The official Genkit skill for Node.js and TypeScript — flows, Dotprompt files, tools and the beta agent API with sessions, interrupts and branching.
.NET Crash Dump Collection
Summary
The .NET team's official skill for capturing crash dumps from CoreCLR and NativeAOT apps on Linux, macOS, Windows and inside containers — collection only, no analysis.
Features
- CoreCLR vs NativeAOT detection from a binary or a live process
- Automatic crash-dump configuration and on-demand collection
- Docker and Kubernetes dump collection, including required capabilities
- Hard stop signals: no analysis, no code changes, no .NET Framework
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Skill Content
Usage Instructions
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Example Usage
"Our ASP.NET Core pod is crash-looping in Kubernetes. Configure it so the next crash writes a full dump to a persistent volume."
Description
Getting a usable crash dump out of a .NET process is a configuration problem, not a debugging one, and the configuration differs by runtime, operating system and whether the process is in a container. Guessing wrong usually means the crash happens again with nothing to show for it.
This official skill from the .NET team walks the decision explicitly. First it establishes the scenario — enable automatic dumps on crash, or capture from a live process — then the platform and the runtime, with concrete commands for telling CoreCLR from NativeAOT from a binary (CorExeMain versus Redhawk Rhp symbols) or from a running process on Linux, macOS and Windows. From there it routes to the right reference: DOTNET_DbgEnableMiniDump environment configuration, dotnet-dump collect, createdump, and the Docker and Kubernetes specifics such as volume mounts and SYS_PTRACE.
What makes it unusually well-behaved as an agent skill is the stop signals section. The skill refuses to open, triage or root-cause a dump, refuses to install analysis tooling such as dotnet-dump analyze or WinDbg, refuses to modify application code, and bails out entirely if the process turns out to be .NET Framework. It reports where the dump landed and stops — which is exactly what you want from a step that runs inside a larger incident workflow.
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