Technology Selection
dotnet/skills
Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX…
Production Ray (open-source, pinned to 2.57) for classic-ML workloads from training to serving — Ray Train, Tune, Data, Serve, Core, and cluster deployment on KubeRay.
Install Ray
$ npx skills add pproenca/dot-skills --skill ray -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install pproenca/dot-skills ray --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.experimental/ray .claude/skills/ray && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "ray" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/ray into .claude/skills/ray/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ray", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/rayType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add pproenca/dot-skills --skill ray -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install pproenca/dot-skills ray --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/.experimental/ray .agents/skills/ray && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ray" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/ray into .agents/skills/ray/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ray", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add pproenca/dot-skills --skill ray -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install pproenca/dot-skills ray --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/.experimental/ray .cursor/skills/ray && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ray" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/ray into .cursor/skills/ray/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ray", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/pproenca/dot-skills.git --path skills/.experimental/ray--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add pproenca/dot-skills --skill ray -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install pproenca/dot-skills ray --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/.experimental/ray .gemini/skills/ray && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ray" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/ray into .gemini/skills/ray/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ray", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install pproenca/dot-skills rayInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add pproenca/dot-skills --skill ray -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/.experimental/ray .github/skills/ray && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ray" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/ray into .github/skills/ray/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ray", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add pproenca/dot-skills --skill ray -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install pproenca/dot-skills ray --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/.experimental/ray .opencode/skills/ray && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ray" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/ray into .opencode/skills/ray/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ray", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
ray- GitHub stars
- 214
- Token cost
- ~2.2k tokens
- SKILL.md length
- 691 words
- Files
- 31 (incl. references, assets)
- Skills in repo
- 182
- Repo updated
- First seen
- Licence
- MIT
At a glance
Production Ray (open-source, pinned to 2.57) for classic-ML workloads from training to serving — Ray Train, Tune, Data, Serve, Core, and cluster deployment on KubeRay.
- Works in 6 steps: Ray Train → Ray Serve → Ray Data → …
- Productionizing Python code that touches Ray distributed training
- SKILL.md covers When to Apply, Rule Categories, Quick Reference and How to Use, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Ray is an agent skill from pproenca/dot-skills. Production Ray (open-source, pinned to 2.57) for classic-ML workloads from training to serving — Ray Train, Tune, Data, Serve, Core, and cluster deployment on KubeRay. Corrects the older-corpus defaults a model reaches for (ray.air session reporting, Trainer-inside-Tuner, tune.run, mapbatches concurrency=, DatasetPipeline/totorch, maxconcurrentqueries, RayServeHandle + ray.get, Deployment.deploy, ray.state, ray.get-in-a-loop) with the 2.57 idioms that replaced them (Train V2 defaults, driver-function tuning…
Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 33 other files, including reference files and assets (for example `AGENTS.md`, `assets/templates/_template.md` and `metadata.json`).
It sits in AI & LLM Engineering, covering Data pipelines and ETL, LLM inference and serving and Deep learning. It works with Python. The repository describes itself as: A collection of AI agent skills following the Agent Skills open format. The licence is MIT.
When your agent uses it
- Productionizing Python code that touches Ray distributed training
- Hyperparameter tuning
- Ray cluster operations
Example prompts
- “/ray”
Requirements
- Python 3
Workflow steps
6 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit cf93c57. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Ray loads about 2.2k tokens when it runs, and up to ~12k if it reads all its reference files. Until then it costs about 228 tokens; SKILL.md has 691 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from pproenca/dot-skills at commit cf93c57, republished under its MIT licence (© pproenca). 691 words, ~2,197 tokens.
