Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add latestaiagents/agent-skills --skill agent-mesh-architecturegit clone --depth 1 https://github.com/latestaiagents/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/latestaiagents/agent-skills/agent-mesh-architecture)<a href="https://agentmods.dev/skills/latestaiagents/agent-skills/agent-mesh-architecture"><img src="https://agentmods.dev/badge/skills/latestaiagents/agent-skills/agent-mesh-architecture/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/latestaiagents/agent-skills/agent-mesh-architecture"><img src="https://agentmods.dev/badge/skills/latestaiagents/agent-skills/agent-mesh-architecture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.02503 |
| Opus 5 | $0.00024 | $0.01252 |
| Sonnet 5 | $0.00010 | $0.00501 |
| Haiku 4.5 | $0.00005 | $0.00250 |
Grade A, and why
agent-mesh-architecture scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 403 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Mesh Architecture
Design resilient peer-to-peer agent networks where agents collaborate without central orchestration.
When to Use
- Resilience is critical (no single point of failure)
- Agents need to collaborate dynamically
- Tasks benefit from emergent behavior
- Scale varies (agents join/leave dynamically)
- Different perspectives improve outcomes
Architecture Overview
┌─────────┐ ┌─────────┐
│ Agent A │◄───►│ Agent B │
└────┬────┘ └────┬────┘
│ │
│ ┌───────┐ │
└──►│ Agent │◄──┘
│ C │
┌──►│ │◄──┐
│ └───────┘ │
┌────┴────┐ ┌────┴────┐
│ Agent D │◄───►│ Agent E │
└─────────┘ └─────────┘
Core Components
1. Agent Node
Each agent is an independent node with communication capabilities.
interface AgentNode {
id: string;
capabilities: string[];
state: AgentState;
// Communication
broadcast(message: Message): Promise<void>;
sendTo(agentId: string, message: Message): Promise<void>;
onMessage(handler: MessageHandler): void;
// Discovery
discoverPeers(): Promise<AgentNode[]>;
announceCapability(capability: string): void;
// Task handling
canHandle(task: Task): boolean;
handle(task: Task): Promise<Result>;
}
interface Message {
type: 'task_request' | 'task_result' | 'collaboration_invite' |
'capability_query' | 'heartbeat' | 'consensus_vote';
from: string;
to: string | 'broadcast';
payload: unknown;
timestamp: Date;
correlationId: string;
}
2. Message Bus / Communication Layer
interface MessageBus {
publish(topic: string, message: Message): Promise<void>;
subscribe(topic: string, handler: MessageHandler): Subscription;
request(agentId: string, message: Message): Promise<Message>;
}
// In-memory implementation for local agents
class LocalMessageBus implements MessageBus {
private subscribers = new Map<string, MessageHandler[]>();
async publish(topic: string, message: Message) {
const handlers = this.subscribers.get(topic) || [];
await Promise.all(handlers.map(h => h(message)));
}
subscribe(topic: string, handler: MessageHandler) {
const handlers = this.subscribers.get(topic) || [];
handlers.push(handler);
this.subscribers.set(topic, handlers);
return {
unsubscribe: () => {
const idx = handlers.indexOf(handler);
if (idx >= 0) handlers.splice(idx, 1);
}
};
}
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 403 lines · 49 tokens per session scan A 4a5f1e1c2880
agent-mesh-architecture is a skill published in the GitHub repository latestaiagents/agent-skills (5 stars, last pushed 4mo ago), licensed MIT. It adds 49 tokens to every session and 2,503 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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