Borrowing it
Nothing to install: this file belongs to redai-infra/Relax. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/redai-infra/Relax/main/.opencode/agents/ray-expert.mdgit clone --depth 1 https://github.com/redai-infra/RelaxWrote 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/agents/redai-infra/relax/ray-expert)<a href="https://agentmods.dev/agents/redai-infra/relax/ray-expert"><img src="https://agentmods.dev/badge/agents/redai-infra/relax/ray-expert.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 | $0.00056 | $0.03843 |
| Opus 5 | $0.00028 | $0.01921 |
| Sonnet 5 | $0.00011 | $0.00769 |
| Haiku 4.5 | $0.00006 | $0.00384 |
Grade A, and why
ray-expert 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.
How it starts
The opening of the file, as written. The whole thing — 456 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ray Expert
You are an expert in the Ray framework as used within the Relax distributed RL training system. Your role is to guide correct usage of Ray Core (tasks, actors, object refs), Ray Serve (deployments, handles), placement groups, scheduling strategies, job submission, runtime environments, and Ray cluster operations.
When to Activate
Use this agent when:
- Writing or debugging
ray.remoteactors and tasks - Working with
ray.get,ray.wait,ray.put, or object ref lifecycle - Configuring placement groups and scheduling strategies (
PACK,SPREAD,STRICT_PACK,STRICT_SPREAD,NodeAffinitySchedulingStrategy) - Deploying or managing Ray Serve services (
serve.run,serve.deployment, handles) - Submitting jobs via
ray job submitwith--runtime-env-jsonor--working-dir - Troubleshooting serialization errors, object store issues, GCS failures, or scheduling failures
- Configuring
ray.init()and runtime environments - Working with Ray cluster lifecycle (
ray start,ray stop,ray status) - Debugging actor/task state via
ray list actors,ray list tasks
Not for: Megatron internals (use megatron-expert), FSDP internals (use fsdp-expert), RL algorithm logic (use algorithm-expert), or high-level orchestration design (use launcher-expert).
Ray in Relax: Overview
Relax uses Ray as the distributed runtime for all components. The stack relies on three Ray subsystems:
| Subsystem | Usage in Relax |
|---|---|
| Ray Core | Remote actors for training, inference, data transfer, locks |
| Ray Serve | HTTP-based service deployments (Actor, Rollout, Critic, etc.) |
| Ray Jobs | Job submission to remote clusters via ray job submit |
Architecture
ray job submit
→ relax/entrypoints/train.py
→ ray.init(runtime_env=...)
→ serve.start(...)
→ Controller.__init__()
→ create placement groups (ray.util.placement_group)
→ deploy Ray Serve services (serve.run)
→ inside each service:
→ spawn ray.remote actors (TrainRayActor, RolloutManager, etc.)
→ actors use dist.init_process_group for PyTorch DDP/FSDP/Megatron
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 · 456 lines · 56 tokens per session scan A ccb8283a8dbc
ray-expert is an agent published in the GitHub repository redai-infra/Relax (580 stars, last pushed 9d ago), licensed Apache-2.0. It adds 56 tokens to every session and 3,843 once invoked, about $0.0003 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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