysis
├── bin/
│ └── claude-vision # CLI wrapper script
├── tests/ # Test suite
│ └── test_vision.py
├── docs/ # Documentation
│ ├── INSTALLATION.md
│ └── USAGE.md
└── examples/ # Usage examples
└── example_usage.sh
```
## Supported Vision Models
Tested and working:
- **minicpm-v:latest** (Recommended) - Best for structured output
- **llama3.2-vision:latest** - Good alternative
- **llava:latest** - Fallback option
## Performance
- **Startup**: < 1 second
- **Screenshot**: ~100ms
- **Vision Analysis**: 2-5 seconds (depends on model)
- **Total Response Time**: 3-7 seconds per approval
## Limitations
- **X11 Only**: Requires X11 display server (no Wayland support for scrot)
- **Linux Only**: Currently only tested on Debian/Ubuntu
- **Vision Dependency**: Requires Ollama and vision model
- **Screen Required**: Must have GUI session (no headless support)
## Future Enhancements
- [ ] Wayland support (alternative screenshot tools)
- [ ] macOS support
- [ ] Headless mode (API-only, no screenshots)
- [ ] Configurable response patterns
- [ ] Multi-terminal support
- [ ] Session recording and replay
## License
MIT License - See LICENSE file
## Author
**Svrnty**
- Email: jp@svrnty.io
- Repository: https://git.openharbor.io/svrnty/claude-vision-auto
## Contributing
Contributions welcome! Please:
1. Fork the repository
2. Create a feature branch
3. Submit a pull request
## Acknowledgments
- **Anthropic** - Claude Code CLI
- **MiniCPM-V** - Vision model
- **Ollama** - Model serving infrastructure

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#!/usr/bin/env python3
"""
Claude Vision Auto - CLI wrapper
"""
import sys
from claude_vision_auto.main import main
if __name__ == '__main__':
main()

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"""
Claude Vision Auto - Vision-based auto-approval for Claude Code
Automatically analyzes terminal screenshots using MiniCPM-V vision model
to respond to Claude Code approval prompts.
"""
__version__ = "1.0.0"
__author__ = "Svrnty"
__license__ = "MIT"
from .main import run_claude_with_vision
__all__ = ["run_claude_with_vision"]

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"""
Configuration for Claude Vision Auto
"""
import os
from pathlib import Path
# Ollama configuration
OLLAMA_URL = os.getenv("OLLAMA_URL", "http://localhost:11434/api/generate")
VISION_MODEL = os.getenv("VISION_MODEL", "minicpm-v:latest")
# Timing configuration
IDLE_THRESHOLD = float(os.getenv("IDLE_THRESHOLD", "3.0")) # seconds of no output before screenshot
RESPONSE_DELAY = float(os.getenv("RESPONSE_DELAY", "1.0")) # seconds to wait before sending response
# Buffer configuration
OUTPUT_BUFFER_SIZE = int(os.getenv("OUTPUT_BUFFER_SIZE", "4096")) # bytes
# Keywords that suggest we're waiting for approval
APPROVAL_KEYWORDS = [
"Yes",
"No",
"(y/n)",
"[y/n]",
"Approve",
"Do you want to",
"create",
"edit",
"delete"
]
# Screenshot configuration
SCREENSHOT_TIMEOUT = int(os.getenv("SCREENSHOT_TIMEOUT", "5")) # seconds
SCREENSHOT_TOOLS = ["scrot", "gnome-screenshot", "import", "maim"]
# Vision analysis timeout
VISION_TIMEOUT = int(os.getenv("VISION_TIMEOUT", "30")) # seconds
# Debug mode
DEBUG = os.getenv("DEBUG", "false").lower() in ("true", "1", "yes")
def get_cache_dir() -> Path:
"""Get cache directory for screenshots"""
cache_dir = Path.home() / ".cache" / "claude-vision-auto"
cache_dir.mkdir(parents=True, exist_ok=True)
return cache_dir

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"""
Main entry point for Claude Vision Auto
"""
import sys
import time
import select
import subprocess
from pathlib import Path
from . import config
from .screenshot import take_screenshot, cleanup_old_screenshots
from .vision_analyzer import VisionAnalyzer
def run_claude_with_vision(args: list = None):
"""
Run Claude Code with vision-based auto-approval
Args:
args: Command line arguments to pass to claude
"""
args = args or []
# Initialize vision analyzer
analyzer = VisionAnalyzer()
# Test connection
print("[Claude Vision Auto] Testing Ollama connection...")
