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hive-create

Guide engineers through building goal-driven AI agents and exporting Python packages.

10.9k|5.7k|Updated Jan 12, 2026
Search Tags:#agent#nodes#mcp#builder#graph#edges
One-click install
npx skills add https://github.com/aden-hive/hive --skill hive-create-aden-hive
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hive-create
Source: https://github.com/aden-hive/hive/tree/main/.claude/skills/hive-create
Command: npx skills add https://github.com/aden-hive/hive --skill hive-create-aden-hive

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Step-by-step guidance to turn a raw idea for an autonomous AI agent into a production-ready package, covering goal-definition, node-and-edge design, and packaging.

Core Features & Use Cases

  • Structured workflow for defining goals, constructing a node graph, connecting edges, and exporting a Python package.
  • Templates and from-scratch support for rapid agent creation and customization.
  • MCP integration and runtime scaffolding to run agents with standard tooling and storage.

Quick Start

Define a goal, assemble nodes and edges, and export a runnable Python package in a guided, end-to-end session.

Frequently Asked Questions about hive-create

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a goal-driven AI agent from scratch and export it as a Python package?▼

To build a goal-driven AI agent, you define the objective, construct a node graph, connect edges, and export a production-ready Python package using guided step-by-step workflows. This process enforces repeatable packaging and MCP-based export.

What is the best way to design and validate a node graph for an autonomous agent?▼

Designing a node graph for an autonomous agent involves assembling individual nodes and connecting edges in a structured sequence. The guided workflow allows you to validate the graph before exporting the agent.

Can I use templates for rapid AI agent creation instead of starting from scratch?▼

You can use templates for rapid AI agent creation to quickly scaffold a functional node graph. The system supports both template-based workflows and from-scratch development for full customization.

Does the agent packaging process support MCP integration and standard tooling?▼

The agent packaging process supports MCP integration and runtime scaffolding, allowing you to run exported Python packages with standard tooling and storage without additional manual setup.

What are the limitations of template-based agent workflows compared to from-scratch development?▼

Template-based agent workflows accelerate initial node graph design but may limit deep customization compared to from-scratch development. Use templates for rapid scaffolding and from-scratch development when you need granular edge and node control.

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