crossfit/AGENT.md

Qwen Code Agent Configuration

This file configures the behavior and preferences for the Qwen Code agent for this specific project.

Project Context

• Name: CrossFit Scheduler
• Primary Language: Python
• Key Technologies: Python scripts, JSON, Docker
• Purpose: Automates booking CrossFit classes based on user preferences.

Agent Interaction Preferences

Code Style & Conventions

• Follow PEP 8 for Python code.
• Use clear, descriptive variable and function names.
• Prefer explicit over implicit code.
• Use docstrings for modules, classes, and functions.
• Keep functions small and focused on a single task.

Project Structure Awareness

• main.py: Entry point for the application.
• execute_book_session.py: Core logic for booking sessions.
• preferred_sessions.json: User preferences for session booking.
• src/: Directory for source code modules.
• test_book_session.sh: Shell script for testing session booking.

Preferred Tools & Commands

• Use pytest for running tests.
• Use docker compose for container orchestration.
• Use python for running scripts.
• Refer to requirements.txt for dependencies.

Communication Style

• Be concise and direct.
• Use markdown for formatting responses.
• Prioritize clarity and correctness.
• Avoid unnecessary explanations or chit-chat.

Testing & Verification

• Always run tests after making changes.
• Use pytest.ini for test configuration.
• Ensure Docker containers are properly configured before running.
• Verify JSON files are correctly formatted.

Security & Best Practices

• Never commit sensitive information like passwords or API keys.
• Use .env files for environment variables.
• Follow Docker best practices for containerization.
• Keep dependencies up to date.

Custom Instructions

• When modifying preferred_sessions.json, ensure the structure remains valid.
• When writing shell scripts, ensure they are executable and follow best practices.
• When creating new Python files, ensure they are properly formatted and tested.