Agent skill · research science · aiskillstore
multi-agent-analysis
Analyze coordination patterns, handoff mechanisms, and state sharing in multi-agent systems. Use when (1) understanding how agents transfer control, (2) evaluating shared vs isolated state patterns, (3) mapping communication protocols between agents, (4) assessing multi-agent orchestration approaches, or (5) comparing coordination models across frameworks.
Why this skill is useful
Provides detailed analysis frameworks and coordination models for multi-agent systems that the AI wouldn't reliably generate on its own.
What it needs
About 4k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Multi-Agent Analysis Analyzes coordination patterns in multi-agent systems. Process 1. Identify coordination model — Supervisor, peer-to-peer, pipeline 2. Document handoffs — How control transfers between agents 3. Classify state sharing — Blackboard vs message passing 4. Trace communication — Protocol and data flow Coordination Models Supervisor (Hierarchical) Characteristics: Central control point Clear routing logic Single point of failure Easy to understand Peer-to-Peer Characteristics: Decentralized Resilient to single failures Complex coordination Harder to debug Pipeline (Sequential) Characteristics: Clear data flow Easy to reason about Limited parallelism Each stage is a bottleneck Market-Based Characteristics: Dynamic allocation Self-organizing Overhead of bidding Complex to tune Handoff Mechanisms Explicit Transfer Router-Based Implicit (State-Based) State Sharing Patterns Blackboard (Shared Global State) Pros: Simple, full visibility Cons: Race conditions, tight coupling, hard to scale Message Passing (Isolated State) Pros: Isolation, clear boundaries, scalable Cons: More complex, async handling Hybrid Communication Protocol Analysis Direct Invocation Latency: Lowest Coupling: Highest Async: No Queue-Based Latency: Medium Coupling: Low Async: Yes Event-Driven Latency: Variable Coupling: Lowest Async: Yes Output Template Integration Prerequisite: codebase-mapping to identify agent files Feeds into: comparative-matrix for coordination decisions Related: control-loop-extraction for individual agent loops
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/multi-agent-analysis