Back to Skills

moai-core-expertise-detection

mattnigh
Updated 5 days ago
11 views
22
1
22
View on GitHub
Metaaidesign

About

This Claude Skill analyzes user behavior and communication to automatically detect their expertise level. It uses this assessment to dynamically adjust response complexity, tutorial depth, and communication style. Developers should use it to enable personalized, adaptive interactions within their enterprise AI applications.

Quick Install

Claude Code

Recommended
Primary
npx skills add mattnigh/skills_collection -a claude-code
Plugin CommandAlternative
/plugin add https://github.com/mattnigh/skills_collection
Git CloneAlternative
git clone https://github.com/mattnigh/skills_collection.git ~/.claude/skills/moai-core-expertise-detection

Copy and paste this command in Claude Code to install this skill

GitHub Repository

mattnigh/skills_collection
Path: collection/jg-chalk-io__Nora-LiveKit__claude__skills__moai-core-expertise-detection__SKILL.md
0

Related Skills

content-collections

Meta

This skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.

View skill

polymarket

Meta

This skill enables developers to build applications with the Polymarket prediction markets platform, including API integration for trading and market data. It also provides real-time data streaming via WebSocket to monitor live trades and market activity. Use it for implementing trading strategies or creating tools that process live market updates.

View skill

creating-opencode-plugins

Meta

This skill helps developers create OpenCode plugins that hook into 25+ event types like commands, files, and LSP operations. It provides the plugin structure, event API specifications, and implementation patterns for JavaScript/TypeScript modules. Use it when you need to intercept, monitor, or extend the OpenCode AI assistant's lifecycle with custom event-driven logic.

View skill

sglang

Meta

SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.

View skill