if not analyzer.test_connection():
print("[ERROR] Cannot connect to Ollama or model not available")
print(f"Make sure Ollama is running and '{config.VISION_MODEL}' is installed")
sys.exit(1)
print(f"[Claude Vision Auto] Connected to Ollama")
print(f"[Claude Vision Auto] Using model: {config.VISION_MODEL}")
print(f"[Claude Vision Auto] Idle threshold: {config.IDLE_THRESHOLD}s")
print()
# Build command
cmd = ['claude'] + args
# Start Claude Code process
try:
process = subprocess.Popen(
cmd,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
bufsize=0
)
except FileNotFoundError:
print("[ERROR] 'claude' command not found")
print("Make sure Claude Code CLI is installed")
sys.exit(1)
last_output_time = time.time()
output_buffer = bytearray()
# Cleanup old screenshots
cleanup_old_screenshots()
try:
while True:
# Check if there's data to read
readable, _, _ = select.select([process.stdout], [], [], 0.1)
if readable:
char = process.stdout.read(1)
if not char:
# Process ended
break
# Print to terminal
sys.stdout.buffer.write(char)
sys.stdout.buffer.flush()
output_buffer.extend(char)
last_output_time = time.time()
# Keep buffer reasonable size
if len(output_buffer) > config.OUTPUT_BUFFER_SIZE:
output_buffer = output_buffer[-config.OUTPUT_BUFFER_SIZE:]
# Check if idle (no output for threshold seconds)
idle_time = time.time() - last_output_time
if idle_time >= config.IDLE_THRESHOLD:
# Check if buffer suggests we're waiting for input
buffer_str = output_buffer.decode('utf-8', errors='ignore')
# Look for approval keywords
has_keywords = any(
keyword in buffer_str
for keyword in config.APPROVAL_KEYWORDS
)
if has_keywords:
if config.DEBUG:
print("\n[DEBUG] Approval keywords detected in buffer")
print("\n[Vision] Analyzing prompt...", file=sys.stderr)
# Take screenshot
screenshot_path = take_screenshot()
if screenshot_path:
# Analyze with vision
response = analyzer.analyze_screenshot(screenshot_path)
if response:
print(f"[Vision] Response: {response}", file=sys.stderr)
if response and response.upper() != "WAIT":
# Send response
time.sleep(config.RESPONSE_DELAY)
process.stdin.write(f"{response}\n".encode('utf-8'))
process.stdin.flush()
# Clear buffer
output_buffer.clear()
last_output_time = time.time()
print("[Vision] Response sent", file=sys.stderr)
else:
print("[Vision] No action needed (WAIT)", file=sys.stderr)
else:
print("[Vision] Analysis failed, waiting for manual input", file=sys.stderr)
# Clean up screenshot
try:
Path(screenshot_path).unlink()
except Exception:
pass
else:
print("[Vision] Screenshot failed, waiting for manual input", file=sys.stderr)
# Reset idle detection
last_output_time = time.time()
# Check if process is still running
if process.poll() is not None:
break
except KeyboardInterrupt:
print("\n[Claude Vision Auto] Interrupted by user")
process.terminate()
process.wait()
sys.exit(130)
finally:
# Wait for process to finish
exit_code = process.wait()
sys.exit(exit_code)
def main():
"""CLI entry point"""
args = sys.argv[1:]
run_claude_with_vision(args)
if __name__ == '__main__':
main()

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"""
Screenshot capture functionality
"""
import subprocess
import tempfile
import shutil
from pathlib import Path
from typing import Optional
from . import config
class ScreenshotError(Exception):
"""Raised when screenshot capture fails"""
pass
def find_screenshot_tool() -> Optional[str]:
"""Find available screenshot tool on the system"""
for tool in config.SCREENSHOT_TOOLS:
if shutil.which(tool):
return tool
return None
def take_screenshot() -> Optional[str]:
"""
Take screenshot of active terminal window
Returns:
Path to screenshot file, or None if capture failed
"""
tool = find_screenshot_tool()
if not tool:
if config.DEBUG:
print(f"[DEBUG] No screenshot tool found. Tried: {', '.join(config.SCREENSHOT_TOOLS)}")
return None
# Create temporary file
cache_dir = config.get_cache_dir()
screenshot_path = cache_dir / f"screenshot_{int(subprocess.check_output(['date', '+%s']).decode().strip())}.png"
try:
if tool == "scrot":
# Capture active window
subprocess.run(
["scrot", "-u", str(screenshot_path)],
check=True,
capture_output=True,
timeout=config.SCREENSHOT_TIMEOUT
)
elif tool == "gnome-screenshot":
# Capture active window
subprocess.run(
["gnome-screenshot", "-w", "-f", str(screenshot_path)],
check=True,
capture_output=True,
timeout=config.SCREENSHOT_TIMEOUT
)
elif tool == "import":
# ImageMagick - capture root window
subprocess.run(
["import", "-window", "root", str(screenshot_path)],
check=True,
capture_output=True,
