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 Ghosteken/agent-harness --skill agents-v2-pygit clone --depth 1 https://github.com/Ghosteken/agent-harnessWrote 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/ghosteken/agent-harness/agents-v2-py)<a href="https://agentmods.dev/skills/ghosteken/agent-harness/agents-v2-py"><img src="https://agentmods.dev/badge/skills/ghosteken/agent-harness/agents-v2-py/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/ghosteken/agent-harness/agents-v2-py"><img src="https://agentmods.dev/badge/skills/ghosteken/agent-harness/agents-v2-py.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 | $0.00039 | $0.02130 |
| Opus 5.5 | $0.00016 | $0.00852 |
| Sonnet 5.5 | $0.00008 | $0.00426 |
| Haiku 4.5 | $0.00004 | $0.00213 |
Grade A, and why
agents-v2-py 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 1mo 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.
This is a copy
97% identical to agents-v2-py — 137 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 332 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure AI Hosted Agents (Python)
Build container-based hosted agents using ImageBasedHostedAgentDefinition from the Azure AI Projects SDK.
Installation
pip install azure-ai-projects>=2.0.0b3 azure-identity
Minimum SDK Version: 2.0.0b3 or later required for hosted agent support.
Environment Variables
AZURE_AI_PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
Prerequisites
Before creating hosted agents:
- Container Image - Build and push to Azure Container Registry (ACR)
- ACR Pull Permissions - Grant your project's managed identity
AcrPullrole on the ACR - Capability Host - Account-level capability host with
enablePublicHostingEnvironment=true - SDK Version - Ensure
azure-ai-projects>=2.0.0b3
Authentication
Always use DefaultAzureCredential:
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
credential = DefaultAzureCredential()
client = AIProjectClient(
endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
credential=credential
)
Core Workflow
1. Imports
import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import (
ImageBasedHostedAgentDefinition,
ProtocolVersionRecord,
AgentProtocol,
)
2. Create Hosted Agent
client = AIProjectClient(
endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
credential=DefaultAzureCredential()
)
agent = client.agents.create_version(
agent_name="my-hosted-agent",
definition=ImageBasedHostedAgentDefinition(
container_protocol_versions=[
ProtocolVersionRecord(protocol=AgentProtocol.RESPONSES, version="v1")
],
cpu="1",
memory="2Gi",
image="myregistry.azurecr.io/my-agent:latest",
tools=[{"type": "code_interpreter"}],
environment_variables={
"AZURE_AI_PROJECT_ENDPOINT": os.environ["AZURE_AI_PROJECT_ENDPOINT"],
"MODEL_NAME": "gpt-4o-mini"
}
)
)
print(f"Created agent: {agent.name} (version: {agent.version})")
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.
- 1mo ago First seen · 332 lines · 39 tokens per session scan A 764fffdebaf0
agents-v2-py is a skill published in the GitHub repository Ghosteken/agent-harness (4 stars, last pushed yesterday), licensed MIT. It adds 39 tokens to every session and 2,130 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to agents-v2-py, differing in 137 lines, and is treated as a copy.
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