Can I use this skill to prevent common coding errors?

Yes, it includes an Anti-Pattern Sharing strategy that allows agents to document and propagate information about techniques that failed during execution to prevent future occurrences.

What kind of data is shared between groups?

Groups share confidence scores, decision rationales, success metrics, detected patterns, and performance impacts to ensure every agent has full context for its specific task.

How does this skill facilitate agent learning?

It implements specific feedback loops where the Validation group provides quantified effectiveness data back to the Analysis group, allowing the system to refine its recommendations over time.

What is the four-tier architecture used by this skill?

The architecture divides agents into four specialized groups: Group 1 for Strategic Analysis, Group 2 for Decision Making, Group 3 for Execution, and Group 4 for Validation and Optimization.

Group Collaboration & Multi-Agent Orchestration

Name: Group Collaboration & Multi-Agent Orchestration
Author: bejranonda

bybejranonda

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Collaboration Tools

Standardizes communication, knowledge sharing, and feedback loops between specialized AI agent groups in a four-tier architecture.

About

This skill provides a comprehensive framework for coordinating the four distinct tiers of an autonomous AI agent system: Strategic Analysis, Decision Making, Execution, and Validation. It establishes rigorous communication protocols and automated knowledge transfer strategies that allow agents to share patterns, anti-patterns, and success metrics. By implementing these collaborative workflows, users can ensure that their Claude Code agents work in harmony, learning from each iteration to improve code quality, security, and performance autonomously.

Key Features

Comprehensive metric tracking and rationale recording for every group interaction
Structured handoff protocols between analysis, planning, and execution phases
Standardized inter-group communication protocols for four-tier architectures
9 GitHub stars
Closed-loop feedback mechanisms for continuous autonomous learning
Automated pattern propagation and knowledge transfer across agent groups

Use Cases

Managing complex refactoring projects that require separate analysis and execution steps
Building a self-improving development environment where validation results inform future planning
Scaling autonomous agent systems by standardizing how different specialized tools communicate

About

Key Features

Comprehensive metric tracking and rationale recording for every group interaction
Structured handoff protocols between analysis, planning, and execution phases
Standardized inter-group communication protocols for four-tier architectures
9 GitHub stars
Closed-loop feedback mechanisms for continuous autonomous learning
Automated pattern propagation and knowledge transfer across agent groups

Use Cases

Managing complex refactoring projects that require separate analysis and execution steps
Building a self-improving development environment where validation results inform future planning
Scaling autonomous agent systems by standardizing how different specialized tools communicate