timeout=config.SCREENSHOT_TIMEOUT
)
elif tool == "maim":
# Capture active window
subprocess.run(
["maim", "-i", "$(xdotool getactivewindow)", str(screenshot_path)],
shell=True,
check=True,
capture_output=True,
timeout=config.SCREENSHOT_TIMEOUT
)
else:
return None
if screenshot_path.exists():
if config.DEBUG:
print(f"[DEBUG] Screenshot saved to {screenshot_path}")
return str(screenshot_path)
else:
return None
except subprocess.TimeoutExpired:
if config.DEBUG:
print(f"[DEBUG] Screenshot timeout with tool: {tool}")
return None
except subprocess.CalledProcessError as e:
if config.DEBUG:
print(f"[DEBUG] Screenshot failed: {e}")
return None
except Exception as e:
if config.DEBUG:
print(f"[DEBUG] Unexpected error: {e}")
return None
def cleanup_old_screenshots(max_age_seconds: int = 3600):
"""
Clean up old screenshots from cache directory
Args:
max_age_seconds: Maximum age of screenshots to keep (default 1 hour)
"""
import time
cache_dir = config.get_cache_dir()
current_time = time.time()
for screenshot in cache_dir.glob("screenshot_*.png"):
if current_time - screenshot.stat().st_mtime > max_age_seconds:
try:
screenshot.unlink()
if config.DEBUG:
print(f"[DEBUG] Cleaned up old screenshot: {screenshot}")
except Exception as e:
if config.DEBUG:
print(f"[DEBUG] Failed to cleanup {screenshot}: {e}")

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"""
Vision analysis using MiniCPM-V via Ollama
"""
import base64
import requests
from pathlib import Path
from typing import Optional
from . import config
VISION_PROMPT = """You are analyzing a terminal screenshot showing a Claude Code approval prompt.
Look for:
- Menu options like "1. Yes", "2. Yes, allow all", "3. No"
- Questions asking for approval (create/edit/delete files)
- Yes/No questions
If you see an approval prompt with numbered options:
- Respond ONLY with the number to select "Yes" (usually "1")
- Output format: Just the number, nothing else
If you see a yes/no question:
- Respond with: y
If you don't see any prompt requiring input:
- Respond with: WAIT
Your response (one word only):"""
class VisionAnalyzer:
"""Analyzes screenshots using vision model"""
def __init__(self, ollama_url: str = None, model: str = None):
"""
Initialize vision analyzer
Args:
ollama_url: Ollama API URL (default from config)
model: Vision model name (default from config)
"""
self.ollama_url = ollama_url or config.OLLAMA_URL
self.model = model or config.VISION_MODEL
def analyze_screenshot(self, image_path: str) -> Optional[str]:
"""
Analyze screenshot and determine what response to give
Args:
image_path: Path to screenshot image
Returns:
Response to send ("1", "y", "WAIT", etc.) or None on error
"""
try:
# Read and encode image
with open(image_path, 'rb') as f:
image_data = base64.b64encode(f.read()).decode('utf-8')
# Send to Ollama
payload = {
"model": self.model,
"prompt": VISION_PROMPT,
"images": [image_data],
"stream": False
}
if config.DEBUG:
print(f"[DEBUG] Sending to Ollama: {self.ollama_url}")
print(f"[DEBUG] Model: {self.model}")
response = requests.post(
self.ollama_url,
json=payload,
timeout=config.VISION_TIMEOUT
)
response.raise_for_status()
result = response.json()
answer = result.get('response', '').strip()
if config.DEBUG:
print(f"[DEBUG] Vision model response: {answer}")
return answer
except requests.Timeout:
if config.DEBUG:
print("[DEBUG] Vision analysis timeout")
return None
except requests.RequestException as e:
if config.DEBUG:
print(f"[DEBUG] Vision API error: {e}")
return None
except Exception as e:
if config.DEBUG:
print(f"[DEBUG] Unexpected error in vision analysis: {e}")
return None
def test_connection(self) -> bool:
"""
Test if Ollama is accessible and model is available
Returns:
True if connection successful, False otherwise
"""
try:
# Try to list tags
tags_url = self.ollama_url.replace('/api/generate', '/api/tags')
response = requests.get(tags_url, timeout=5)
response.raise_for_status()
data = response.json()
models = [m['name'] for m in data.get('models', [])]
if config.DEBUG:
print(f"[DEBUG] Available models: {models}")
# Check if our model is available
model_available = any(self.model in m for m in models)
if not model_available:
print(f"Warning: Model '{self.model}' not found in Ollama")
print(f"Available models: {', '.join(models)}")
return model_available
except Exception as e:
if config.DEBUG:
print(f"[DEBUG] Connection test failed: {e}")
return False

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# Installation Guide
Detailed installation instructions for Claude Vision Auto.
