By tonone-ai
Generates production-grade infrastructure as code for services or projects, assessing scale stage and choosing between managed platforms (Fly.io, Render) or Terraform/Pulumi on AWS/GCP.
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
npx claudepluginhub tonone-ai/tonone --plugin forge-infraInfrastructure engineer — cloud services, networking, IaC, cost optimization
Generate Infrastructure as Code for Terraform, CloudFormation, Pulumi, and more
Infrastructure and deployment plugin for Arness with progressive zero-config init — auto-configures with sensible defaults on first skill invocation, no upfront ceremony required. 23 skills and 9 agents covering containerization, IaC generation, deployment, CI/CD pipelines, environment management, secrets, monitoring, migration, and structured change management pipeline. Can operate standalone or alongside the arn-code plugin.
Terraform module creation and infrastructure planning
Terraform infrastructure as code toolkit with module scaffolding, state management, cost estimation, and upgrade assistance. Includes strategic IaC architect agent.
Infrastructure management for AWS, Kubernetes, Docker, Helm, Fly.io
Design and build networking infrastructure — VPCs, subnets, DNS, load balancers, firewall rules. Use when asked to "set up networking", "VPC design", "configure DNS", "load balancer setup", "network architecture", or "firewall rules".
Generate onboarding documentation — what this project does, how to set up locally, where things live, key decisions, how to deploy. Written for day-one engineers who know nothing. Use when asked for "onboarding docs", "new engineer guide", "how to get started", or "developer setup".
Implement a reusable, accessible, typed component from a design spec. Use when asked to "create a component", "build a widget", "implement this design", or "reusable UI element".
Verify observability posture — audit monitoring coverage, find blind spots, prioritize gaps. Use when asked "is monitoring sufficient", "observability review", "are we covered", or "pre-launch monitoring check".
ML reconnaissance — inventory all models, pipelines, data sources, and monitoring. Use when asked "what ML do we have", "model inventory", or "ML assessment".