Skill UI

This framework provides a comprehensive, structured approach for making critical architectural decisions. It guides users through requirements analysis, detailed trade-off evaluation, and documenting decisions using Architecture Decision Records (ADRs). It is essential when analyzing or designing complex system architectures to ensure simplicity, robustness, and maintainability.

Behavioral rules for LLMs writing or reviewing code, emphasizing caution, simplicity, surgical edits, and goal-driven execution to avoid common mistakes during implementation tasks, bug fixes, or refactors.

Collection of Go idioms and best practices for building robust, maintainable services, covering simplicity, zero values, interface returns, error handling, and concurrency when writing, reviewing, or refactoring Go code.

This skill transforms a refined research proposal or method idea into a detailed, structured, and claim-driven experiment roadmap. It helps users determine exactly which experiments to run, in what order, and what evidence is needed to support the core claims of a research paper, ensuring focus on novelty, simplicity, and genuine contribution.

This skill helps users design robust and optimal software interfaces (APIs) for a module by applying the 'Design It Twice' philosophy. It generates multiple, radically different architectural designs using parallel agents, allowing for comprehensive comparison of trade-offs (e.g., simplicity vs. flexibility, specialized vs. general-purpose). Use this when you need to explore various structural options or compare module shapes before committing to a final design.

This tool enforces four rigorous coding principles, inspired by Andrej Karpathy's insights on LLM pitfalls. It guides developers to surface assumptions, prioritize simplicity, make surgical changes, and define verifiable goals. Use it when reviewing code, checking diffs, or before committing to ensure high code quality and prevent over-engineering.

This skill operates on two layers: the THINK layer enforces rigorous coding discipline (simplicity, surgical changes, goal-driven execution), and the SPEAK layer compresses prose output (caveman/classical modes) for maximum token efficiency. Use it when brevity is explicitly requested or when disciplined, high-precision code generation is required.