Download SKILL.mdSave it as
.claude/skills/ray/SKILL.md (or your agent's skills folder). This skill also uses 30 other files; get the full folder from GitHub.- name
- ray
- description
- Production Ray (open-source, pinned to 2.57) for classic-ML workloads from training to serving — Ray Train, Tune, Data, Serve, Core, and cluster deployment on KubeRay. Corrects the older-corpus defaults a model reaches for (ray.air session reporting, Trainer-inside-Tuner, tune.run, map_batches concurrency=, DatasetPipeline/to_torch, max_concurrent_queries, RayServeHandle + ray.get, Deployment.deploy, ray.state, ray.get-in-a-loop) with the 2.57 idioms that replaced them (Train V2 defaults, driver-function tuning, compute strategies, streaming datasets, DeploymentHandle/DeploymentResponse, serve build/deploy, ray.util.state, KubeRay CRDs and Jobs API). Use when writing, reviewing, or productionizing Python code that touches Ray distributed training, data pipelines, hyperparameter tuning, model serving, or Ray cluster operations. LLM serving/batch-inference on Ray lives in the sibling ray-llm skill.
Ray
Library-reference skill for production, open-source Ray — 26 rules across 6 categories covering the path from training to serving. Ray's API surface churned hard through the 2.x line (Train V2 became the default, Serve removed parameters and handle classes outright, Ray Data reversed a deprecation), so a model fluent in the older corpus produces code that warns, errors, or silently means something else. Each rule names the wrong default it corrects; there is no rule for things a capable model already gets right.
Scope is classic-ML Ray on self-hosted/KubeRay clusters. LLM serving and batch inference (ray.serve.llm, ray.data.llm) are the sibling ray-llm skill.
Pinned to ray 2.57.0 (Python ≥ 3.10). API claims were verified against the unpacked 2.57.0 wheel; classic-ML examples were exercised on a live local Ray 2.57.0 runtime.
When to Apply
- Writing or reviewing distributed training code —
TorchTrainer,ScalingConfig, checkpointing, fault tolerance - Building data pipelines with Ray Data — reads,
map_batches, GPU inference pools, training ingest - Running hyperparameter sweeps with Ray Tune, especially combined with Ray Train
- Writing or reviewing Ray Serve deployments — scaling, handles, composition, production config
- Using Ray Core primitives directly — tasks, actors, object store, retries
- Standing up or reviewing production Ray infrastructure — KubeRay CRDs, job submission, fault tolerance, observability
Rule Categories
| # | Category | Prefix | Covers |
|---|---|---|---|
| 1 | Ray Train | train- | Train V2 as the default (deprecated config fields), ray.train.report over ray.air session, config imports and elastic scaling, the prepare_model/prepare_data_loader wrappers |
| 2 | Ray Serve | serve- | max_ongoing_requests (old name removed), current autoscaling fields, DeploymentResponse handles, serve.run/build/deploy lifecycle, replica placement options |
| 3 | Ray Data | data- | compute= strategies (the concurrency reversal), override_num_blocks, streaming execution replacing pipelines, torch ingest, zero-copy read-only batches |
| 4 | Ray Core | core- | The classic anti-patterns' non-obvious residue, retry/restart defaults, object store & /dev/shm sizing, ray.util.state |
| 5 | Ray Tune | tune- | Tuner as canonical (with tune.run's true status), ray.tune.RunConfig imports, the driver-function Train integration |
| 6 | Production & Clusters | prod- | KubeRay CRD choice, Jobs API submission, GCS fault tolerance with Redis, baked images vs runtime_env, Prometheus/Grafana wiring |
Quick Reference
