Photo Toolkit for Openclaw

To run this skill, configure your virtual environment and install the required dependencies as outlined below. Ensure you check for the system dependency libraw first.

1. Install System Dependency

Depending on your operating system, install the core libraw library:

# macOS
brew install libraw

# Debian/Ubuntu
sudo apt-get install libraw-dev

# RedHat/CentOS/Fedora
sudo dnf install LibRaw-devel

2. Set Up Virtual Environment and Install Python Dependencies

Navigate to the skill directory, set up your virtual environment, and install dependencies listed in requirements.txt:

# Create and activate virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install python packages
pip install -r photo-toolkit/requirements.txt

# (Optional) Install HEIC support
pip install pillow-heif

3. Verify the Installation

# Verify core dependencies
python3 -c "import rawpy; from PIL import Image; import numpy; print('✓ Core dependencies installed')"

# Verify HEIC/HEIF support
python3 -c "from pillow_heif import register_heif_opener; print('✓ HEIC/HEIF support available')" 2>/dev/null || echo "HEIC/HEIF support not installed"

4. Configuration

Copy config.example.toml to config.toml in your workspace directory to define the default input/output pathways.

The Photo Toolkit structures output directories, configuration, and logs for seamless pipelines. It works with standard JSON formats for piped metadata.

File Output Structures

Input Metadata Structure (JSON Pipe)

When passing file listings through standard input between scripts (e.g., piping find_by_date.py to convert.py), the data uses a standard JSON array structure containing target file paths:

[
  "/path/to/photo1.nef",
  "/path/to/photo2.arw",
  "/path/to/photo3.heic"
]