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Prisma ORM Best Practices Guide

prisma-patterns

A comprehensive guide covering advanced patterns, common pitfalls, and optimization techniques for using Prisma ORM in TypeScript backend applications. Learn best practices for schema design, handling transactions, optimizing queries (include vs select), and managing connection pools in production environments.

Product Capability Translation Placeholder

product-capability

This skill file acts as a Japanese translation placeholder for the 'product-capability' module. It indicates that the localized content is currently under development and not functional. Users should refer to the original English skill file for the core functionality and detailed implementation.

Product Lens Translation Placeholder

This file serves as a Japanese translation placeholder for the 'product-lens' skill. It indicates that the localization process is currently in progress. Users should refer to the original source file for the complete English version and functionality.

Production Audit Tool

production-audit

This skill is designed for conducting comprehensive production audits. It helps users systematically check processes, identify quality gaps, and ensure compliance standards are met in a production environment. (Note: The provided content is a translation placeholder.)

Production Scheduling Localization Status

production-scheduling

This file is currently undergoing Japanese localization for the production scheduling functionality. It serves as a placeholder for the Japanese translation and refers to the original English skill file for the core logic and details.

Project Flow Operations Localization Status

project-flow-ops

This file serves as a placeholder for the Japanese translation (localization) of the 'project-flow-ops' module. It indicates that the translation is currently in progress. Users should refer to the original English skill file for the core functionality and detailed usage instructions.

Full-Stack Project Architecture Guidelines

project-guidelines-example

A comprehensive guide detailing the architectural standards, technology stack, file structure, and coding patterns for building modern, AI-driven full-stack applications. It covers best practices for Next.js, FastAPI, database interaction (Supabase), and structured AI integration (Claude), ensuring consistency and scalability across the development lifecycle.

Prompt Optimizer Tool

prompt-optimizer

This tool is designed to help optimize and refine prompts used with Large Language Models (LLMs). By structuring inputs, it ensures that the instructions given to AI systems are clear, comprehensive, and contextually rich, resulting in significantly higher quality and relevance of the generated output. Useful for developers, writers, and researchers.

Biomedical Literature Search Using PubMed API

pubmed-database

This guide provides a comprehensive workflow for searching and retrieving biomedical literature from PubMed and NCBI. It covers advanced query construction using MeSH terms and specific field tags, advanced filtering (date, publication type), and a repeatable programmatic workflow using NCBI E-utilities (esearch, efetch). Essential for researchers conducting systematic reviews and literature monitoring.

Idiomatic Python Development Patterns

python-patterns

A comprehensive guide to writing robust, efficient, and maintainable Python applications. Covers core principles like readability, explicit coding, and EAFP (Easier to Ask Forgiveness than Permission), advanced features like modern type hinting, context managers, and sophisticated exception handling techniques.

Advanced Python Testing with Pytest

Master professional Python testing practices using the pytest framework. This guide covers Test-Driven Development (TDD) methodology, advanced features like fixtures (with different scopes), parametrization, assertion techniques, and achieving high code coverage, ensuring robust and maintainable software.

PyTorch Deep Learning Patterns Guide

pytorch-patterns

This skill provides guidance on implementing common architectural patterns and best practices within the PyTorch framework. It is designed for developers building sophisticated deep learning models, helping to improve structure, efficiency, and code maintainability when working with neural networks.

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