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agentic-patterns

Provide reusable reasoning patterns for designing and debugging LLM agents.

2|Updated Mar 25, 2026
Search Tags:#planning#debugging#react#agent#multi-agent#self-reflection#context-management
One-click install
npx skills add https://github.com/slabgorb/sidequest --skill agentic-patterns-slabgorb
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agentic-patterns
Source: https://github.com/slabgorb/sidequest/tree/main/.pennyfarthing/skills/pf-agentic-patterns
Command: npx skills add https://github.com/slabgorb/sidequest --skill agentic-patterns-slabgorb

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Helps developers design and debug robust AI agent behavior by codifying core reasoning patterns like ReAct, Plan-and-Execute, Self-Reflection, and confidence calibration.

Core Features & Use Cases

  • ReAct for reasoning and acting with tool use.
  • Plan-and-Execute to decompose complex goals into steps.
  • Self-Reflection to critique and refine outputs before presenting.
  • Multi-Agent Coordination and Context Management for seamless workflow across agents.

Quick Start

Apply ReAct, Plan-and-Execute, and Self-Reflection patterns to a small debugging task and observe guided improvements.

Frequently Asked Questions about agentic-patterns

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

FAQPage Schema
What are the core reasoning patterns for designing robust LLM agents?▼

Core reasoning patterns for LLM agents include ReAct for tool use, Plan-and-Execute for goal decomposition, Self-Reflection for output refinement, and confidence calibration for safe interaction.

How do I debug multi-agent coordination failures and manage context?▼

Debug multi-agent coordination failures by applying reusable reasoning patterns that provide context management and error recovery, ensuring seamless workflow across multiple agents.

How does the Plan-and-Execute pattern help decompose complex goals?▼

The Plan-and-Execute pattern decomposes complex goals into manageable steps, allowing agents to systematically execute tasks and recover from errors during the workflow.

When should I use Self-Reflection in an AI agent workflow?▼

Use Self-Reflection in AI agent workflows to critique and refine outputs before presenting them, ensuring higher quality results and improved confidence calibration.

Can I use these reasoning patterns for React development and debugging tasks?▼

Yes, you can apply reasoning patterns like ReAct to React development and debugging tasks to observe guided improvements in agent behavior and error recovery.

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