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/dotnet-skills/running-agentsgit clone --depth 1 https://github.com/managedcode/dotnet-skillsWhat 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 | $0.03925 |
| Opus 5 | $0.00004 | $0.01962 |
| Sonnet 5 | $0.00002 | $0.00785 |
| Haiku 4.5 | $0.00001 | $0.00392 |
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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 421 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 method with stream=True. This returns a ResponseStream object that can be iterated asynchronously:
async for update in agent.run("What is the weather like in Amsterdam?", stream=True):
if update.text:
print(update.text, end="", flush=True)
ResponseStream
The ResponseStream object returned by run(..., stream=True) supports two consumption patterns:
Pattern 1: Async iteration — process updates as they arrive for real-time display:
response_stream = agent.run("Tell me a story", stream=True)
async for update in response_stream:
if update.text:
print(update.text, end="", flush=True)
Pattern 2: Direct finalization — skip iteration and get the complete response:
response_stream = agent.run("Tell me a story", stream=True)
final = await response_stream.get_final_response()
print(final.text)
Pattern 3: Combined — iterate for real-time display, then get the aggregated result:
response_stream = agent.run("Tell me a story", stream=True)
# First, iterate to display streaming output
async for update in response_stream:
if update.text:
print(update.text, end="", flush=True)
# Then get the complete response (uses already-collected updates, does not re-iterate)
final = await response_stream.get_final_response()
print(f"\n\nFull response: {final.text}")
print(f"Messages: {len(final.messages)}")
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.
- 2d ago First seen · 421 lines · 8 tokens per session scan A e18241156cca
running-agents is an agent published in the GitHub repository managedcode/dotnet-skills (477 stars, last pushed 2d ago), licensed MIT. It adds 8 tokens to every session and 3,925 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
observability
Prometheus-metrikker, OpenTelemetry-tracing, Grafana-dashboards og varsling.
research-agent
Utforsker kodebaser, undersøker problemer og samler kontekst før implementering.
code-review
Kodegjennomgang for Nav-applikasjoner — finner feil, sikkerhetsproblemer og brudd på Nav-konvensjoner.
rust-agent
Idiomatisk Rust-utvikling med cargo, clippy, error handling, async/tokio, unsafe og testing.
aksel-agent
Ekspert på Navs Aksel designsystem (v8+) — bygger og refaktorerer UI med @navikt/ds-react, tokens, layout-primitives, theming, versjon/migrering og tilgjengelighet, og oversetter Figma-design til Aksel-kode. Drevet av aksel-builder-skillen og Aksel MCP som fasit.
MAF Migration Agent
Use when migrating a .NET codebase to Microsoft Agent Framework (MAF) 1.3.0. Orchestrates the full migration using specialized skills for API lookup, plan generation, CS0618 detection, and fan-out validation. Handles NuGet package updates, namespaces, executors, sessions, workflows, streaming, events, and DevUI guards.