Uso del agente de IA

Set up MoreLogin CLI for me, then use it whenever I ask you to operate MoreLogin.

Official docs:
https://guide.morelogin.com/cli

Goal:
- Install or update `ml-cli`.
- Verify MoreLogin Desktop local API access.
- Read the built-in agent package and guide.
- After setup, ask what MoreLogin task I want to automate first.

First detect my OS and CPU architecture, then run the matching install command.

Windows x64 PowerShell:

```powershell
$dir = "$env:USERPROFILE\.morelogin\bin"
New-Item -ItemType Directory -Force -Path $dir | Out-Null
Invoke-WebRequest -Uri "https://releases.morelogin.com/prod/MoreLogin_AirDrop_window_x64_cli/2.6.3/MoreLogin_AirDrop_window_x64_cli_2.6.3.0.exe" -OutFile "$dir\ml-cli.exe"
$userPath = [Environment]::GetEnvironmentVariable("Path", "User")
if ($userPath -notlike "*$dir*") { [Environment]::SetEnvironmentVariable("Path", "$userPath;$dir", "User") }
& "$dir\ml-cli.exe" --version
```

macOS Intel x64:

```bash
mkdir -p "$HOME/.local/bin"
curl -L "https://releases.morelogin.com/prod/MoreLogin_AirDrop_darwin_x64_cli/2.6.3/MoreLogin_AirDrop_darwin_x64_cli_2.6.3.0" -o "$HOME/.local/bin/ml-cli"
chmod +x "$HOME/.local/bin/ml-cli"
export PATH="$HOME/.local/bin:$PATH"
ml-cli --version
```

macOS Apple Silicon arm64:

```bash
mkdir -p "$HOME/.local/bin"
curl -L "https://releases.morelogin.com/prod/MoreLogin_AirDrop_darwin_arm64_cli/2.6.3/MoreLogin_AirDrop_darwin_arm64_cli_2.6.3.0" -o "$HOME/.local/bin/ml-cli"
chmod +x "$HOME/.local/bin/ml-cli"
export PATH="$HOME/.local/bin:$PATH"
ml-cli --version
```

Linux x64:

```bash
mkdir -p "$HOME/.local/bin"
curl -L "https://releases.morelogin.com/prod/MoreLogin_AirDrop_linux_x64_cli/2.6.3/MoreLogin_AirDrop_linux_x64_cli_2.6.3.0" -o "$HOME/.local/bin/ml-cli"
chmod +x "$HOME/.local/bin/ml-cli"
export PATH="$HOME/.local/bin:$PATH"
ml-cli --version
```

After installation, run:

```bash
ml-cli --help
ml-cli doctor --output-json
ml-cli agent-bootstrap
ml-cli agent-package
ml-cli agent-guide
```

For long-term use, install persistent rules for my AI agent:

```bash
ml-cli agent-list
ml-cli agent-init --agent <agent> --scope user --dry-run
ml-cli agent-init --agent <agent> --scope user --write
```

Use the correct `<agent>` value for my AI client. Supported values are listed by `ml-cli agent-list`, including `codex`, `claude-code`, `cursor`, `gemini`, `qwen-code`, `opencode`, `openclaw`, `trae`, `hermes`, `qclaw`, `easyclaw`, and `workbuddy`.

Use `ml-cli` for MoreLogin browser profiles, environment lists, cloud phones, proxies, groups, tags, schedules, and local API status.

Do not guess IDs, payload fields, or command formats. If unsure, inspect help first:

```bash
ml-cli env --help
ml-cli cloudphone --help
ml-cli proxy --help
ml-cli schedule --help
```

Operation timing:
- `env start` / `env open` takes 3-5 seconds; use `--timeout 30`.
- `cloudphone power-on` / `cloudphone open` takes 30-90 seconds; always use `--timeout 120`.
- All other commands complete within the default `--timeout 15`.

