AI Agent Skills

Compile a mapped surface into working interfaces and keep them alive as the target changes. Use when the user has a recon report or a surfacer IR and wants a CLI, an Ope…

This repository contains links to machine learning exams, homework assignments, and exercises that can help you test your understanding.

Lightpanda browser, drop-in replacement for Chrome-based browsing in any AI agent - faster and lighter for tasks without graphical rendering like data retrieval. Use it…

Smoothly Manage Multiple LLMs (OpenAI, Anthropic, Azure) and Image Models (Dall-E, SDXL), Speed Up Responses, and Ensure Non-Stop Reliability.

LLM-powered generation pipeline for Claude Code skills and AGENTS.md — doc-init command, prompt system, context building, and output parsing

Claude Code adapter for the DeepRefine agent-native refinement loop. Use when the user invokes /deeprefine, or asks to refine, diagnose, review, or apply change

Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when desig…

[CVPR 2025 Highlight] Official code for "Olympus: A Universal Task Router for Computer Vision Tasks"

Use this skill when creating, editing, or reviewing an Unreal Engine Agent Skill, a named bundle of instructions registered with the unreal-mcp server (distinct from Cla…

A Python CLI to test, benchmark, and find the best RAG chunking strategy for your Markdown documents.

Connects and configures the Render MCP server for AI coding tools—setup per tool (Cursor, Claude Code, Codex), authentication, workspace selection, tool catalog

Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memo…

🚀 rooroo (如如): Minimalist AI Orchestration with Swiss Army Knife Agents 🚀

Code repository for the online course Feature Selection for Machine Learning

A tiny yet powerful LLM inference system tailored for researching purpose. vLLM-equivalent performance with only 2k lines of code (2% of vLLM).

Integrating AI into every workflow with our open-source, no-code platform, powered by the actor model for dynamic, graph-based solutions.