Skill UI

A comprehensive guide to building robust, maintainable, and idiomatic Perl applications using modern features (Perl 5.36+). This resource covers best practices for object-oriented programming (Moo/Moose), advanced error handling (Try::Tiny), context sensitivity, complex regex usage, and efficient module design.

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.

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.

A comprehensive guide to architectural patterns for building cloud-native, event-driven microservices using Quarkus 3.x. Covers RESTful API design with JAX-RS, implementing event messaging with Apache Camel and RabbitMQ, structured data access using Hibernate Panache, managing transactions, and advanced logging context handling (MDC). Ideal for developers building robust, scalable, and maintainable enterprise systems.

This skill provides a structured framework for designing and implementing composable recommendation, ranking, and feed pipelines. It models the industry-standard six-stage pattern: Source, Hydrator, Filter, Scorer, Selector, and SideEffect. Use it for any system that selects "the top K items for a (user, context)", such as social feeds, ad ranking, RAG rerankers, or content recommendation engines, ensuring a robust and scalable architecture.

This is an advanced, structured workflow designed to conduct deep, multi-source investigations. It synthesizes current public evidence with user-provided context to generate highly structured reports. The workflow explicitly differentiates between verifiable facts, user-supplied evidence, logical inferences, and actionable recommendations, making it ideal for complex comparative analysis or continuous monitoring tasks.

This toolset allows users to save and restore complete agent sessions, ensuring context continuity across multiple conversations. It captures the agent's entire state, including memory, configuration, task progress, and learning metrics. Use it when pausing work and needing to resume later with full context, or for checkpointing long-running tasks.

Suggests when to manually run /compact at logical workflow boundaries instead of relying on automatic compaction, helping preserve context after research, milestones, or task shifts.

This guide details the architectural and implementation patterns for building modular applications or microservices using the tinystruct Java framework. It covers core concepts like using the @Action annotation for dual routing (CLI/HTTP), managing request state with Context, and using native Builder for JSON serialization, ensuring a lightweight and structured development environment.

This skill guides users through a comprehensive, structured design process. It helps transform vague ideas or initial concepts into detailed, validated specifications by exploring project context, proposing multiple options with trade-offs, and iteratively refining architectural details. It is essential for defining clear boundaries and requirements before any code is written, ensuring a robust and well-documented development roadmap.

Provides a structured, multi-step workflow for addressing and fixing GitHub issues. This skill guides the user through the entire development lifecycle: understanding the bug, researching context, planning changes, implementing code, rigorous testing, and finally submitting a polished Pull Request using GitHub CLI commands.

This skill acts as an advanced orchestration protocol for reviewing GitHub pull requests. It is stack-agnostic, meaning it does not enforce its own rules. Instead, it dynamically discovers the project's existing standards—such as the test runner, architectural conventions, and requirement specifications—before running six specialized subagents in parallel. It provides a comprehensive, context-aware review and summary directly on GitHub.