## Table of Contents
1. [Prerequisites](#prerequisites)
2. [System Dependencies](#system-dependencies)
3. [Ollama Setup](#ollama-setup)
4. [Package Installation](#package-installation)
5. [Verification](#verification)
6. [Troubleshooting](#troubleshooting)
## Prerequisites
### 1. Claude Code CLI
Install Anthropic's official CLI:
```bash
npm install -g @anthropic-ai/claude-code
```
Verify installation:
```bash
claude --version
```
### 2. Python 3.8+
Check Python version:
```bash
python3 --version
```
If not installed:
```bash
sudo apt-get update
sudo apt-get install python3 python3-pip
```
### 3. Docker (for Ollama)
Install Docker:
```bash
curl -fsSL https://get.docker.com | sh
sudo usermod -aG docker $USER
```
Log out and back in for group changes to take effect.
## System Dependencies
### Screenshot Tool
Install `scrot` (recommended):
```bash
sudo apt-get update
sudo apt-get install -y scrot
```
Alternative screenshot tools:
```bash
# GNOME Screenshot
sudo apt-get install -y gnome-screenshot
# ImageMagick
sudo apt-get install -y imagemagick
# Maim
sudo apt-get install -y maim xdotool
```
### Additional Dependencies
```bash
sudo apt-get install -y \
python3-pip \
git \
curl
```
## Ollama Setup
### 1. Pull Ollama Docker Image
```bash
docker pull ollama/ollama:latest
```
### 2. Start Ollama Container
```bash
docker run -d \
-p 11434:11434 \
--name ollama \
--restart unless-stopped \
ollama/ollama:latest
```
For GPU support (NVIDIA):
```bash
docker run -d \
-p 11434:11434 \
--name ollama \
--gpus all \
--restart unless-stopped \
ollama/ollama:latest
```
### 3. Pull Vision Model
```bash
# MiniCPM-V (recommended - 5.5GB)
docker exec ollama ollama pull minicpm-v:latest
# Alternative: Llama 3.2 Vision (7.8GB)
docker exec ollama ollama pull llama3.2-vision:latest
# Alternative: LLaVA (4.5GB)
docker exec ollama ollama pull llava:latest
```
### 4. Verify Ollama
```bash
# Check container status
docker ps | grep ollama
# Test API
curl http://localhost:11434/api/tags
# List installed models
curl -s http://localhost:11434/api/tags | python3 -m json.tool
```
## Package Installation
### Method 1: Using Makefile (Recommended)
```bash
cd claude-vision-auto
# Install system dependencies
make deps
# Install package
make install
```
### Method 2: Manual Installation
```bash
cd claude-vision-auto
# Install system dependencies
sudo apt-get update
sudo apt-get install -y scrot python3-pip
# Install Python package
pip3 install -e .
```
### Method 3: From Git
```bash
# Clone repository
git clone https://git.openharbor.io/svrnty/claude-vision-auto.git
cd claude-vision-auto
# Install
pip3 install -e .
```
## Verification
### 1. Check Command Installation
```bash
which claude-vision
```
Expected output: `/home/username/.local/bin/claude-vision`
### 2. Test Ollama Connection
```bash
curl http://localhost:11434/api/tags
```
Should return JSON with list of models.
