Opinionated DevOps and platform engineering skills from a Docker Captain: Dockerfiles and Compose, GitHub Actions workflows and audits, production Kubernetes pod specs, recent Kubernetes features, and local linting setup. Tested with evals.
Prices what your coding agent is about to do before it runs: asks before costly launches and destroys, tracks AI spend, and answers cost questions across AWS, Azure, GCP, Kubernetes and AI providers. Local, read-only. Includes /nable:guard and /nable:connect. Credentials stay on your machine.
Deploy a web app to Vercel or Render and manage its env vars through APIs and GitHub Actions instead of clicking a dashboard: one-token Vercel + Actions setup, Vercel REST env/deploy state, Render GHCR-image services with commit-tagged releases, Supabase migration-ledger repair, and proving the live site is the commit you pushed.
Secure SSH bridge for remote server management — 476 tools across 77 groups (Linux, Windows, Docker, Kubernetes, cloud) over one SSH connection. CLI-first for 10-32x token savings vs MCP; MCP transport also available.
Keeps a Mac that runs Claude Code agents from filling its disk — daily LaunchAgent + MCP server that clear regenerable caches and reap stale, clean, pushed git worktrees (plan first, apply on confirm)
Research Nebius AI Cloud documentation and operate resources: inventory, capacity/quota checks, gated provisioning. Read-only by default; destructive operations refused.
Skills for Redpanda's five products (Streaming, SQL, Connect, Cloud, Agentic Data Plane) plus the rpk CLI, grounded in Redpanda source, docs, and APIs.
Install every llm-d skill at once. Ships no skills of its own, it only pulls in the eight individual plugins, so it can be combined with them without duplicating anything.
Create or prepare GKE infrastructure (cluster, GPU node pools, RDMA/RoCE networking, DRA and DRANET drivers, Gateway API prerequisites) so that an llm-d stack can be deployed on it. Use this skill when users want to provision a new GKE cluster for llm-d, or make an existing GKE cluster llm-d-ready, especially for prefill/decode disaggregation or wide expert parallelism which require RDMA. Stops at a cluster that is ready for llm-d: install deploy-llm-d (config already decided) or llm-d-autoconfig (config to be recommended) to deploy onto it.
Configure an EPP EndpointPickerConfig for an llm-d deployment based on workload + SLA inputs, then optionally deploy it to the user's cluster. Activate when the user asks about configuring llm-d, setting up the inference scheduler, tuning EPP plugins, choosing what config to use for a model or workload, or deploying llm-d. Owns config generation and its own deploy path; install the single-purpose plugins (deploy-llm-d, run-llm-d-benchmark, teardown-llm-d, configure-wva-autoscaling-llm-d) for standalone operations on a config you already have.