FreeSkills
Commits, branches and sync with the but CLI
- 361361 downloads
- 198198 likes
- 103103 saves
Works with
- Claude Code
- Codex
- Cursor
- and 3 more
Ray 2.57 for classic ML v1.0.0
Use this resource to write or review Ray 2.57 code for classic machine learning
New
- 578 downloads
- 155 likes
- 213 saves
Free
Install with the CLI
In a terminal, from the folder you want:
npx --yes palatekit@latest install ray-classic-ml-practicesWith no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.
First time: connect the CLI with
npx --yes palatekit@latest loginThe agent replaces outdated patterns (ray.air, tune.run, DatasetPipeline, RayServeHandle) with Ray 2.57 APIs. You get training, tuning and serving code that uses the current API rather than deprecated usage.
Mostly picked by
Updated November 2, 2026
Previews
Details
- Install guides
- Generates with
- Your agent's model
- Licence
- MIT
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What you get
- AGENTS.md: rules per block, from Ray Train to production
- references/data-streaming-replaced-pipelines.md: streaming replaces pipelines; references/core-state-api-not-ray-state.md: state API
Good fit for
- You are porting a scikit-learn or PyTorch training job to Ray
- Your Ray Serve service still uses old handles
Not for
Tags
- ray
- machine learning
- distributed
- kubernetes
About
You say
“Move this Ray Tune script to the Ray 2.57 API.”
Skill published by pproenca under the MIT license, delivered in its original version.
Show moreShow less
How to use it
- Install the skill in your agent's skills folder
- Ask your agent to write or review Ray 2.57 code for classic machine learning
- Review and adjust the result
Content preview
Files
- AGENTS.md8.5 KB
- assets/templates/_template.md0.9 KB
- LICENSE1.1 KB
- metadata.json3.1 KB
- NOTICE-PALATEKIT.md0.5 KB
- references/_sections.md3.1 KB
- references/core-classic-traps-residue.md1.8 KB
- references/core-object-store-sizing.md1.3 KB
- references/core-retries-system-failures-only.md1.1 KB
- references/core-state-api-not-ray-state.md1.1 KB
- references/data-compute-not-concurrency.md1.3 KB
- references/data-iter-torch-batches-not-to-torch.md1.2 KB
- references/data-override-num-blocks.md0.9 KB
- references/data-streaming-replaced-pipelines.md1.1 KB
- references/data-zero-copy-read-only-batches.md1.2 KB
- references/prod-baked-images-not-runtime-env.md1.3 KB
- references/prod-gcs-ft-redis.md1.4 KB
- references/prod-jobs-api-not-head-driver.md1.1 KB
- references/prod-kuberay-crd-choice.md1.3 KB
- references/prod-metrics-wiring.md1.5 KB
- references/serve-autoscaling-current-fields.md1.3 KB
- references/serve-deployment-response-handles.md1.4 KB
- references/serve-max-ongoing-requests.md1.2 KB
- references/serve-placement-controls.md1.3 KB
- references/serve-run-build-deploy.md1.3 KB
- references/train-configs-from-ray-train-elastic.md1.2 KB
- references/train-prepare-model-and-loader.md1.5 KB
- references/train-report-not-air-session.md1.8 KB
- references/train-v2-default-changed-semantics.md1.5 KB
- references/tune-driver-function-not-trainer.md2.1 KB
- references/tune-runconfig-from-ray-tune.md1.2 KB
- references/tune-tuner-canonical.md1.4 KB
- SKILL.md8.8 KB
Origin and licence
Listed unmodified
- Author
- pproenca
- Source
- github.com/pproenca/dot-skills/tree/cf93c57cac89d6fc3e4194686000411567f5caf3/skills/.experimental/ray
- Licence
- MIT
Notice
« ray » (pproenca, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.
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