### 3. Test Screenshot
```bash
scrot /tmp/test_screenshot.png
ls -lh /tmp/test_screenshot.png
```
Should create a screenshot file.
### 4. Run Test
```bash
# Start claude-vision
claude-vision
# You should see:
# [Claude Vision Auto] Testing Ollama connection...
# [Claude Vision Auto] Connected to Ollama
# [Claude Vision Auto] Using model: minicpm-v:latest
```
## Troubleshooting
### "claude-vision: command not found"
Add to PATH in `~/.bashrc` or `~/.zshrc`:
```bash
export PATH="$HOME/.local/bin:$PATH"
```
Then reload:
```bash
source ~/.bashrc # or source ~/.zshrc
```
### "Cannot connect to Ollama"
Check if Ollama container is running:
```bash
docker ps | grep ollama
# If not running, start it:
docker start ollama
```
Check if port 11434 is open:
```bash
netstat -tulpn | grep 11434
# or
ss -tulpn | grep 11434
```
### "Model not found"
Pull the model:
```bash
docker exec ollama ollama pull minicpm-v:latest
```
List available models:
```bash
docker exec ollama ollama list
```
### "Screenshot failed"
Install scrot:
```bash
sudo apt-get install scrot
```
Test screenshot:
```bash
scrot -u /tmp/test.png
```
If error persists, try alternative tools in config:
```bash
export SCREENSHOT_TOOL="gnome-screenshot"
claude-vision
```
### Permission Issues
If pip install fails with permissions:
```bash
# Install for user only
pip3 install --user -e .
# Or use virtual environment
python3 -m venv venv
source venv/bin/activate
pip install -e .
```
### Docker Permission Denied
Add user to docker group:
```bash
sudo usermod -aG docker $USER
```
Log out and back in, then:
```bash
docker ps # Should work without sudo
```
## Uninstallation
### Remove Package
```bash
make uninstall
# or
pip3 uninstall claude-vision-auto
```
### Remove Ollama
```bash
docker stop ollama
docker rm ollama
docker rmi ollama/ollama
```
### Remove System Dependencies
```bash
sudo apt-get remove scrot
```
## Next Steps
After successful installation:
1. Read [USAGE.md](USAGE.md) for usage examples
2. Configure environment variables if needed
3. Test with a simple Claude Code command
## Getting Help
If you encounter issues not covered here:
1. Check the main [README.md](../README.md)
2. Enable debug mode: `DEBUG=true claude-vision`
3. Check logs: `~/.cache/claude-vision-auto/`
4. Report issues: https://git.openharbor.io/svrnty/claude-vision-auto/issues

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# Usage Guide
Comprehensive usage guide for Claude Vision Auto.
## Table of Contents
1. [Basic Usage](#basic-usage)
2. [Configuration](#configuration)
3. [Common Scenarios](#common-scenarios)
4. [Advanced Usage](#advanced-usage)
5. [Tips and Best Practices](#tips-and-best-practices)
## Basic Usage
### Starting Interactive Session
Simply replace `claude` with `claude-vision`:
```bash
claude-vision
```
Expected output:
```
[Claude Vision Auto] Testing Ollama connection...
[Claude Vision Auto] Connected to Ollama
[Claude Vision Auto] Using model: minicpm-v:latest
[Claude Vision Auto] Idle threshold: 3.0s
▐▛███▜▌ Claude Code v2.0.26
▝▜█████▛▘ Sonnet 4.5 · Claude Max
▘▘ ▝▝ /home/username/project
>
```
### With Initial Prompt
```bash
claude-vision "create a test.md file in /tmp"
```
### How Auto-Approval Works
When Claude asks for approval:
```
╭───────────────────────────────────────────╮
│ Create file │
│ ╭───────────────────────────────────────╮ │
│ │ /tmp/test.md │ │
│ │ │ │
│ │ # Test File │ │
│ │ │ │
│ │ This is a test. │ │
│ ╰───────────────────────────────────────╯ │
│ Do you want to create test.md? │
1. Yes │
│ 2. Yes, allow all edits │
│ 3. No │
╰───────────────────────────────────────────╯
```
Claude Vision Auto will:
1. Detect idle state (3 seconds)
2. Take screenshot
3. Analyze with vision model
4. Automatically select option 1
Output:
```
[Vision] Analyzing prompt...
