Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add pproenca/dot-skills --skill raygit clone --depth 1 https://github.com/pproenca/dot-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pproenca/dot-skills/ray)<a href="https://agentmods.dev/skills/pproenca/dot-skills/ray"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/ray/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pproenca/dot-skills/ray"><img src="https://agentmods.dev/badge/skills/pproenca/dot-skills/ray.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00207 | $0.02179 |
| Opus 5 | $0.00103 | $0.01090 |
| Sonnet 5 | $0.00041 | $0.00436 |
| Haiku 4.5 | $0.00021 | $0.00218 |
Grade A, and why
ray scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 |
What ships with it
30 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- AGENTS.md 8.4 KB
- assets/templates/_template.md 935 B
- metadata.json 3.0 KB
- references/_sections.md 3.1 KB
- references/core-classic-traps-residue.md 1.8 KB
- references/core-object-store-sizing.md 1.2 KB
- references/core-retries-system-failures-only.md 1.1 KB
- references/core-state-api-not-ray-state.md 1.1 KB
- references/data-compute-not-concurrency.md 1.3 KB
- references/data-iter-torch-batches-not-to-torch.md 1.2 KB
- references/data-override-num-blocks.md 899 B
- references/data-streaming-replaced-pipelines.md 1.1 KB
- references/data-zero-copy-read-only-batches.md 1.2 KB
- references/prod-baked-images-not-runtime-env.md 1.3 KB
- references/prod-gcs-ft-redis.md 1.4 KB
- references/prod-jobs-api-not-head-driver.md 1.0 KB
- references/prod-kuberay-crd-choice.md 1.2 KB
- references/prod-metrics-wiring.md 1.5 KB
- references/serve-autoscaling-current-fields.md 1.3 KB
- references/serve-deployment-response-handles.md 1.4 KB
- references/serve-max-ongoing-requests.md 1.2 KB
- references/serve-placement-controls.md 1.3 KB
- references/serve-run-build-deploy.md 1.3 KB
- references/train-configs-from-ray-train-elastic.md 1.2 KB
- references/train-prepare-model-and-loader.md 1.5 KB
- references/train-report-not-air-session.md 1.7 KB
- references/train-v2-default-changed-semantics.md 1.5 KB
- references/tune-driver-function-not-trainer.md 2.1 KB
- references/tune-runconfig-from-ray-tune.md 1.2 KB
- references/tune-tuner-canonical.md 1.4 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 100 lines · 207 tokens per session scan A ce2a19c3e669
ray is a skill published in the GitHub repository pproenca/dot-skills (205 stars, last pushed 24d ago), licensed MIT. It adds 207 tokens to every session and 2,179 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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