Skill UI

Qdrant is a high-performance vector database. This client SDK suite provides official, easy-to-use libraries for multiple programming languages (including Python, Java, Go, and TypeScript). It enables developers to seamlessly integrate sophisticated vector search capabilities into their applications, supporting both REST and gRPC APIs. Ideal for building semantic search engines, recommendation systems, and advanced AI applications.

This skill allows for the generation and execution of complex, optimized SQL queries to derive deep business insights. It supports sophisticated data workflows, including multi-table joins, multi-dimensional analysis (e.g., trend analysis, attribution modeling, comparative analysis), and subsequent recommendations for appropriate data visualizations (e.g., line charts, waterfall charts). It goes beyond simple SELECT statements by planning the query structure, handling schema relationships, and ensuring adherence to best practices.

This skill guides users through comprehensive Conversion Rate Optimization (CRO) analysis for any marketing page, including homepages, landing pages, and product pages. It analyzes critical elements such as value proposition clarity, headline effectiveness, CTA placement, trust signals, and friction points, providing actionable recommendations for immediate quick wins and high-impact structural changes to boost conversions.

Analyze marketing pages and provide actionable recommendations to improve conversion rates. Covers homepage, landing, pricing, feature, and form optimization with frameworks for quick wins, high-impact changes, and A/B test ideas.

This skill provides a scaffold for building complex, composable recommendation, ranking, and feed pipelines. It implements the robust six-stage pattern (Source→Hydrator→Filter→Scorer→Selector→SideEffect), which is the industry standard for determining "top K items." Use it for social feeds, content CMS ranking, RAG reranking, or any system needing complex candidate prioritization.

A comprehensive intelligence tool designed for clinical researchers to navigate the complex NIH grant landscape. It utilizes a detailed 6-step intake process (covering research idea, career stage, and data status) to perform a multi-faceted positioning analysis. The system maps research to appropriate NIH institutes using RePORTER data and generates an editable Word document containing actionable mechanism recommendations, submission timelines, and strategic guidance for funding applications.

A comprehensive skill designed to process and organize incoming emails. It classifies messages using a predefined user taxonomy, researches new senders, generates decision recommendations, drafts replies (without sending), and updates a knowledge base with learnings. Ideal for running on a recurring schedule (1-3 times daily) or on demand to maintain an organized inbox.

This advanced skill provides comprehensive patent prior-art and landscape intelligence. Instead of generic searches, it forces the user to commit to one of five specialized sub-use-cases: Novelty Search, Freedom-to-Operate (FTO), Competitive Landscape, Acquisition Diligence, or Litigation Prior-Art. It searches multiple global databases (Google Patents, Espacenet, USPTO) and outputs a detailed, actionable DOCX report with strategic recommendations, risk assessments, and technical verdicts.

A comprehensive tool for BizOps and VMO operators to conduct proactive, multi-dimensional vendor performance audits. It scores SaaS and third-party vendors using weighted criteria (reliability, security, support), tracks SLA compliance to identify potential credit claims, and classifies third-party risk across critical vectors (data, financial, regulatory). Ideal for quarterly vendor health checks and defensible renewal recommendations.

This skill performs an observability-first audit of deployed Vercel applications. It comprehensively analyzes production metrics, project configurations, and codebase scans to identify potential cost overruns and performance bottlenecks. It provides version-aware, actionable recommendations for improving application efficiency and reducing cloud expenditure.

This meta-skill is designed for generating comprehensive competitive intelligence briefs for sales, business development, and strategy teams. It monitors signals across various dimensions (pricing, product, leadership, funding, etc.) for specified competitors or targets over a defined time window. It helps track changes against a baseline and provides actionable recommendations, moving beyond simple raw data dumps.

This meta-skill is designed for complex decision analysis when a user provides or references structured documents such as contracts, quotes, spreadsheets, or notices. Instead of simple summarization, it extracts key evidence, identifies risks (e.g., renewal risk, payment terms), and structures the output into decision-ready formats, such as 'sign/reject/negotiate' recommendations, evidence tables, or required next actions. It supports input from PDF, DOCX, and XLSX formats.