[Vision] Response: 1
[Vision] Response sent
```
## Configuration
### Environment Variables
Set before running `claude-vision`:
```bash
# Example: Custom configuration
export OLLAMA_URL="http://192.168.1.100:11434/api/generate"
export VISION_MODEL="llama3.2-vision:latest"
export IDLE_THRESHOLD="5.0"
export RESPONSE_DELAY="2.0"
export DEBUG="true"
claude-vision
```
### Persistent Configuration
Add to `~/.bashrc` or `~/.zshrc`:
```bash
# Claude Vision Auto Configuration
export CLAUDE_VISION_OLLAMA_URL="http://localhost:11434/api/generate"
export CLAUDE_VISION_MODEL="minicpm-v:latest"
export CLAUDE_VISION_IDLE_THRESHOLD="3.0"
export CLAUDE_VISION_RESPONSE_DELAY="1.0"
# Alias for convenience
alias cv="claude-vision"
```
Reload:
```bash
source ~/.bashrc
```
### Configuration Options Reference
| Variable | Type | Default | Description |
|----------|------|---------|-------------|
| `OLLAMA_URL` | URL | `http://localhost:11434/api/generate` | Ollama API endpoint |
| `VISION_MODEL` | String | `minicpm-v:latest` | Vision model name |
| `IDLE_THRESHOLD` | Float | `3.0` | Seconds to wait before screenshot |
| `RESPONSE_DELAY` | Float | `1.0` | Seconds to wait before responding |
| `OUTPUT_BUFFER_SIZE` | Integer | `4096` | Buffer size in bytes |
| `SCREENSHOT_TIMEOUT` | Integer | `5` | Screenshot timeout (seconds) |
| `VISION_TIMEOUT` | Integer | `30` | Vision analysis timeout (seconds) |
| `DEBUG` | Boolean | `false` | Enable debug logging |
## Common Scenarios
### Scenario 1: File Creation
```bash
claude-vision "create a new Python script called hello.py"
```
Auto-approves file creation.
### Scenario 2: File Editing
```bash
claude-vision "add error handling to main.py"
```
Auto-approves file edits.
### Scenario 3: Multiple Operations
```bash
claude-vision "refactor the authentication module and add tests"
```
Auto-approves each operation sequentially.
### Scenario 4: Longer Wait Time
For slower systems or models:
```bash
IDLE_THRESHOLD="5.0" RESPONSE_DELAY="2.0" claude-vision
```
### Scenario 5: Different Vision Model
```bash
VISION_MODEL="llama3.2-vision:latest" claude-vision
```
### Scenario 6: Remote Ollama
```bash
OLLAMA_URL="http://192.168.1.100:11434/api/generate" claude-vision
```
### Scenario 7: Debug Mode
When troubleshooting:
```bash
DEBUG=true claude-vision "test command"
```
Output will include:
```
[DEBUG] Screenshot saved to /home/user/.cache/claude-vision-auto/screenshot_1234567890.png
[DEBUG] Sending to Ollama: http://localhost:11434/api/generate
[DEBUG] Model: minicpm-v:latest
[DEBUG] Vision model response: 1
```
## Advanced Usage
### Using Different Models
#### MiniCPM-V (Default)
Best for structured responses:
```bash
VISION_MODEL="minicpm-v:latest" claude-vision
```
#### Llama 3.2 Vision
Good alternative:
```bash
VISION_MODEL="llama3.2-vision:latest" claude-vision
```
#### LLaVA
Lightweight option:
```bash
VISION_MODEL="llava:latest" claude-vision
```
### Shell Aliases
Add to `~/.bashrc`:
```bash
# Quick aliases
alias cv="claude-vision"
alias cvd="DEBUG=true claude-vision" # Debug mode
alias cvs="IDLE_THRESHOLD=5.0 claude-vision" # Slower
# Model-specific
alias cv-mini="VISION_MODEL=minicpm-v:latest claude-vision"
alias cv-llama="VISION_MODEL=llama3.2-vision:latest claude-vision"
```
### Integration with Scripts
```bash
#!/bin/bash
# auto-refactor.sh
export DEBUG="false"
export IDLE_THRESHOLD="3.0"
claude-vision "refactor all JavaScript files to use modern ES6 syntax"
```
### Conditional Auto-Approval
Create wrapper script:
```bash
#!/bin/bash
# conditional-claude.sh
if [ "$AUTO_APPROVE" = "true" ]; then
claude-vision "$@"
else
claude "$@"
fi
```
Usage:
```bash
AUTO_APPROVE=true ./conditional-claude.sh "create files"
```
### Multiple Terminal Support
Each terminal needs separate instance:
```bash
# Terminal 1
claude-vision # Project A
# Terminal 2
claude-vision # Project B
```
## Tips and Best Practices
### 1. Adjust Idle Threshold
- **Fast system**: `IDLE_THRESHOLD=2.0`
- **Slow system**: `IDLE_THRESHOLD=5.0`
- **Remote Ollama**: `IDLE_THRESHOLD=4.0`
### 2. Model Selection
- **Accuracy**: MiniCPM-V > Llama 3.2 Vision > LLaVA
- **Speed**: LLaVA > MiniCPM-V > Llama 3.2 Vision
- **Size**: LLaVA (4.5GB) < MiniCPM-V (5.5GB) < Llama 3.2 (7.8GB)
### 3. Debug When Needed
Enable debug mode if responses are incorrect:
```bash
DEBUG=true claude-vision
```
Check vision model output to see what it's detecting.
