Hive Agent Architecture
Framework for building, registering, and orchestrating Model Context Protocol (MCP) tools and AI agent workflows within the Hive native Rust architecture.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
188 skills found
Framework for building, registering, and orchestrating Model Context Protocol (MCP) tools and AI agent workflows within the Hive native Rust architecture.
CLI interface for Gemini AI, enabling one-shot model inference, text generation, and JSON-formatted data extraction for OpenClaw users.
Unified AI gateway for building full-stack apps and automating tasks. Access 100+ AI models for content generation, web scraping, app deployment, and Stripe payments with a single API key.
Physical hardware synthesis bridge for PAI. Generates blueprints, 3D printing code, SVG paths for laser cutting, and G-Code for CNC machining to bring agentic designs into the physical world.
An advanced research intelligence skill for content creators and marketers that analyzes trends across 10+ platforms to generate data-driven content outlines based on user intent.
Full-stack automated paper writing pipeline from research narrative to polished LaTeX/PDF.
A CLI tool that automates the discovery and symlinking of agent skills distributed via npm packages, simplifying integration for AI-powered coding agents.
AI-powered Kubernetes and OpenShift troubleshooting. Proactively assess cluster health, debug pod failures, analyze logs, and validate security using Popeye-inspired patterns.
Implementation patterns for MERIDIAN autonomous AI agents using Claude API, including BaseAgent lifecycle, structured tool use, token budget enforcement, and cron scheduling.
Build stateful AI agents on Cloudflare Workers using the Agents SDK. Features real-time WebSockets, persistent state management, scheduled background tasks, and native tool integration for production-ready deployments.
Operate the btca CLI for source-first code research. Manage git, local, and npm resources to ground AI answers in actual codebase context rather than outdated documentation.
Read and navigate external documentation efficiently using llms.txt, MCP search, and smart parsing strategies.