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/run-agentgit 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 | $0.00012 | $0.02081 |
| Opus 5 | $0.00006 | $0.01040 |
| Sonnet 5 | $0.00002 | $0.00416 |
| Haiku 4.5 | $0.00001 | $0.00208 |
Grade A, and why
run-agent 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 run-agent — 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 — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create and run an agent with Agent Framework
::: zone pivot="programming-language-csharp"
This tutorial shows you how to create and run an agent with Agent Framework, based on the Azure OpenAI Chat Completion service.
[!IMPORTANT] Agent Framework supports many different types of agents. This tutorial uses an agent based on a Chat Completion service, but all other agent types are run in the same way. For more information on other agent types and how to construct them, see the Agent Framework user guide.
Prerequisites
Before you begin, ensure you have the following prerequisites:
- .NET 8.0 SDK or later
- Azure OpenAI service endpoint and deployment configured
- Azure CLI installed and authenticated (for Azure credential authentication)
- User has the
Cognitive Services OpenAI UserorCognitive Services OpenAI Contributorroles for the Azure OpenAI resource.
[!NOTE] Microsoft Agent Framework is supported with all actively supported versions of .NET. For the purposes of this sample, we recommend the .NET 8 SDK or a later version.
[!IMPORTANT] This tutorial uses Azure OpenAI for the Chat Completion service, but you can use any inference service that provides a xref:Microsoft.Extensions.AI.IChatClient implementation.
Install NuGet packages
To use Microsoft Agent Framework with Azure OpenAI, you need to install the following NuGet packages:
dotnet add package Azure.AI.OpenAI --prerelease
dotnet add package Azure.Identity
dotnet add package Microsoft.Agents.AI.OpenAI --prerelease
Create the agent
- First, create a client for Azure OpenAI by providing the Azure OpenAI endpoint and using the same login as you used when authenticating with the Azure CLI in the Prerequisites step.
- Then, get a chat client for communicating with the chat completion service, where you also specify the specific model deployment to use. Use one of the deployments that you created in the Prerequisites step.
- Finally, create the agent, providing instructions and a name for the agent.
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 · 264 lines · 12 tokens per session scan A 5ba32b7af1a7
run-agent is an agent published in the GitHub repository managedcode/dotPilot (23 stars, last pushed 4mo ago), licensed MIT. It adds 12 tokens to every session and 2,081 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to run-agent, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
knowledge-ops
Executes knowledge management operations under CKO direction. Audits consistency, distributes learnings, optimizes memory files, and detects knowledge gaps across the agent fleet.
Explore
Fast read-only codebase & docs exploration. Returns structured findings, never raw file dumps.
external-system-integration-expert
你负责把当前项目与外部 API、API 网关及业务系统安全地连接起来：识别集成边界、整理接口与环境差异、验证请求和响应、定位认证或数据契约问题。.
Audit
Deep security + performance audit of a specific diff. Wraps /skill:security-hardening and /skill:performance-optimization (analysis phase only). Use when a change touches auth, untrusted input, secrets, webhooks, PII, or a latency/throughput budget — a focused, read-only risk pass that returns findings the parent…
registry
InitRunner's role registry lets you install, share, and discover roles from InitHub and OCI registries. Roles are downloaded, validated, and saved to /.initrunner/roles/ where they integrate automatically with the CLI.
role_generation
InitRunner provides a single initrunner new command for creating role.yaml files. It supports multiple seed modes (templates, AI generation, examples, hub bundles, or local files) and an interactive refinement loop for iterating on the YAML before saving. Run with no arguments in a terminal and it shows a guided start…