### 4. Screenshot Quality
Ensure terminal is visible and not obscured by other windows.
### 5. Performance Optimization
For faster responses:
```bash
# Pre-warm the model
docker exec ollama ollama run minicpm-v:latest "test" < /dev/null
# Then use normally
claude-vision
```
### 6. Clean Up Old Screenshots
Screenshots are auto-cleaned after 1 hour, but manual cleanup:
```bash
rm -rf ~/.cache/claude-vision-auto/*.png
```
### 7. Check Model Status
Before starting long sessions:
```bash
# Verify Ollama is responsive
curl -s http://localhost:11434/api/tags | python3 -m json.tool
# Check model is loaded
docker exec ollama ollama ps
```
## Troubleshooting During Use
### Issue: Not Auto-Responding
**Symptoms**: Vision analyzes but doesn't respond
**Solutions**:
1. Check if "WAIT" is returned:
```bash
DEBUG=true claude-vision
# Look for: [DEBUG] Vision model response: WAIT
```
2. Increase idle threshold:
```bash
IDLE_THRESHOLD="5.0" claude-vision
```
3. Try different model:
```bash
VISION_MODEL="llama3.2-vision:latest" claude-vision
```
### Issue: Wrong Response
**Symptoms**: Selects wrong option or types wrong answer
**Solutions**:
1. Enable debug to see what vision model sees:
```bash
DEBUG=true claude-vision
```
2. Check screenshot manually:
```bash
# Don't auto-clean
ls ~/.cache/claude-vision-auto/
```
3. Adjust response delay:
```bash
RESPONSE_DELAY="2.0" claude-vision
```
### Issue: Slow Response
**Symptoms**: Takes too long to respond
**Solutions**:
1. Use faster model:
```bash
VISION_MODEL="llava:latest" claude-vision
```
2. Reduce vision timeout:
```bash
VISION_TIMEOUT="15" claude-vision
```
3. Check Ollama performance:
```bash
docker stats ollama
```
### Issue: Too Aggressive
**Symptoms**: Responds to non-approval prompts
**Solutions**:
1. Increase idle threshold:
```bash
IDLE_THRESHOLD="5.0" claude-vision
```
2. Check approval keywords in config
3. Use manual mode for sensitive operations:
```bash
claude # No auto-approval
```
## Getting Help
If issues persist:
1. Check logs with `DEBUG=true`
2. Review documentation
3. Report issues with debug output
4. Include screenshot samples
## Next Steps
- Explore [examples/](../examples/) directory
- Customize configuration for your workflow
- Create shell aliases for common tasks
- Integrate with CI/CD pipelines (if applicable)

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#!/bin/bash
# Example usage scenarios for Claude Vision Auto
echo "=== Claude Vision Auto - Example Usage ==="
echo ""
# Example 1: Basic usage
echo "Example 1: Basic Interactive Session"
echo "Command: claude-vision"
echo ""
# Example 2: With prompt
echo "Example 2: With Initial Prompt"
echo "Command: claude-vision \"create a test.md file\""
echo ""
# Example 3: Debug mode
echo "Example 3: Debug Mode"
echo "Command: DEBUG=true claude-vision"
echo ""
# Example 4: Custom timing
echo "Example 4: Custom Timing (slower system)"
echo "Command: IDLE_THRESHOLD=5.0 RESPONSE_DELAY=2.0 claude-vision"
echo ""
# Example 5: Different model
echo "Example 5: Use Llama 3.2 Vision"
echo "Command: VISION_MODEL=\"llama3.2-vision:latest\" claude-vision"
echo ""
# Example 6: Remote Ollama
echo "Example 6: Remote Ollama Server"
echo "Command: OLLAMA_URL=\"http://192.168.1.100:11434/api/generate\" claude-vision"
echo ""
# Example 7: Combined configuration
echo "Example 7: Combined Custom Configuration"
cat << 'EOF'
export OLLAMA_URL="http://localhost:11434/api/generate"
export VISION_MODEL="minicpm-v:latest"
export IDLE_THRESHOLD="4.0"