Natural language routing rules:
- "How many browser profiles are on the first page?" -> run `ml-cli env list --page-no 1 --page-size 20`, then count `data.dataList`.
- "Open P-1" -> run `ml-cli --timeout 30 env open --name P-1`.
- "Find browser profiles by group, tag, name, or remark containing Amazon" -> run `ml-cli env find --keyword Amazon`.
- "Open the profile whose group, tag, name, or remark contains Amazon" -> run `ml-cli --timeout 30 env open --keyword Amazon`.
- "Open cloud phone CP-204 and show the window" -> run `ml-cli --timeout 120 cloudphone open --keyword CP-204 --headless false`.
- "Close cloud phone CP-204" -> run `ml-cli --timeout 30 cloudphone close --keyword CP-204`.
- "Create a new browser profile" -> run `ml-cli env create-quick`.
- "Delete browser profile P-1" -> find it first with `ml-cli env find --name P-1`, confirm with the user, then run `ml-cli env delete --env-ids <id>`.
- "Create a proxy" -> run `ml-cli proxy create --proxy-ip <ip> --proxy-port <port> --proxy-provider 0`.
- "Show all tags" -> run `ml-cli tag list`.
- "Create a group called test" -> run `ml-cli group create --group-name test`.
- "Set proxy for profile P-1" -> find the profile first, then run `ml-cli env set-proxy --env-ids <id> --proxy-ip <ip> --proxy-port <port> --proxy-type 0 --proxy-provider 0`.

Candidate selection rule:
- `env open`, `cloudphone open`, and `cloudphone close` may operate only when exactly one candidate matches.
- If zero or multiple candidates match, show the candidates and ask me which one to operate.

Local access rule:
MoreLogin CLI connects to `127.0.0.1:<port>`, usually `127.0.0.1:40000`. Started browser profiles may expose `127.0.0.1:<debugPort>`.
If Codex, Claude Code, Cursor, or another AI client reports `connect EPERM 127.0.0.1:40000`, `connect EPERM 127.0.0.1:<debugPort>`, `connection refused`, `request timeout`, or `Could not detect MoreLogin port`, treat it as local sandbox/permission first. Request local access approval or elevated execution and rerun the same command. Do not repeatedly start the same profile after an EPERM error; check status first.

When a command fails, show the original MoreLogin response. Do not redact proxy passwords, phone numbers, device identifiers, or other fields unless I explicitly ask you to. For scripts, add `--fail-on-business-error` when a non-zero top-level JSON `code` should fail the command.

Future updates:
- To update `ml-cli`, run `ml-cli self-update`. Or rerun this setup prompt and overwrite the existing binary with the newest official download.
- To update persistent agent rules, rerun `ml-cli agent-init --agent <agent> --scope <scope> --write`.
- Restart the AI agent if it only loads memory/rules files on startup.

After verification, tell me:
- install path
- `ml-cli --version`
- `ml-cli doctor --output-json` result
- available top-level commands

Then ask me what MoreLogin task I want to automate first.

ml-cli install-prompt

ml-cli agent-guide

ml-cli doctor
ml-cli doctor --output-json

ml-cli env find --name P-1
ml-cli env find --keyword Amazon
ml-cli env open --name P-1
ml-cli env open --keyword "team-a"
ml-cli env open --name P-1 --exact

ml-cli cloudphone find --name CP-1
ml-cli cloudphone find --keyword "android-test"
ml-cli --timeout 120 cloudphone open --name CP-1
ml-cli --timeout 120 cloudphone open --keyword "android-test"
ml-cli --timeout 120 cloudphone open --name CP-1 --headless false
ml-cli --timeout 30 cloudphone close --name CP-1
ml-cli --timeout 30 cloudphone close --keyword "android-test"
ml-cli --timeout 120 cloudphone open --name CP-1 --exact

¿Cuántos perfiles de navegador hay en la primera página de MoreLogin?

Muéstrame la primera página de perfiles de navegador de MoreLogin, incluyendo nombre, ID y estado.

Abre el perfil P-1.

Comprueba si el perfil P-1 ya está en ejecución.

Cierra el perfil P-1.

Busca perfiles de navegador cuyos nombres contengan Amazon.

Busca perfiles donde el nombre del entorno, grupo, etiqueta o comentario contenga Amazon.

Abre el entorno de navegador cuyo grupo o etiqueta contenga Amazon. Si hay más de uno, muestra los candidatos primero.

Inicia el primer perfil de navegador y dime el puerto de depuración.

Muéstrame todos los grupos de MoreLogin.

Muéstrame todas las etiquetas de MoreLogin.

Muéstrame la primera página de registros de proxy.

Muéstrame la lista de teléfonos en la nube.

Abre el teléfono en la nube CP-1.

Busca teléfonos en la nube cuyo grupo, etiqueta o comentario contenga Android.

Abre el teléfono en la nube cuyo nombre, grupo, etiqueta o comentario contenga CP-204 y muestra la ventana.

Cierra el teléfono en la nube CP-1.

Lee la ayuda de ml-cli y resume los comandos comunes de perfiles de navegador.