Skill UI

Master the Zod library for defining robust, type-safe schemas in TypeScript. This expert covers advanced validation techniques, including primitives, objects, unions, and discriminated unions. It specializes in type inference (`z.infer`), data coercion, complex refinements, and seamless integration with modern frameworks like React Hook Form, Next.js, and tRPC, ensuring consistency across your application state and API inputs.

This skill transforms vague or fuzzy research directions into highly concrete, elegant, and academically rigorous proposals. It operates through multi-phase, iterative refinement, anchoring the process to the core problem while ensuring the proposed method is minimal, cutting-edge, and ready for publication or implementation.

This comprehensive pipeline guides users from a vague research concept to a fully structured, testable academic proposal. It first refines the core methodology (research-refine) to establish a stable thesis, and then generates a detailed, claim-driven experiment roadmap (experiment-plan). This end-to-end workflow ensures that the final academic output is both elegant and empirically validated.

The Santa Method is a multi-agent adversarial verification framework designed to improve output quality by requiring two independent reviewers (Agent B and Agent C) to pass the content before publication. It eliminates single-agent bias and systematically improves outputs through iterative refinement loops until convergence or manual escalation.

This meta-skill set provides a structured framework covering the entire software development lifecycle (SDLC). It guides developers through every phase, from initial concept refinement and requirement specification to incremental implementation, testing, code review, and deployment. It enforces critical professional behaviors like surfacing assumptions, managing ambiguity, and maintaining scope discipline to ensure high-quality, robust engineering outcomes.

The Santa Method is a multi-agent adversarial verification framework designed to improve output quality by requiring two independent reviewers (Agent B and Agent C) to pass the content before publication. It eliminates single-agent bias and systematically improves outputs through iterative refinement loops until convergence or manual escalation.

This skill executes the final two phases of the SPARC methodology: Refinement and Completion. It systematically reviews the implemented feature through rigorous code reviews, expands test coverage (unit, integration, edge cases), validates performance, generates a traceability matrix, creates comprehensive documentation, and confirms deployment readiness. It ensures the feature is robust, tested, and production-ready.

This skill guides authors on optimizing manuscripts for the ACM SIGPLAN International Conference on Functional Programming (ICFP). It helps assess the technical depth, scope fit, and required evidence (e.g., type systems, semantics, compilers) needed to successfully position a functional programming paper for this top-tier venue. Use it for pre-submission refinement, framing, and strategic planning.

This tool acts as a comprehensive router, guiding users through the specific stages of writing a short-format manuscript for American Economic Review: Insights (AER: Insights). It helps structure the process from initial topic selection and identifying a single core insight, through literature positioning, model refinement, and final submission/rebuttal preparation, ensuring compliance with strict length and scope requirements.

This skill provides a comprehensive fit assessment and checklist for clinical research targeting The American Journal of Gastroenterology. It guides authors on required evidence levels, reporting standards (e.g., CONSORT, STROBE), structuring the manuscript, and ensuring the study has clear, practice-relevant clinical implications for GI/Hepatology practitioners. It serves as a crucial pre-submission refinement aid.

A specialized tool for polishing pure mathematics manuscripts, designed to enhance the rigor and overall flow of academic writing. It guides authors to eliminate soft language (e.g., "clearly," "obviously"), ensure precise quantification, explicitly state constant dependencies, and structure hypotheses meticulously. Ideal for late-stage proof refinement before journal submission.

This guide serves as a comprehensive router for authors preparing an invited review article for the Annual Review of Psychology (ARPsych). It outlines the systematic, multi-stage process, guiding authors through topic selection, literature synthesis, framework development, writing style refinement, and final submission/revision, ensuring the final piece is authoritative and comprehensive.