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 G1Joshi/Agent-Skills --skill raygit clone --depth 1 https://github.com/G1Joshi/Agent-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/g1joshi/agent-skills/ray)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/ray"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/ray.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00012 | $0.00267 |
| Opus 5 | $0.00006 | $0.00133 |
| Sonnet 5 | $0.00002 | $0.00053 |
| Haiku 4.5 | $0.00001 | $0.00027 |
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 7d 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.
What it actually says
Ray
Ray is the compute layer for AI. It powers ChatGPT training and massive scale workloads. v3.0 (2025) improves efficiency and adds an MCP Server for agents.
When to Use
- Distributed Training: Scaling PyTorch across 100 GPUs.
- Ray Serve: Serving LLMs with high throughput (vLLM integration).
- Hyperparameter Tuning: Ray Tune is the industry standard.
Core Concepts
Actors & Tasks
- Task: Stateless function (like Lambda).
- Actor: Stateful class (like a microservice).
Object Store
Shared memory across the cluster means zero-copy data sharing.
Best Practices (2025)
Do:
- Use
ray.data: For streaming massive datasets into trainers. - Use KubeRay: The Kubernetes operator for managing Ray clusters.
- Use Ray Serve: It supports "Model Composition" (chaining models).
Don't:
- Don't use for simple scripts: The overhead of starting a Ray cluster is 5-10s.
References
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.
- 7d ago First seen · 42 lines · 12 tokens per session scan A cc173f6cbae1
ray is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 6mo ago), licensed MIT. It adds 12 tokens to every session and 267 once invoked, about $0.0001 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 2026-08-30.
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