Skill UI

This skill manages the secure transition of private internal projects to public open-source releases. It executes a three-stage pipeline: Forking (removing secrets), Sanitizing (verifying clean code), and Packaging (generating required documentation like README, setup.sh, and CLAUDE.md). It ensures code safety and readiness before public deployment.

This tool orchestrates the end-to-end process of building a brand-new feature. It automates the entire lifecycle, including initial research, planning, Test-Driven Development (TDD) implementation, code review, and gated commit. Use this when adding a capability that does not currently exist in the system.

This tool orchestrates the complete workflow for addressing defects, ensuring a robust fix process. It guides the user through reproducing a bug as a failing regression test, applying the necessary fix until the test passes (goes green), and managing the subsequent code review and gated commit phases. Ideal for systems where existing functionality is broken, incorrect, or exhibiting regression.

This skill orchestrates complex, behavior-preserving code refactoring. It is designed to improve code structure—such as extracting modules, eliminating duplication, or reducing nesting—without changing the core functionality. The process follows a rigorous, multi-phase gate system, ensuring existing tests remain green throughout the entire development lifecycle, culminating in a behavior-neutral commit.

This comprehensive guide covers essential security patterns for Perl development, addressing common vulnerabilities like SQL Injection, XSS, Path Traversal, and Shell Injection. It details best practices including using Perl's Taint Mode, implementing strict allow-list validation, performing safe file operations, and executing external commands securely to ensure robust and resilient applications.

This skill is intended for Perl testing functionalities. Currently, the Japanese translation and content are under development, serving as a placeholder. Users should refer to the original English skill file for the core functionality and detailed implementation.

This tool detects and flags Personally Identifiable Information (PII) across text, code, and configuration files. It is essential to use this before committing code, writing logs, or generating model responses to prevent accidental exposure of sensitive data. It identifies various types of PII, including emails, phone numbers, SSNs, credit card numbers, API keys, and passwords, offering quick checks, detailed scans, and full context-aware analysis.

This skill is intended to analyze and improve the quality of codebases, ensuring adherence to best practices and coding standards. Note: This specific file is a translation placeholder, and the core functionality description should refer to the original skill file for details.

A comprehensive guide to PostgreSQL best practices, covering advanced indexing (GIN, BRIN, Partial), schema design, performance tuning, and security implementation (RLS). It details optimizing common SQL patterns like cursor pagination and UPSERT, essential for building high-performance, reliable database systems.

This skill reviews prediction market, basket, oracle, and trading-agent workflows to ensure compliance, safety, data quality, and privacy. It must be run before any workflow handles user portfolio data, venue authentication, API keys, or trade planning to mitigate execution and regulatory risks across multiple domains.

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.

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.