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/running-agentsgit 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 | $0.02510 |
| Opus 5 | $0.00004 | $0.01255 |
| Sonnet 5 | $0.00002 | $0.00502 |
| Haiku 4.5 | $0.00001 | $0.00251 |
Grade A, and why
running-agents 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 running-agents — 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 — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Running Agents
The base Agent abstraction exposes various options for running the agent. Callers can choose to supply zero, one, or many input messages. Callers can also choose between streaming and non-streaming. Let's dig into the different usage scenarios.
Streaming and non-streaming
Microsoft Agent Framework supports both streaming and non-streaming methods for running an agent.
::: zone pivot="programming-language-csharp"
For non-streaming, use the RunAsync method.
Console.WriteLine(await agent.RunAsync("What is the weather like in Amsterdam?"));
For streaming, use the RunStreamingAsync method.
await foreach (var update in agent.RunStreamingAsync("What is the weather like in Amsterdam?"))
{
Console.Write(update);
}
::: zone-end ::: zone pivot="programming-language-python"
For non-streaming, use the run method.
result = await agent.run("What is the weather like in Amsterdam?")
print(result.text)
For streaming, use the run_stream method.
async for update in agent.run_stream("What is the weather like in Amsterdam?"):
if update.text:
print(update.text, end="", flush=True)
::: zone-end
Agent run options
::: zone pivot="programming-language-csharp"
The base agent abstraction does allow passing an options object for each agent run, however the ability to customize a run at the abstraction level is quite limited. Agents can vary significantly and therefore there aren't really common customization options.
For cases where the caller knows the type of the agent they are working with, it is possible to pass type specific options to allow customizing the run.
For example, here the agent is a ChatClientAgent and it is possible to pass a ChatClientAgentRunOptions object that inherits from AgentRunOptions.
This allows the caller to provide custom xref:Microsoft.Extensions.AI.ChatOptions that are merged with any agent level options before being passed to the IChatClient that
the ChatClientAgent is built on.
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 · 283 lines · 8 tokens per session scan A 90e5eae4b13d
running-agents 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 2,510 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 running-agents, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
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…
Transcribe
Batch audio/video transcription to SRT. Wraps /skill:video-transcription. Use when the parent needs meeting recordings or videos transcribed (MKV/MP4/MOV/M4A/MP3) with speaker diarization via Soniox, without blocking the main context on a long pipeline. Returns output paths + a short summary.
tool_creation
This guide covers the four ways to extend InitRunner with tools: built-in tools (contributing to InitRunner itself), custom tools (Python modules), declarative API tools (YAML-only), and the plugin registry (distributable packages).
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.