Provision, monitor, and tear down AWS EC2 GPU instances for ML/CUDA development
1 on GitHubby promptrompv0.20.0MITupdated 8 days ago
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Plugin aws-bootstrap-skill from marketplace aws-bootstrap-skill in promptromp/aws-bootstrap-g4dn. Commands read from the repo's manifest — 7 days ago.
What it installs
Skills (1)
aws-bootstrap
Use when the user wants to provision AWS GPU instances (g4dn/g5/g6/p-series spot or on-demand) for CUDA/PyTorch/ML work, check GPU instance status or cost, SSH into a remote GPU box, terminate instances, manage EBS data volumes, clean up cloud resources, check or request GPU vCPU quotas, or stand up
Statically analyze a local JS/Electron app you're authorized to study, with file+line evidence, and build an original reimplementation of what you find. Experimental. Phase 1: JS/Electron static analysis. Phase 2: native ELF/PE/Mach-O container inspection. Phase 3: .NET assembly metadata. Phase 4: EVM bytecode inventory. Phase 5: CUDA fatbin, cubin and PTX inventory. Parses, never executes. Dynamic analysis is a future phase, not yet implemented.
Connect Claude to Prowler Cloud through the Prowler MCP server. Reads providers, scans, findings, compliance results and resources for AWS, Azure, GCP, Kubernetes, Microsoft 365, GitHub and the other providers listed at https://registry.prowler.com, and returns remediation guidance for failed checks. Writes are limited to Prowler Cloud itself: triggering scans, finding triage, mute rules and the mutelist, providers and integrations. Your cloud resources are not changed by the connector.
1 skill · 1 MCP server
prowler-cloud/prowler 15,234
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