1. Ray Train
train-v2-default-changed-semantics— V2 is the default since 2.51;sync_config/verbose/fail_fastare gonetrain-report-not-air-session—ray.train.report(metrics, checkpoint=);ray.airis a legacy shimtrain-configs-from-ray-train-elastic— V2 config imports, elasticnum_workers=(min, max), case-sensitive resource keystrain-prepare-model-and-loader— withoutprepare_model/prepare_data_loader, N workers train N unsynchronized copies
2. Ray Serve
serve-max-ongoing-requests—max_concurrent_queriesis removed;max_ongoing_requests(default 5) +max_queued_requestsserve-autoscaling-current-fields—target_ongoing_requests,*_factorfields,num_replicas="auto", scale-to-zeroserve-deployment-response-handles—DeploymentResponse.result()/await, neverray.get; composition via.bind()serve-run-build-deploy—serve.run+serve build/deploy;Deployment.deploy()era is removedserve-placement-controls—max_replicas_per_node, per-replica placement groups, gang scheduling, request routers
Show full SKILL.md (287 more words)Show less
3. Ray Data
data-compute-not-concurrency—concurrency=is deprecated (again);compute=ActorPoolStrategy/TaskPoolStrategydata-override-num-blocks—parallelismis deprecated in read APIsdata-streaming-replaced-pipelines—DatasetPipeline/window/repeatremoved; execution streams on consumptiondata-iter-torch-batches-not-to-torch—to_torchis gone;iter_torch_batchesand Train dataset shardsdata-zero-copy-read-only-batches— batches are read-only views by default; set an explicitbatch_size
4. Ray Core
core-classic-traps-residue— the non-obvious residue of the classic anti-patterns:ray.waitdraining, closure/global copies, few-ms task floorcore-retries-system-failures-only—retry_exceptionsis opt-in; actors don't restart by defaultcore-object-store-sizing— 30% default,/dev/shmin containers, spilling to local diskcore-state-api-not-ray-state—ray.stateis gone;ray.util.stateis the introspection API
5. Ray Tune
tune-tuner-canonical—Tuneris the API;tune.runis legacy but not removedtune-runconfig-from-ray-tune— Tuner takesray.tune.RunConfig, notray.train's orray.air'stune-driver-function-not-trainer— Trainer-inside-Tuner is deprecated; use a driver function +with_resources
6. Production & Clusters
prod-kuberay-crd-choice— RayCluster vs RayJob (shutdownAfterJobFinishesdefaults false) vs RayServiceprod-jobs-api-not-head-driver—ray job submitfor long-lived clusters; Ray Client is a dev toolprod-gcs-ft-redis— head-crash survival for RayService needs external Redis GCSprod-baked-images-not-runtime-env— images carry prod dependencies;runtime_env(now incl.uv,image_uri) is for iterationprod-metrics-wiring— the dashboard needs external Prometheus/Grafana to show time series
How to Use
Read a reference file when its decision comes up. Each rule names the wrong default it corrects, then shows the canonical way (with an incorrect/correct contrast only where the wrong way is a real trap).
- Section definitions — category structure
- Rule template — for adding new rules
- AGENTS.md — auto-built table of contents across all rules
Related Skills
ray-llm— the sibling rule pack for LLM serving (ray.serve.llm) and batch inference (ray.data.llm) on Raymlflow-3— experiment tracking and model registry; pairs with Ray Train for the tracking side of the MLOps cycle
Reference Files
| File | Description |
|---|---|
| references/_sections.md | Category definitions and ordering |
| assets/templates/_template.md | Template for new rules |
| metadata.json | Version and source references |
© pproenca, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 30 other files (references, assets) in skills/.experimental/ray of pproenca/dot-skills.
