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arl-cli

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포크1

업데이트2026년 7월 18일 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.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

Lincyaw

Lincyaw/agent-env

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

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 ./...