Execute qualquer Skill no Manus
com um clique

Execute qualquer Skill no Manus com um clique

arl-cli

Estrelas1

Forks1

Atualizado18 de julho de 2026 às 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.

Instalação

Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

Executar no Manus

Fonte

Lincyaw

Lincyaw/agent-env

Abrir repositório GitHub Ver repositórios do creator

Download

Executar no Manus

Ocupações relacionadasSOC

Baseado na classificação ocupacional SOC

Administradores de redes e sistemas de computadorInformática e Matemática·SOC 15-1244

Explorador de arquivos

3 arquivos

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 ./...

Mais deste repositório

mesmo repositório

arl-python-sdk

Lincyaw/agent-env

Conceptual map for maintaining or using the `arl-env` Python SDK (`from arl import ...`). Use this skill when the user asks about ARL Python SDK capabilities, how SDK features map to gateway/runtime code, where to implement a SDK change, or how Python users should interact with ARL sessions, managed experiments, warm pools, files, replay, trajectories, logs, auth, or interactive shells. Prefer this as a feature/file guide, not a detailed API tutorial.

2026-07-181

iroh-quic

Lincyaw/agent-env

Reference for using the iroh P2P QUIC library (iroh 1.0+) by n0-computer in Rust and Python. Use this skill whenever writing or modifying code that touches iroh — creating Endpoints, connecting peers, opening QUIC streams, implementing protocol handlers, TCP tunneling, or using the Python iroh bindings. Also use when debugging iroh connection issues, NAT traversal problems, or relay connectivity. Triggers on: iroh, QUIC, EndpointId, iroh transport, iroh tunnel, iroh relay, iroh connect, direct-connect, P2P QUIC, iroh Python, dumbpipe, iroh protocol handler, ALPN.

2026-07-121

kubernetes

Lincyaw/agent-env

Kubernetes operations, testing, and validation. Use when working with Kubernetes clusters for deploying resources, verifying deployments, testing operators/CRDs, debugging pods, monitoring workloads, or performing end-to-end testing and validation of K8s applications.

2026-03-101

code-review

Lincyaw/agent-env

Professional code review for anti-patterns, extensibility, architectural soundness, and code quality. Use when users request code review, ask to check for anti-patterns, need architectural validation, want to ensure extensibility, or request analysis of code quality. Apply to tasks involving code auditing, refactoring recommendations, design pattern validation, or ensuring every line of code has purpose and meaning.

2026-01-071

doc

Lincyaw/agent-env

Modern documentation management with MkDocs Material theme and GitHub Pages publishing. Use when users need to create, maintain, or publish professional documentation websites. Supports initialization of new documentation projects, live preview with auto-reload, production builds, and automated deployment to GitHub Pages. Features modern Material Design theme with dark mode, full-text search, mobile responsiveness, code highlighting, and rich markdown extensions including Mermaid diagrams, admonitions, and tabs.

2026-01-071

python

Lincyaw/agent-env

Advanced Python development with architecture expertise and automated code quality enforcement. Use when developing Python applications, libraries, or scripts that require high code quality, type safety, best practices, and professional architecture patterns. Includes automated linting with Ruff, type checking with MyPy, security scanning with Bandit, and comprehensive testing. Apply for tasks involving Python code creation, refactoring, quality checks, project initialization, or implementing design patterns.

2026-01-061

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 ./...