- SKILL.md
- AGENTS.md
- assets/templates/_template.md
- metadata.json
- references/_sections.md
- references/core-classic-traps-residue.md
- references/core-object-store-sizing.md
- references/core-retries-system-failures-only.md
- references/core-state-api-not-ray-state.md
- references/data-compute-not-concurrency.md
- references/data-iter-torch-batches-not-to-torch.md
- references/data-override-num-blocks.md
- references/data-streaming-replaced-pipelines.md
- references/data-zero-copy-read-only-batches.md
- references/prod-baked-images-not-runtime-env.md
- references/prod-gcs-ft-redis.md
- references/prod-jobs-api-not-head-driver.md
- references/prod-kuberay-crd-choice.md
- … and 13 more
Open the folder on GitHubat commit cf93c57
Compare with similar skills
Ray next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Ray this skillpproenca/dot-skills | 214 | — | ~2.2k | Automated safety check: Pass | MIT | |
| Technology Selectiondotnet/skills | 5.6k | 2 repos | ~2.1k | Automated safety check: Pass | MIT | |
| vLLM Model ServingOrchestra-Research/AI-Research-SKILLs | 13k | 6 repos | ~2.3k | Automated safety check: Pass | MIT | |
| RWKV Architecture GuideOrchestra-Research/AI-Research-SKILLs | 13k | 3 repos | ~1.8k | Automated safety check: Pass | MIT | |
| Vllm Deploy K8svllm-project/vllm-skills | 103 | — | ~2k | Automated safety check: Pass | Apache-2.0 | |
| Upgrading Mwaa Environmentsaws/agent-toolkit-for-aws | 2.8k | — | ~7.3k | Automated safety check: Pass | Apache-2.0 |
Similar skills
- Official
vLLM Model Serving
Orchestra-Research/AI-Research-SKILLs
Deploys LLMs with vLLM for high-throughput serving, covering the OpenAI-compatible server, offline batch inference, monitoring and a Docker rollout.
RWKV Architecture Guide
Orchestra-Research/AI-Research-SKILLs
Explains RWKV, a hybrid that trains in parallel like a GPT and runs inference like an RNN with constant memory per token, plus usage, fine-tuning and troubleshooting.
Vllm Deploy K8s
vllm-project/vllm-skills
Deploy vLLM to Kubernetes (K8s) with GPU support, health probes, and OpenAI-compatible API endpoint.
- Official
Upgrading Mwaa Environments
aws/agent-toolkit-for-aws
Upgrades an MWAA environment to a newer Airflow version — within 2.x, within 3.x, or across the 2.x-to-3.x boundary.
Prefect
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Related
Works with
Questions about Ray
What does Ray do?
Production Ray (open-source, pinned to 2.57) for classic-ML workloads from training to serving — Ray Train, Tune, Data, Serve, Core, and cluster deployment on KubeRay. Ray is an agent skill from pproenca/dot-skills.57) for classic-ML workloads from training to serving — Ray Train, Tune, Data, Serve, Core, and cluster deployment on KubeRay.
When should I use Ray?
Ray fits situations like: productionizing Python code that touches Ray distributed training; hyperparameter tuning; ray cluster operations.
How do I install Ray in Claude Code?
Run `npx skills add pproenca/dot-skills --skill ray -a claude-code`. Or copy the skill folder (skills/.experimental/ray in pproenca/dot-skills) into .claude/skills/ray in your project. Claude Code loads it when a task matches its description.
How do I install Ray in Codex?
Run `npx skills add pproenca/dot-skills --skill ray -a codex`. Or copy the skill folder (skills/.experimental/ray in pproenca/dot-skills) into .agents/skills/ray in your project. Codex loads it when a task matches its description.
Can I use Ray in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add pproenca/dot-skills --skill ray -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ray, .gemini/skills/ray, .github/skills/ray and .opencode/skills/ray in your project.
What does Ray need to run?
SKILL.md names no scripts, command-line tools or credentials: Ray is instructions for the agent only. Our summary lists: Python 3.
Does Ray access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Ray safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Ray use?
Ray is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Ray use?
About 2.2k tokens (SKILL.md is roughly 8.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 9.6k tokens, read only when the agent opens those files.
What are the alternatives to Ray?
Skills that share tags, products or a category with Ray: Technology Selection (dotnet/skills, 5.6k stars), vLLM Model Serving (Orchestra-Research/AI-Research-SKILLs, 13k stars), RWKV Architecture Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Vllm Deploy K8s (vllm-project/vllm-skills, 103 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Ray?
pproenca (a GitHub user) maintains it in pproenca/dot-skills, which has 214 GitHub stars. The repository holds 182 skills in this directory. The repository was last updated on August 15, 2026.
Source: pproenca/dot-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.