shap
About
This skill provides SHAP-based model interpretability for explaining ML predictions and computing feature importance across various model types. It enables developers to generate multiple SHAP visualizations and perform model debugging, fairness analysis, and explainable AI implementation. Use it when you need to interpret predictions from tree-based models, deep learning frameworks, linear models, or black-box models.
Quick Install
Claude Code
Recommendednpx skills add robinbarvaag/poynt -a claude-code/plugin add https://github.com/robinbarvaag/poyntgit clone https://github.com/robinbarvaag/poynt.git ~/.claude/skills/shapCopy and paste this command in Claude Code to install this skill
GitHub Repository
Related Skills
content-collections
MetaThis 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.
polymarket
MetaThis 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.
creating-opencode-plugins
MetaThis 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.
sglang
MetaSGLang 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.
