Skill UI

The Desktop Commander MCP grants agents deep reach across the user's entire operating environment, extending far beyond the typical IDE sandbox. Key capabilities include maintaining persistent shell sessions, managing long-running background processes, accessing arbitrary filesystems, and natively handling structured data formats (Excel, PDF, DOCX). It also enables remote operations via SSH and large-scale content search, making it ideal for complex, multi-turn development, DevOps, and data analysis workflows.

This expert skill helps design optimal pricing and monetization strategies for SaaS and digital products. It guides users through value-based pricing, packaging structure, and pricing metrics. By utilizing advanced research methods like Van Westendorp and MaxDiff, users can determine optimal price points, tier structures, and pricing models to maximize Customer Lifetime Value (LTV) and Average Revenue Per User (ARPU).

A comprehensive guide to implementing schema.org structured data using JSON-LD format. Learn to correctly identify page types (Product, Article, Organization, FAQPage) and apply the necessary schema markup to help search engines understand content context. This skill covers initial assessment, core principles (accuracy, validation), and optimizing for rich results like star ratings, site search blocks, and rich snippets, significantly boosting search engine visibility and CTR.

Provides a structured workflow for systematically accessing and analyzing official intellectual property data from the USPTO. This skill covers patent search (PatentSearch API), trademark status checks (TSDR), ownership transfer tracking, and building reproducible IP landscape logs. It is designed for data collection and record verification, emphasizing the use of official APIs and rigorous logging practices. (Warning: Not for legal advice.)

This skill generates structured and standardized Git commit messages following the Conventional Commits specification. By analyzing code changes (via diff or status), it helps developers automatically create clear, actionable, and searchable commit history messages, ensuring consistency across the project.

Expert skill for implementing schema.org structured data (JSON-LD) to enhance search engine visibility and rich results. Covers validation, common types like Product and Article, and integration across static and dynamic web platforms.

A comprehensive tool designed for academic literature orientation. It searches papers using free keyless APIs (PubMed and OpenAlex) and builds a structured 'launching pad' guide, rather than a finished review. It helps researchers define their scope using frameworks like PICO/SPIDER, ensuring a confident and systematic starting point for deep research in unfamiliar fields.

This skill transforms a course syllabus (PDF, DOCX, etc.) into a highly curated, supplementary academic reading list. It parses topics and learning outcomes, calibrates content difficulty based on the target audience (undergraduate, graduate, etc.), and searches academic databases (Consensus) for recent, peer-reviewed papers. The output is a professional .docx file containing summaries, links, and advanced discussion questions, making it invaluable for educators and students needing structured, up-to-date course resources.

This skill automatically enriches research wiki pages that are left as bare scaffolds after initial ingestion. It reads multiple external sources (like arXiv/alphaxiv) and fills in 10 structured `_TODO._` sections (e.g., Problem/Gap, Key Results, Open Questions) with detailed prose summaries. Use this skill to transform incomplete research scaffolds into a comprehensive, structured knowledge base.

Structures prospective clinical study design: classify endpoints with surrogate flagging, estimate sample size and power for means, proportions and survival, and score phase-gate feasibility (GO / GO-WITH-CONDITIONS / REDESIGN / NO-GO) with named owners. Estimates only, never clinical fact.

This skill converts complex, narrative technical documents, research excerpts, or natural language problem descriptions into a highly structured Mathematical Problem Specification (MPS). It systematically extracts variables, constraints, objectives, and uncertainty parameters. By adhering to a strict Zero-Inference Protocol, it ensures all extracted elements are strictly grounded in the source text, making it invaluable for formalizing real-world optimization, data science, and engineering problems.

This skill provides a rigorous, operational framework for product and user research. It helps researchers select the appropriate study method (generative, evaluative, validation) based on goals and product stages. It also calculates defensible sample sizes and saturation levels, and synthesizes raw observations into governed, evidence-based insights, ensuring all findings are rooted in cross-participant recurrence.