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 agentmods add skills/versoxbt/claude-initial-setup/agent-communicationnpx skills add VersoXBT/claude-initial-setup --skill agent-communicationgit clone --depth 1 https://github.com/VersoXBT/claude-initial-setupWrote 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/versoxbt/claude-initial-setup/agent-communication)<a href="https://agentmods.dev/skills/versoxbt/claude-initial-setup/agent-communication"><img src="https://agentmods.dev/badge/skills/versoxbt/claude-initial-setup/agent-communication.svg" alt="Measured on agentmods" 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 | $0.00061 | $0.01705 |
| Opus 5 | $0.00030 | $0.00852 |
| Sonnet 5 | $0.00012 | $0.00341 |
| Haiku 4.5 | $0.00006 | $0.00170 |
Grade A, and why
agent-communication 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 5d 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 — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Communication
Patterns for communication between AI agents. Covers message passing, shared state, event-driven design, pub/sub, inbox/outbox, and structured message formats.
When to Use
- User is building multi-agent systems that need inter-agent communication
- User needs shared state between agents
- User wants event-driven agent coordination
- User is designing message formats for agent-to-agent data exchange
- User asks about pub/sub or inbox/outbox patterns for agents
Core Patterns
Direct Message Passing
Agents communicate through explicit function calls with typed messages.
from dataclasses import dataclass
from typing import Any
@dataclass(frozen=True)
class AgentMessage:
sender: str
recipient: str
msg_type: str # "request", "response", "notification"
payload: dict
correlation_id: str # Links requests to responses
class MessageBus:
def __init__(self):
self._handlers: dict[str, list] = {}
self._inbox: dict[str, list[AgentMessage]] = {}
def register(self, agent_id: str, handler):
self._handlers[agent_id] = handler
self._inbox[agent_id] = []
def send(self, message: AgentMessage):
self._inbox[message.recipient].append(message)
async def deliver(self, agent_id: str) -> list[AgentMessage]:
messages = self._inbox[agent_id]
self._inbox[agent_id] = []
return messages
# Usage
bus = MessageBus()
bus.send(AgentMessage(
sender="orchestrator",
recipient="researcher",
msg_type="request",
payload={"task": "Find recent papers on RAG optimization"},
correlation_id="task-001"
))
Shared State Store
Agents read and write to a shared state store for coordination.
import asyncio
from dataclasses import dataclass, field
@dataclass(frozen=True)
class StateEntry:
value: Any
updated_by: str
version: int
class SharedState:
def __init__(self):
self._state: dict[str, StateEntry] = {}
self._lock = asyncio.Lock()
self._watchers: dict[str, list] = {}
async def get(self, key: str) -> StateEntry | None:
return self._state.get(key)
async def put(self, key: str, value: Any, agent_id: str) -> StateEntry:
async with self._lock:
current = self._state.get(key)
version = (current.version + 1) if current else 1
entry = StateEntry(value=value, updated_by=agent_id, version=version)
self._state = {**self._state, key: entry} # Immutable update
# Notify watchers
for callback in self._watchers.get(key, []):
await callback(key, entry)
return entry
def watch(self, key: str, callback):
watchers = self._watchers.get(key, [])
self._watchers = {**self._watchers, key: [*watchers, callback]}
# Usage
state = SharedState()
await state.put("research_findings", {"papers": [...]}, agent_id="researcher")
await state.put("code_review", {"issues": [...]}, agent_id="reviewer")
# Another agent reads the state
findings = await state.get("research_findings")
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.
- 5d ago First seen · 230 lines · 61 tokens per session scan A 2f1d814ab350
agent-communication is a skill published in the GitHub repository VersoXBT/claude-initial-setup (4 stars, last pushed 3mo ago), licensed MIT. It adds 61 tokens to every session and 1,705 once invoked, about $0.0003 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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