Does this support multi-agent collaboration?

Yes, it includes patterns for sequential pipelines where agents pass data to one another, as well as supervisor architectures where a primary agent delegates tasks to specialized sub-agents.

What types of memory systems are covered?

It covers four distinct layers: working memory for the immediate conversation, short-term scratchpads, long-term vector-based knowledge, and episodic memory for recording and learning from past task executions.

How does this skill manage agent safety?

The skill provides patterns for input/output validation, strict tool schemas, maximum iteration limits to prevent infinite loops, and human-in-the-loop triggers for high-risk actions.

What is the ReAct pattern in AI agents?

ReAct stands for Reason + Act. It is a paradigm where the agent generates a reasoning trace (Thought) followed by a specific tool invocation (Action), then observes the result to iterate until a goal is achieved.

AI Agent Systems

Name: AI Agent Systems
Author: InugamiDev

byInugamiDev

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Ciencia de Datos y ML

Architects and implements autonomous AI agent systems with advanced tool use, memory architectures, and multi-agent orchestration patterns.

This skill provides comprehensive architectural patterns and implementation guidance for building sophisticated agentic workflows. It enables developers to move beyond simple chat interfaces by implementing the ReAct pattern, designing multi-agent supervisor systems, and managing complex state through episodic and long-term memory. With a heavy focus on safety, it includes guardrails for tool execution, token budgeting, and human-in-the-loop workflows, ensuring that autonomous agents remain controllable and cost-effective while performing real-world actions.

Características Principales

01Standardized tool definition schemas and validation logic

02Multi-agent orchestration including sequential and supervisor architectures

03Built-in safety guardrails and execution boundary enforcement

04Sophisticated memory systems (working, episodic, and long-term vector storage)

0545 GitHub stars

06Implementation of ReAct (Reason + Act) and Plan-and-Execute patterns

Casos de Uso

01Creating customer service agents with secure tool access to databases and external APIs

02Developing multi-agent coding pipelines with specialized researchers, writers, and reviewers

03Building autonomous research agents that use search tools and analyze findings

Características Principales

01Standardized tool definition schemas and validation logic

02Multi-agent orchestration including sequential and supervisor architectures

03Built-in safety guardrails and execution boundary enforcement

04Sophisticated memory systems (working, episodic, and long-term vector storage)

0545 GitHub stars

06Implementation of ReAct (Reason + Act) and Plan-and-Execute patterns

Casos de Uso

01Creating customer service agents with secure tool access to databases and external APIs

02Developing multi-agent coding pipelines with specialized researchers, writers, and reviewers

03Building autonomous research agents that use search tools and analyze findings