How does the memory management system work?

It utilizes a sliding window approach that maintains a specific number of recent messages while compressing older context into summaries to prevent token overflow and maintain performance.

What is the difference between ReAct and Plan-and-Execute?

ReAct is highly iterative and decides the next step after every action, while Plan-and-Execute creates an entire roadmap upfront and executes it sequentially, which is often more efficient for predictable tasks.

What is the ReAct pattern in this skill?

The ReAct pattern (Reasoning + Acting) allows an LLM to generate a 'Thought' before taking an 'Action' and then processing the 'Observation' from that action to decide the next logical step.

Can I prevent the agent from getting stuck in an infinite loop?

Yes, the skill includes logic for 'max_steps' limits and validation checkpoints, recommending a 5-15 step window to ensure the agent reaches a conclusion or terminates safely.

Agentic Workflow Loops

Name: Agentic Workflow Loops
Author: yonatangross

byyonatangross

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Data Science & ML

Implements autonomous reasoning patterns and agentic workflows for multi-step LLM task execution and self-correction.

About

The Agentic Workflow Loops skill provides production-ready architectural patterns for building autonomous AI agents within the Claude Code environment. It enables developers to implement sophisticated reasoning loops like ReAct (Reasoning + Acting), Plan-and-Execute strategies, and self-correcting validation cycles. By providing standardized templates for memory management and tool orchestration, this skill transforms Claude from a simple chat interface into a capable agent that can independently plan complex projects, execute sequential steps, and recover from errors using structured feedback loops.

Key Features

Pre-configured safeguards against infinite loops and context overflow
ReAct (Reasoning + Acting) pattern implementation
29 GitHub stars
Sliding window memory management with context compression
Self-correction loops for automated output validation
Plan-and-Execute multi-step task orchestration

Use Cases

Building autonomous research agents that plan and execute multi-source data gathering
Creating complex automation scripts that require iterative reasoning and tool use
Developing self-healing code generation workflows that validate and fix errors

About

Key Features

Pre-configured safeguards against infinite loops and context overflow
ReAct (Reasoning + Acting) pattern implementation
29 GitHub stars
Sliding window memory management with context compression
Self-correction loops for automated output validation
Plan-and-Execute multi-step task orchestration

Use Cases

Building autonomous research agents that plan and execute multi-source data gathering
Creating complex automation scripts that require iterative reasoning and tool use
Developing self-healing code generation workflows that validate and fix errors