katib
Katib automatically runs hyper‑parameter tuning jobs on your Kubernetes cluster so you can find the best model settings without writing custom training loops.
- •You have a Kubernetes (or Kubeflow) cluster and need to scale hyper‑parameter searches across many pods.
- •Your training code is already containerized and you want a plug‑and‑play way to run Bayesian, grid, or random search.
- •You need experiment results (best parameters, metrics) stored as native Kubernetes objects for downstream CI/CD pipelines.
kubectl create -f https://raw.githubusercontent.com/kubeflow/katib/master/examples/v1beta1/xgboost-iris/experiment.yaml
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/katib && curl -fsSL https://workflowstacks.com/api/skills/katib/claude-skill -o ~/.claude/skills/katib/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
What's inside — free to inspect
Read the entire source before you build — unlike paid marketplaces that hide it behind a buy button.
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/katib && curl -fsSL https://workflowstacks.com/api/skills/katib/claude-skill -o ~/.claude/skills/katib/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
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