Agent Skills Hub

research-pipeline

End-to-end autonomous research agent: from idea generation and literature review to experiment execution, adversarial review loops, and paper writing.

peekaboo

A macOS UI automation CLI that enables agents to capture screens, target UI elements, manage applications, and execute cross-app workflows with JSON-based scripting.

data-cleaning-pipeline-generator

Generates data cleaning pipelines for pandas/polars/PySpark, handling missing values, duplicates, outliers, type conversions, and validation.

gemini-vision

Implement Google Gemini API vision capabilities for image/document analysis including captioning, object detection, segmentation, and multi-image comparison.

microsim-screen-capture

Automate high-quality screenshot generation for MicroSim visualizations using Chrome headless mode. Ideal for documentation, social media previews, and quality assessment.

agentic-workflows

Build production-grade AI agents using LangGraph, Anthropic/OpenAI/vLLM, and structured outputs. Features streaming, A2A protocol, Pydantic validation, vector memory, and guardrails for resilient, multi-agent workflows.

moltbot-arena

AI agent skill for Moltbot Arena, a real-time strategy programming game. Manage units, automate resource harvesting, coordinate structures, and execute tactical decisions via REST API.

template-structure

Standardize project scaffolding with pre-configured Claude Code directories, commands, and agents to ensure consistency across all your development templates.

code-review

Perform comprehensive code reviews with a focus on security vulnerabilities, performance optimization, maintainability, and code correctness.

karpathy-guidelines

Behavioral guidelines for LLMs to reduce coding mistakes, follow best practices, and improve output quality by enforcing simplicity, surgical changes, and goal-driven verification.

typescript

TypeScript development standards for LobeHub, covering type safety, async patterns, import organization, UI component integration, and performance optimization.

context-fundamentals

Foundational guidelines for context engineering: optimizing token budgets, attention mechanics, and system architecture for AI agents.