---

name: services-cli-operations description: "Operate AReaL 2.0 service CLIs, service lifecycles, model registration, status/log/state inspection, Hermes online RL session/reward wiring, and safe command validation." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

AReaL Services And CLI Operations

Use this sub-skill when the task is to operate AReaL 2.0 service surfaces rather than author a new training algorithm or backend. Covered surfaces are:

• areal inf: inference gateway/router lifecycle, model registration, model listing, logs, status, state, and OpenAI-compatible inference endpoints.
• areal agent: agent-service gateway/router/worker/data-proxy lifecycle, session-affine agent calls, status, logs, and state.
• areal train: safe driver invocation shape for AReaL training scripts.
• Service HTTP APIs: inference, agent, training, and weight-update gateway/router/worker/data-proxy contracts.
• Online RL service wiring: start a session, route external or agent traffic through the inference gateway, set rewards, export or refresh trajectories, and shut down cleanly.

Route boundaries

• If the user needs to write or repair an AgentRunnable, AgentWorkflow, RolloutWorkflow, dataset loader, reward function, or framework adapter, route to ../custom-data-rewards-workflows/SKILL.md.
• If the service starts but SGLang/vLLM/FSDP/Megatron/Archon workers fail, GPUs are misallocated, weight sync hangs, CUDA/NCCL fails, or backend specs need cluster planning, route to ../distributed-engines-backends/SKILL.md.
• This sub-skill may generate commands and validate them, but bundled scripts must not start services, training, model downloads, or credentialed provider calls.

Operating checklist

1. Identify the surface: areal inf, areal agent, areal train, direct service module, online RL session/reward, or weight update.

2. Read references/service-cli-reference.md for command/API contracts, state locations, config precedence, and side effects.

3. For online RL, Hermes, or self-evolution loops, read references/online-rl-service-recipes.md before composing commands. Keep admin keys, agent-service keys, and sk-sess-* session keys distinct.

4. Validate command text before handing it to a user or scheduler:

   python scripts/check_service_cli.py --command 'areal inf register --model-name qwen --backend sglang:d1 --model-path /models/qwen'
   python scripts/check_service_cli.py --config service.toml --config-type inf
   

   The checker is static: it uses shell parsing and TOML inspection only. It does not import AReaL, contact HTTP endpoints, start services, start training, or read model files.

5. Use areal <group> status, ps, and logs as the first live checks once the user has confirmed that live service inspection is allowed. Use references/troubleshooting.md to map symptoms to fixes.

Safety defaults

• Treat every run, register, deregister, stop, train run, direct python -m areal.v2.* service module, and weight-update request as side-effecting.
• Never paste real API keys into generated examples; use shell variables such as $INF_ADMIN_KEY, $AGENT_ADMIN_KEY, and $SESSION_API_KEY.
• Do not use built-in demo keys for non-local services. For production or shared hosts, require user-provided secrets and explicit bind host/port decisions.
• Do not run native repo tests, example services, training jobs, or model downloads as part of this sub-skill. Classify those as native candidates or live operations requiring user approval.

Bundled references

• references/service-cli-reference.md — CLI matrix, config/state/log layout, HTTP API summaries, model registration, direct module entrypoints.
• references/online-rl-service-recipes.md — online proxy and Hermes/session/reward recipes with concrete request payloads.
• references/troubleshooting.md — service, key, session, reward, state, port, and backend-boundary failure modes.
• scripts/check_service_cli.py — safe static validator for service CLI commands, TOML snippets, backend specs, and quoted engine/proxy args.