Agent Skills: sre-expert
>-
ID: personamanagmentlayer/pcl/sre-expert
4011
Install this agent skill to your local
Skill Files
The files that make up sre-expert.
Loading file tree…
More agent skills
- write-exampleWriting examples for the tldraw SDK examples app. Use when creating new examples, adding SDK demonstrations, or writing example code in apps/examples.
- hf-cloud-sagemaker-iam-preflightEnsure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.
- render-envAdd a new Terraform Cloud variable for the Render-hosted backend across production, sandbox, and test. Declares the tfe_variable in terraform/global/{production,sandbox,test}.tf and the matching variable {} block in terraform/{production,sandbox,test}/variables.tf, then reminds the user to wire it into render.tf / the render_service module.
- vector-forgeMutation-driven test vector generation. Finds implementations of a cryptographic algorithm or protocol, runs mutation testing to identify escaped mutants, then generates new test vectors that deliberately exercise the uncovered code paths. Compares before/after mutation kill rates to prove vector effectiveness. Use when generating cryptographic test vectors, measuring Wycheproof coverage gaps, finding escaped mutants via mutation testing, creating cross-implementation test suites, or improving test vector coverage for crypto primitives.
- ci-metricsQuery PyTorch CI, GitHub Actions, HUD, Grafana, and infrastructure metrics. Use when users ask about CI duration, job failures, queue times, workflow trends, runner health, dashboard data, or PyTorch infrastructure metrics.
- variance-analysisDecompose financial variances into drivers with narrative explanations and waterfall analysis. Use when analyzing budget vs. actual, period-over-period changes, revenue or expense variances, or preparing variance commentary for leadership.