byteforge-claude-skills

Byteforge Claude Skills

A collection of Claude Code skills by @jmazzahacks that codify best practices and reusable patterns for software development.

Available Skills

postgres-setup

A skill that provides a standardized pattern for setting up PostgreSQL databases with proper separation of schema and setup logic.

What it creates:

• database/schema.sql - SQL schema definitions
• dev_scripts/setup_database.py - Python setup script with test DB support
• Project-specific environment variable naming
• Idempotent operations (safe to run multiple times)

Features:

• ✅ Parameterized project naming (not hardcoded)
• ✅ Unix timestamp convention for dates (BIGINT, not TIMESTAMP)
• ✅ UUID primary keys with gen_random_uuid()
• ✅ Test database support (--test-db flag)
• ✅ Proper permissions and user creation
• ✅ Connection pooling pattern with ThreadedConnectionPool
• ✅ RealDictCursor for easy dict conversion

Design Principles:

1. Unix Timestamps Only - Use BIGINT for all date/time storage
2. UUID Primary Keys - Better for distributed systems
3. Idempotent Operations - CREATE TABLE IF NOT EXISTS, safe to re-run
4. Separation of Concerns - Schema in SQL, setup logic in Python
5. Test Database Support - Easy isolated testing with --test-db
6. No Hardcoded Credentials - Everything via environment variables

View full postgres-setup documentation →

python-lib-setup

A skill that provides a modern, standardized pattern for setting up Python libraries for PyPI publishing or private GitHub distribution.

What it creates:

• pyproject.toml - Modern Python project configuration with hatchling
• src/{package_name}/ - Source layout with explicit package discovery
• .gitignore - Comprehensive Python artifact exclusions
• dev-requirements.txt - Development dependencies (includes build/twine for PyPI only)
• build-publish.sh - Automated build and publish script (PyPI only)
• README.md - Basic project documentation

Features:

• ✅ Modern pyproject.toml-based configuration (PEP 517/518)
• ✅ Src layout pattern for better code organization
• ✅ Explicit package discovery with hatchling
• ✅ Comprehensive .gitignore for Python projects
• ✅ PyPI or GitHub distribution paths
• ✅ Automated build-publish script with venv activation (PyPI path)
• ✅ git+https:// install instructions with CR_PAT patterns (GitHub path)
• ✅ Parameterized project setup (interactive questions)
• ✅ License selection (Proprietary, MIT, O'Saasy)
• ✅ Proper PyPI classifiers and metadata

Design Principles:

1. Modern Standards - Follows PEP 517/518, no setup.py needed
2. Src Layout - Clean separation of source code in src/ directory
3. Explicit Configuration - Explicit package discovery, no magic
4. Virtual Environment Convention - Uses bin/ at project root
5. Comprehensive .gitignore - Covers all common Python artifacts
6. Interactive Setup - Asks user questions before generating files
7. Flexible Distribution - Supports PyPI publishing and private GitHub libraries

View full python-lib-setup documentation →

flask-smorest-api

A skill that provides a production-ready pattern for building Flask REST APIs with automatic OpenAPI/Swagger documentation.

What it creates:

• Main application file with Flask app factory pattern and dotenv support
• Blueprint architecture for modular endpoint organization
• Marshmallow schema files for validation and documentation
• Singleton manager for shared service instances
• example.env with all required environment variables
• CORS support and error handling
• requirements.txt with all dependencies (unpinned)

Features:

• ✅ Automatic OpenAPI/Swagger documentation via flask-smorest
• ✅ Blueprint architecture for modularity
• ✅ MethodView classes for clean HTTP method handling
• ✅ Type-safe request/response with Marshmallow schemas
• ✅ Singleton manager pattern for database/service initialization
• ✅ CORS support for frontend integration
• ✅ Application factory pattern for testing
• ✅ Gunicorn production server support
• ✅ python-dotenv for environment variable management
• ✅ example.env pattern for configuration

Design Principles:

1. Blueprint Organization - One blueprint per feature/resource
2. Schema-Driven - Marshmallow schemas for validation and docs
3. Singleton Manager - Centralized service initialization
4. Application Factory - create_app() pattern for flexibility
5. OpenAPI/Swagger - Automatic documentation generation
6. Environment-Based Config - All secrets via environment variables with example.env
7. Production Ready - Gunicorn support out of the box

View full flask-smorest-api documentation →

flask-docker-deployment