export RESPONSE_DELAY="1.5"
export DEBUG="true"
claude-vision "refactor authentication module"
EOF
echo ""
echo "=== End of Examples ==="
echo ""
echo "Run any of these commands to test Claude Vision Auto"
echo "For more information, see docs/USAGE.md"

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-r requirements.txt
pytest>=7.4.0
pytest-cov>=4.1.0
pytest-mock>=3.11.1

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@ -0,0 +1 @@
requests>=2.31.0

49
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"""
Setup script for Claude Vision Auto
"""
from setuptools import setup, find_packages
from pathlib import Path
# Read README
readme_file = Path(__file__).parent / "README.md"
long_description = readme_file.read_text() if readme_file.exists() else ""
setup(
name="claude-vision-auto",
version="1.0.0",
author="Svrnty",
author_email="jp@svrnty.io",
description="Vision-based auto-approval for Claude Code using MiniCPM-V",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://git.openharbor.io/svrnty/claude-vision-auto",
packages=find_packages(),
classifiers=[
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Operating System :: POSIX :: Linux",
"Topic :: Software Development :: Libraries :: Python Modules",
"Topic :: Terminals",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
],
python_requires=">=3.8",
install_requires=[
"requests>=2.31.0",
],
entry_points={
"console_scripts": [
"claude-vision=claude_vision_auto.main:main",
],
},
include_package_data=True,
zip_safe=False,
)

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"""
Basic tests for Claude Vision Auto
"""
import pytest
from unittest.mock import Mock, patch, MagicMock
from claude_vision_auto import config
from claude_vision_auto.vision_analyzer import VisionAnalyzer
from claude_vision_auto.screenshot import find_screenshot_tool
def test_config_defaults():
"""Test default configuration values"""
assert config.VISION_MODEL == "minicpm-v:latest"
assert config.IDLE_THRESHOLD == 3.0
assert config.RESPONSE_DELAY == 1.0
assert config.OUTPUT_BUFFER_SIZE == 4096
def test_find_screenshot_tool():
"""Test screenshot tool detection"""
tool = find_screenshot_tool()
# Should find at least one tool or return None
assert tool is None or isinstance(tool, str)
@patch('requests.get')
def test_vision_analyzer_connection(mock_get):
"""Test Ollama connection check"""
mock_response = Mock()
mock_response.json.return_value = {
'models': [
{'name': 'minicpm-v:latest'},
{'name': 'llama3.2-vision:latest'}
]
}
mock_response.raise_for_status = Mock()
mock_get.return_value = mock_response
analyzer = VisionAnalyzer()
assert analyzer.test_connection() is True
@patch('requests.post')
def test_vision_analyzer_analyze(mock_post):
"""Test vision analysis"""
mock_response = Mock()
mock_response.json.return_value = {
'response': '1'
}
mock_response.raise_for_status = Mock()
mock_post.return_value = mock_response
analyzer = VisionAnalyzer()
# Create a temporary test image
import tempfile
with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as tmp:
# Write a minimal PNG header
tmp.write(b'\x89PNG\r\n\x1a\n')
tmp_path = tmp.name
result = analyzer.analyze_screenshot(tmp_path)
assert result == '1'
# Cleanup
import os
os.unlink(tmp_path)
def test_approval_keywords():
"""Test approval keyword configuration"""
assert 'Yes' in config.APPROVAL_KEYWORDS
assert 'No' in config.APPROVAL_KEYWORDS
assert '(y/n)' in config.APPROVAL_KEYWORDS
if __name__ == '__main__':
pytest.main([__file__, '-v'])