---

name: rllm description: "Use rLLM for language-agent evaluation, dataset/task management, RL/SFT post-training, CLI setup, and gateway-backed rollout tracing." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

rLLM

Use this root skill when a task names rLLM, rllm, rllm-model-gateway, AgentFlow, @rllm.rollout, rllm eval, rllm dataset, rllm train, rllm sft, Tinker, Verl, Fireworks, sandboxed language-agent benchmarks, or the rLLM CLI.

This is a router, not a full manual. First classify the request, then read the focused sub-skill and only the references needed for that workflow.

Fast Start

1. Check installation scope. Read references/installation-and-environment.md before installing extras. Core eval/data workflows are lighter than Tinker/Verl/Fireworks/AgentCore training workflows.

2. Run a safe smoke check when setup is uncertain:

   python scripts/rllm_smoke_check.py
   

   The script imports core packages, inventories CLI entry points, and reports optional backend availability without training, downloading data, or contacting providers.

3. Use current public APIs, not stale docs. Confirmed source facts: rollout and evaluator are top-level rllm exports; AgentConfig lives in rllm.types; SimpleWorkflow is imported from rllm.workflows.simple_workflow, not directly from rllm.workflows.

4. Keep credentials explicit. Provider API keys, Tinker/Fireworks credentials, UI login, and remote sandbox services are never implied by a successful import check.

5. Treat full training as backend-specific. CPU/import tests can validate guidance, but local verl, Tinker, Fireworks, AgentCore, and long distributed runs require their actual backend/runtime evidence.

Route By Task

• Evaluate agents and author AgentFlows: read sub-skills/evaluation/SKILL.md for @rllm.rollout, @rllm.evaluator, AgentFlow, Task, Episode, Trajectory, rllm eval, pass@k, harness selection, sandboxed eval, and saved episode/result interpretation.
• Manage datasets and task layouts: read sub-skills/datasets/SKILL.md for rllm dataset, registered datasets, local benchmark directories, dataset.toml, task.toml, Harbor-compatible task packages, verifier metadata, and eval-to-SFT curation.
• Train or fine-tune models: read sub-skills/training/SKILL.md for rllm train, rllm sft, AgentTrainer, AgentSFTTrainer, BackendProtocol, Tinker, Verl, Fireworks, remote runtimes, gateway-backed traces, algorithm config, and backend validation failures.
• Operate the CLI around workflows: read sub-skills/cli-ops/SKILL.md for rllm model, rllm login, rllm init, rllm agent, rllm view, and rllm snapshot setup/utility workflows.
• Debug gateway or trace capture: start with references/gateway-and-traces.md, then use training for end-to-end training/eval wiring or cli-ops for provider/model configuration.

Shared References

• references/cli-command-map.md maps every top-level CLI command to its owning sub-skill and common non-destructive help checks.
• references/gateway-and-traces.md covers the bundled rllm-model-gateway service, client/config types, sessions, workers, traces, and trace-conversion pitfalls.
• references/troubleshooting.md covers cross-cutting import, optional dependency, provider, sandbox, and backend errors.
• references/repo-provenance.md records the source revision and extraction evidence used to build this skill.

Avoid

• Do not run long RL/SFT jobs, large dataset downloads, provider calls, snapshot builds, or remote sandbox provisioning unless the user explicitly wants those side effects.
• Do not tell users to read or run files from the original checkout. This skill tree contains the distilled references and safe helper scripts needed after the checkout is gone.