
multi-agent-patterns
by Zpankz
MCP Skillset - Claude Code skills, references, and knowledge base
SKILL.md
name: multi-agent-patterns description: Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when specializing agents improves quality.
Multi-Agent Architecture Patterns for Claude Code
Multi-agent architectures distribute work across multiple agent invocations, each with its own focused context. 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.
Core Concepts
Multi-agent systems address single-agent context limitations through distribution. Three dominant patterns exist: supervisor/orchestrator for centralized control, peer-to-peer/swarm for flexible handoffs, and hierarchical for layered abstraction. The critical design principle is context isolation—sub-agents exist primarily to partition context rather than to simulate organizational roles.
Effective multi-agent systems require explicit coordination protocols, consensus mechanisms that avoid sycophancy, and careful attention to failure modes including bottlenecks, divergence, and error propagation.
Why Multi-Agent Architectures
The Context Bottleneck
Single agents face inherent ceilings in reasoning capability, context management, and tool coordination. Multi-agent architectures address these limitations by partitioning work across multiple context windows.
The Parallelization Argument
Many tasks contain parallelizable subtasks that a single agent must execute sequentially. Multi-agent architectures assign each subtask to a dedicated agent with a fresh context.
The Specialization Argument
Different tasks benefit from different agent configurations. Multi-agent architectures enable specialization without combinatorial explosion.
Progressive Loading
L2 Content (loaded when architectural patterns and implementation details needed):
L3 Content (loaded when memory systems and advanced coordination needed):
Score
Total Score
Based on repository quality metrics
SKILL.mdファイルが含まれている
ライセンスが設定されている
100文字以上の説明がある
GitHub Stars 100以上
3ヶ月以内に更新がある
10回以上フォークされている
オープンIssueが50未満
プログラミング言語が設定されている
1つ以上のタグが設定されている
Reviews
Reviews coming soon