Multi Agent Patterns

muratcankoylan/agent-skills-for-context-engineering

This walks you through the three main patterns for splitting work across multiple LLM instances: supervisor (centralized control), swarm (peer handoffs), and hierarchical (layered abstraction). The key insight is that you're doing this for context isolation, not role-playing organizational structure. Watch out for the "telephone game" problem where supervisors lose 50% performance by paraphrasing sub-agent responses instead of forwarding them directly. The token economics section is especially honest about costs, showing multi-agent systems can burn tokens much faster than single-agent approaches. Use it when you're actually hitting context limits or need true parallelization, not just because multiple agents sound more sophisticated.

skills.sh installs: 329
GitHub stars: 18.3K
last updated: May 26, 2026

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Install

$ npx -y skills add muratcankoylan/agent-skills-for-context-engineering --skill multi-agent-patterns --agent claude-code

Installs into .claude/skills of the current project.

Files

Multi-Agent Architecture Patterns

Multi-agent architectures distribute work across multiple language model instances, each with its own context window. When designed well, this distribution enables capabilities beyond single-agent limits. When designed poorly, it introduces coordination overhead that negates benefits. The critical insight is that sub-agents exist primarily to isolate context, not to anthropomorphize role division.