performance-diagnostics-dotnet
Skill4 starsChanged 51 days agoMITView on GitHub
Use when investigating or improving measured .NET performance, allocations, garbage collection, thread-pool starvation, latency, or throughput.
In this repository37 other files it gives its agents.
- global-instructions/AGENTS.mdAGENTS.md
- api-contract-safetySkill
- api-endpoint-deprecationSkill
- aspnet-core-api-developmentSkill
- background-services-dotnetSkill
- bug-investigationSkill
- code-qualitySkill
- code-reviewSkill
- context-resetSkill
- dependency-managementSkill
- deployment-readiness-dotnetSkill
- diff-reviewSkill
- distributed-workflowsSkill
- dotnet-architectureSkill
- dotnet-implementationSkill
- dotnet-securitySkill
- ef-core-safetySkill
- exception-handlingSkill
- git-workflowSkill
- google-bigquery-dotnetSkill
- google-cloud-pubsub-dotnetSkill
- google-firestore-dotnetSkill
- http-client-integrationsSkill
- integration-testing-dotnetSkill
- observability-dotnetSkill
- poc-developmentSkill
- production-debuggingSkill
- production-hotfixSkill
- production-loggingSkill
- redis-dotnetSkill
- relational-database-dotnetSkill
- retry-resilienceSkill
- safe-terminalSkill
- shared-code-impactSkill
- targeted-validationSkill
- task-routerSkill
- unit-testingSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 10
Use existing profiling tools and representative workloads.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Performance Diagnostics Dotnet
Establish the workload, baseline, target, environment and measurement method before changing code. Separate CPU, allocation/GC, contention, thread-pool, database and external dependency hypotheses using evidence.
Use existing profiling tools and representative workloads. Choose bounded diagnostic collection appropriate to the environment; production dump capture may include sensitive data and requires appropriate authorization. Compare like-for-like measurements and account for warmup, variability and collection overhead.
Change the smallest proven bottleneck while preserving behavior. Verify correctness and measure again. Avoid speculative micro-optimizations, premature pooling, or adding complexity unsupported by measured benefit. Report measurements, environment and residual uncertainty.
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