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arl-cli

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Mis à jour18 juillet 2026 à 18:33

Guide for using the `arl` CLI to inspect, debug, and manage the ARL (Agentic RL) runtime: experiments, warm pools, sessions, files, snapshots, replay, private containers, logs, and metrics. Use this skill whenever the user asks about checking pool status, viewing experiment sessions, creating or debugging sessions, transferring files, restoring snapshots, replaying trajectories, streaming logs, exporting trajectories, waiting for pool readiness, running commands in private sidecar containers, or any operational task involving the ARL system. Also trigger when the user mentions `arl` CLI commands, asks "how do I see what's running", "show me the pools", "check experiment X", "debug this session", "看看 pool 状态", "查看实验", "导出 trajectory", or wants to combine multiple arl operations into a workflow.

Installation

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Source

Lincyaw

Lincyaw/agent-env

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Administrateurs de réseaux et de systèmes informatiquesProfessions informatiques et mathématiques·SOC 15-1244

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SKILL.md

readonly

name

arl-cli

description

ARL CLI

arl is the command-line tool for the ARL runtime. It talks to the Gateway REST API, which allocates sandbox-backed sessions and reaches sidecars inside pods.

Use this skill to choose the right CLI workflow and inspect the focused reference needed for the task.

Setup

make build-cli

export ARL_GATEWAY_URL=http://localhost:8080
export ARL_API_KEY=your-token
export ARL_FORMAT=json

Use --format json for scripts and automation. Do not use JSON format with arl session download <id> <remote> -, because stdout is reserved for raw file bytes.

Agent-facing introspection:

arl --dump-schema

Choose a Reference

Command syntax, flags, env vars, and exit codes: read references/commands.md.
Operational playbooks and best practices: read references/workflows.md.
Python SDK parity or implementation mapping: use the arl-python-sdk skill.

Core Workflow

Confirm ARL_GATEWAY_URL and auth context before mutating pools or sessions.
Start with arl status, arl pool list --format wide, and arl session list --format wide when diagnosing runtime state.
Use experiments for benchmark/training runs that need grouping and cleanup.
Use one-off sessions or pool exec for ad hoc debugging.
Capture snapshot IDs and trajectory JSONL when a run may need replay or training data export.
Clean up temporary sessions, experiments, and pools after debugging.

Verification

For CLI code changes, run focused Go checks:

go test ./cmd/arl/... ./pkg/gateway/...
go test ./...

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name

arl-cli

description

ARL CLI

arl is the command-line tool for the ARL runtime. It talks to the Gateway REST API, which allocates sandbox-backed sessions and reaches sidecars inside pods.

Use this skill to choose the right CLI workflow and inspect the focused reference needed for the task.

Setup

make build-cli

export ARL_GATEWAY_URL=http://localhost:8080
export ARL_API_KEY=your-token
export ARL_FORMAT=json

Use --format json for scripts and automation. Do not use JSON format with arl session download <id> <remote> -, because stdout is reserved for raw file bytes.

Agent-facing introspection:

arl --dump-schema

Choose a Reference

Command syntax, flags, env vars, and exit codes: read references/commands.md.
Operational playbooks and best practices: read references/workflows.md.
Python SDK parity or implementation mapping: use the arl-python-sdk skill.

Core Workflow

Confirm ARL_GATEWAY_URL and auth context before mutating pools or sessions.
Start with arl status, arl pool list --format wide, and arl session list --format wide when diagnosing runtime state.
Use experiments for benchmark/training runs that need grouping and cleanup.
Use one-off sessions or pool exec for ad hoc debugging.
Capture snapshot IDs and trajectory JSONL when a run may need replay or training data export.
Clean up temporary sessions, experiments, and pools after debugging.

Verification

For CLI code changes, run focused Go checks:

go test ./cmd/arl/... ./pkg/gateway/...
go test ./...