Skill UI

A comprehensive guide for creating high-value competitor comparison and alternative pages. Designed for SEO and sales enablement, this skill covers multiple formats (You vs Competitor, Competitor Alternatives, etc.), emphasizing deep research, trust-building content, and modular architecture to capture competitive search traffic and guide users toward conversion.

Collection of eight production-ready product agent skills and plugins for Claude Code, Codex CLI, OpenClaw, and others, covering PM toolkits, UX/UI design, SaaS scaffolding, research, and prioritization using Python stdlib tools.

This skill automates the multi-round research review process. It iteratively reviews provided work using advanced AI models (Codex MCP), identifying critical weaknesses, implementing fixes, and re-reviewing until the content achieves a positive assessment or the maximum round limit is reached. Ideal for refining complex academic papers, technical designs, or large codebases that require rigorous, iterative quality assurance.

A comprehensive collection of agent skills for rigorously auditing digital interfaces and user flows. It evaluates designs against 168 research-backed UX/UI principles, detects common design antipatterns, and provides specialized review for AI-powered interfaces, ensuring adherence to modern best practices.

This skill acts as a Visual Psychologist and Environmental Researcher, guiding the systematic mapping of visual elements—including color, typography, spacing, and imagery—to desired emotional states and conversion goals. It ensures that design decisions are strategic, supporting persuasion and brand narrative rather than merely being decorative. Use it when you need visuals to evoke a specific feeling (e.g., trust, urgency, calm) for a defined target audience, while respecting cultural and accessibility constraints.

An advanced, AI-powered agent designed to generate comprehensive and workable unit tests for virtually any programming language (e.g., Python, C#, Java, Go, TypeScript). It operates via a sophisticated multi-agent pipeline (Research → Plan → Implement → Build → Test → Fix → Lint). This ensures generated tests are not only written following project conventions but also compile, pass all tests, and effectively improve code coverage.

A robust, structured knowledge base designed for complex research projects. It accumulates and maps diverse entities—including research papers, core ideas, experiments, and scientific claims—and tracks their relationships (e.g., support, contradiction, extension) into a comprehensive graph. This tool is ideal for researchers who need to manage knowledge evolution, identify field gaps, and prevent knowledge decay across the entire research lifecycle.

This advanced research technique is designed for deep dives into football topics. Instead of relying solely on the original query, it mandates tracking every discovered entity—such as player names, club names, or coaches—from search results. By treating these entities as new leads, the user must formulate follow-up searches using the discovered names and remaining constraints, ensuring nothing relevant is overlooked and building a comprehensive knowledge chain.

This technique is designed for advanced academic research, enabling users to search for theses by running two parallel tracks: a Topic Track (focusing on discipline, methodology, and materials) and a People Track (focusing on university, advisor, degree type, and year). By switching between these two methodologies, users can cross-validate results and significantly improve the accuracy and completeness of their literature review.

A comprehensive mentor for graduate students and early-career researchers, guiding the entire research lifecycle. This skill covers formulating rigorous research questions, designing studies (quantitative, qualitative, mixed-methods), conducting literature reviews, analyzing data, and writing publishable papers. It emphasizes methodological rigor, critical thinking, and ethical considerations.

A powerful tool designed for academic researchers and scientists. It searches a curated database of scientific literature and retrieves structured metadata and quantitative experimental data (including methods, results, sample sizes, and quality scores) directly from full-text papers, going far beyond simple abstracts. Ideal for systematic literature reviews, evidence synthesis, and meta-analyses requiring detailed, comparable study information.

This skill analyzes the implementation gap by comparing approved feature requirements against the existing codebase, architecture, and context. It helps identify missing capabilities, potential integration challenges, and necessary technical research areas. The output provides multiple viable implementation options and a comprehensive analysis report, guiding the transition from requirements definition to technical design.