Skill UI

This tool analyzes and compares staking opportunities across various Proof-of-Stake (PoS) blockchains and liquid staking protocols. It calculates net Annual Percentage Yield (APY), assesses protocol risks, and provides data-driven recommendations for optimal asset allocations to maximize returns while managing risk. Use it when analyzing staking opportunities, comparing validators, or optimizing your DeFi portfolio.

This skill enables the coordination of multiple specialized AI agents within a single session. It is designed for complex, multi-domain tasks such as comprehensive code reviews, security audits, or full-stack feature implementations. It coordinates expertise across various domains (backend, frontend, testing, architecture) to provide holistic analysis and actionable recommendations.

Fully managed, auto-scaling vector database tailored for production AI workloads such as RAG, recommendations, and semantic search, offering hybrid dense+sparse search, namespaces, metadata filters, and <100ms latency on serverless infrastructure.

Provides a catalog of Linux and Windows post-exploitation privilege escalation techniques, covering sudo/capability abuse, scheduled tasks, token impersonation, AD kerberoasting, credential harvesting, and tool recommendations for red teamers.

Provides prioritized performance optimization recommendations across frontend, backend, and infrastructure, offering bottleneck analysis, impact estimates, and phased implementation roadmaps when users request speed improvements or reviews.

Qdrant is a high-performance, Rust-powered vector database designed for production-grade RAG (Retrieval-Augmented Generation) and semantic search applications. It provides fast nearest neighbor search, supports complex hybrid search using metadata filtering, and offers horizontal scalability through sharding and replication. Ideal for building real-time recommendation systems and enterprise-level knowledge retrieval.

This skill analyzes provided long-form content to assess its quality, completeness, and adherence to modern SEO best practices. It meticulously audits for E-E-A-T signals, readability, keyword optimization, and overall structure, providing a detailed 'Content Audit Report' with scores and actionable recommendations for improvement.

This pattern provides established best practices for implementing efficient similarity search in production systems. It is crucial for building semantic search engines, implementing Retrieval-Augmented Generation (RAG), creating advanced recommendation systems, and optimizing retrieval performance when scaling to millions of vectors.

A comprehensive meta-skill for validating, testing, and scoring the quality of technical skills within an ecosystem. It performs deep structural checks (SKILL.md compliance), executes Python scripts for syntax, runtime, and output validation, and generates multi-dimensional quality scores using letter grades and tier recommendations. Ideal for new skill authorship, auditing existing components, or integrating rigorous QA gates into CI/CD pipelines.

Execute systematic SQL injection vulnerability assessments on web applications using reconnaissance, payload tuning, and exploitation workflows to uncover database flaws, capture evidence, and validate remediation recommendations for authorized security testing.

Provides concise guidance for implementing Stripe integrations, prioritizing CheckoutSessions, PaymentIntents, Billing and Connect flows, avoiding deprecated endpoints, and following migration and compliance recommendations.

Analyzes tweet drafts through Twitter’s recommendation architecture to rewrite and edit content, explain what the algorithms favor, and surface engagement tactics that align with Real-graph, SimClusters, TwHIN, and Tweepcred signals for broader reach.