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 agents/managedcode/dotpilot/imagesgit clone --depth 1 https://github.com/managedcode/dotPilotWhat 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 | $0.00008 | $0.00933 |
| Opus 5 | $0.00004 | $0.00466 |
| Sonnet 5 | $0.00002 | $0.00187 |
| Haiku 4.5 | $0.00001 | $0.00093 |
Grade A, and why
images 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 yesterday.
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
100% identical to images — 0 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Using images with an agent
This tutorial shows you how to use images with an agent, allowing the agent to analyze and respond to image content.
Prerequisites
For prerequisites and installing NuGet packages, see the Create and run a simple agent step in this tutorial.
::: zone pivot="programming-language-csharp"
Passing images to the agent
You can send images to an agent by creating a ChatMessage that includes both text and image content. The agent can then analyze the image and respond accordingly.
First, create an AIAgent that is able to analyze images.
AIAgent agent = new AzureOpenAIClient(
new Uri("https://<myresource>.openai.azure.com"),
new AzureCliCredential())
.GetChatClient("gpt-4o")
.AsAIAgent(
name: "VisionAgent",
instructions: "You are a helpful agent that can analyze images");
Next, create a ChatMessage that contains both a text prompt and an image URL. Use TextContent for the text and UriContent for the image.
ChatMessage message = new(ChatRole.User, [
new TextContent("What do you see in this image?"),
new UriContent("https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg", "image/jpeg")
]);
Run the agent with the message. You can use streaming to receive the response as it is generated.
Console.WriteLine(await agent.RunAsync(message));
This will print the agent's analysis of the image to the console.
::: zone-end ::: zone pivot="programming-language-python"
Passing images to the agent
You can send images to an agent by creating a ChatMessage that includes both text and image content. The agent can then analyze the image and respond accordingly.
First, create an agent that is able to analyze images.
import asyncio
from agent_framework.azure import AzureOpenAIChatClient
from azure.identity import AzureCliCredential
agent = AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
name="VisionAgent",
instructions="You are a helpful agent that can analyze images"
)
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.
- yesterday First seen · 131 lines · 8 tokens per session scan A fa9ecb4ee8cb
images is an agent published in the GitHub repository managedcode/dotPilot (23 stars, last pushed 4mo ago), licensed MIT. It adds 8 tokens to every session and 933 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to images, differing in 0 lines, and is treated as a copy.
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