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/fetchai/agentverse-skills/agentverse-deploynpx skills add fetchai/agentverse-skills --skill agentverse-deploygit clone --depth 1 https://github.com/fetchai/agentverse-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/fetchai/agentverse-skills/agentverse-deploy)<a href="https://agentmods.dev/skills/fetchai/agentverse-skills/agentverse-deploy"><img src="https://agentmods.dev/badge/skills/fetchai/agentverse-skills/agentverse-deploy.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 5.1 | $0.00065 | $0.00938 |
| Opus 5 | $0.00032 | $0.00469 |
| Sonnet 5 | $0.00013 | $0.00188 |
| Haiku 4.5 | $0.00006 | $0.00094 |
Grade A, and why
agentverse-deploy scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
allowed-tools: Read Bash(python3 *) Bash(curl *) Bash(pip install requests) How it starts
The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentverse Deploy
Overview
Deploy Python code as a hosted agent on Agentverse. The agent runs on Fetch.ai's infrastructure — no server needed. Creates the agent, uploads code in the correct format, and optionally starts it.
When to Use
- User asks to "deploy this as an agent on Agentverse"
- User asks to "host this code on Agentverse"
- User asks to "create a new hosted agent"
- User has Python agent code they want to run on Agentverse
Prerequisites
AGENTVERSE_API_KEYenvironment variable set- Python 3.8+ with
requests
Quick Steps
1. Deploy from a file
python3 scripts/deploy_agent.py --name "my-agent" --file ./my_agent_code.py --start
2. Deploy inline code
python3 scripts/deploy_agent.py --name "hello-agent" --code '
@agent.on_event("startup")
async def hello(ctx):
ctx.logger.info("Hello from my agent!")
'
3. Parse the result
{
"status": "success",
"name": "my-agent",
"address": "agent1q...",
"running": true
}
Critical: Hosted Agent Code Rules
Your code MUST follow these rules for the hosted environment:
- DO NOT create an
Agent()instance —agentis pre-created by the platform - DO NOT call
agent.run()— the platform manages the lifecycle - DO use
@agent.on_event("startup")for initialization - DO use
ctx.logger.info()for output (no print/stdout) - DO use
Protocolobjects andagent.include()for message handling
Valid hosted agent template:
from uagents import Context, Protocol
@agent.on_event("startup")
async def startup(ctx: Context):
ctx.logger.info(f"Agent started: {ctx.agent.address}")
@agent.on_interval(period=60.0)
async def periodic(ctx: Context):
ctx.logger.info("Running periodic task...")
With Chat Protocol:
from datetime import datetime
from uuid import uuid4
from uagents import Context, Protocol
from uagents_core.contrib.protocols.chat import (
ChatMessage, ChatAcknowledgement, TextContent, chat_protocol_spec
)
protocol = Protocol(spec=chat_protocol_spec)
@protocol.on_message(ChatMessage)
async def handle(ctx: Context, sender: str, msg: ChatMessage):
response = ChatMessage(
timestamp=datetime.now(), msg_id=uuid4(),
content=[TextContent(type="text", text="Hello! I received your message.")]
)
await ctx.send(sender, response)
agent.include(protocol, publish_manifest=True)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 129 lines · 65 tokens per session scan A 7706b8b0f11a
agentverse-deploy is a skill published in the GitHub repository fetchai/agentverse-skills (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 65 tokens to every session and 938 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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