hive-patterns

Provide patterns and best practices for building goal-driven AI agents with Hive.

Updated Mar 4, 2026

Search Tags:#AI agent #feedback loops #event loop #Hive #agent patterns

Ready to run

npx skills add https://github.com/thatditsyboy/aden-pricing-tracker --skill hive-patterns-thatditsyboy

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hive-patterns
Source: https://github.com/thatditsyboy/aden-pricing-tracker/tree/main/.claude/skills/hive-patterns
Command: npx skills add https://github.com/thatditsyboy/aden-pricing-tracker --skill hive-patterns-thatditsyboy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires hive, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps developers overcome challenges in building robust, goal-driven AI agents by providing best practices and patterns, including client-facing interaction, feedback edges, judge patterns, and context management.

Core Features & Use Cases

Client-Facing Interaction: Supports multi-turn conversations with users using event_loop nodes.
Edge-Based Routing and Feedback Loops: Provides conditional edge routing for complex workflows.
Judge Patterns: Utilizes a core principle that the judge is the sole mechanism for acceptance decisions.
Fan-Out/Fan-In: Supports parallel execution for tasks with multiple outputs.
Context Management: Manages context windows with tiered compaction to handle large data.

Quick Start

Run the hive-create command to generate a new agent, then explore the patterns and examples provided by the hive-patterns skill to improve your agent design.

Frequently Asked Questions about hive-patterns

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

FAQPage Schema

How do I build robust AI agents with feedback loops and conditional routing?▼

To build robust AI agents with feedback loops, you implement conditional edge routing alongside judge patterns to gate acceptance decisions. This approach supports complex, goal-driven workflows by ensuring validation steps loop back dynamically until criteria are met.

How do I manage context windows for AI agents handling large data inputs?▼

To manage context windows for AI agents with large data, use tiered compaction patterns. This technique progressively summarizes or offloads context, preventing token overflow while maintaining the necessary background information for goal-driven tasks.

What is the best way to handle multi-turn conversations in a production-grade agent workflow?▼

The best way to handle multi-turn conversations in production-grade agent workflows is using event loop nodes. This pattern facilitates continuous, client-facing interaction by cycling through user inputs and agent responses dynamically within the execution graph.

Does this agent framework support parallel execution for tasks with multiple outputs?▼

Yes, the framework supports parallel execution through a fan-out and fan-in pattern. This allows the agent to dispatch multiple concurrent tasks and aggregate the results, optimizing workflows that generate several distinct outputs simultaneously.

Can I use judge patterns for validation and acceptance decisions in AI agents?▼

Yes, you can use judge patterns for validation, as the judge acts as the sole mechanism for acceptance decisions. This ensures that the agent only progresses or completes a task when explicit validation criteria are satisfied.

What is the core mechanism for routing in complex AI agent workflows?▼

The core mechanism for routing in complex AI agent workflows is edge-based routing. By applying conditional edges, developers can direct the execution flow dynamically based on real-time state, enabling branching logic and robust feedback loops.