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Auto Login Assistant

作者 ddmmddmm · GitHub ↗ · v0.1.0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install auto-login-assistant
功能描述
Help the agent recover from login walls on websites by detecting sign-in states, collecting user-approved credentials, filling common login forms, and handli...
使用说明 (SKILL.md)

Auto Login Assistant

Overview

Use this skill when the agent is navigating a website and progress is blocked by a login screen, expired session, or verification-code challenge. The skill provides a conservative workflow for sign-in assistance: detect the login wall, collect user-approved credentials, fill the form, and handle one-time codes with clear consent boundaries.

This skill is intentionally not a bypass tool. It should never attempt to break captchas, defeat anti-bot systems, or infer secrets the user did not explicitly provide.

When To Use

Trigger this skill when any of the following are true:

  • The page redirects to a login, sign-in, or session-expired screen.
  • The user asks the agent to sign in to a website, mailbox, SaaS product, or admin console.
  • A workflow such as reading email, sending mail, checking dashboards, or accessing settings is blocked by authentication.
  • The user wants the agent to help retrieve or place a verification code after they approve the flow.

Do not use this skill for:

  • Captcha solving, QR login bypass, hardware key prompts, payment approval, or biometric confirmation
  • Guessing usernames, passwords, security questions, or backup codes
  • Reading email or messages unless the user explicitly authorizes it for the current task

Workflow

1. Confirm It Is A Login Barrier

First verify that the page is actually asking for authentication. Look for signals such as:

  • URL patterns like login, signin, auth, session-expired, verify
  • Password fields, OTP fields, or email/username inputs
  • Buttons or headings such as "Sign in", "Log in", "Continue with email", "Enter code"

If the page is ambiguous, say so and ask the user whether you should treat it as a login flow before entering any credentials.

2. Choose Credential Source

Credential priority order:

  1. Credentials the user provides in the current conversation
  2. A local file path the user explicitly points to
  3. Environment variables the user explicitly names

Never scan the filesystem broadly for secrets. Never assume a saved credential source without user direction.

If the user gives a file path or env var name, use scripts/read_credentials.py to normalize it into a consistent structure.

Supported normalized fields:

  • site
  • login_url
  • username
  • email
  • phone
  • password
  • otp_email
  • otp_mode
  • notes

See references/config-example.md for examples.

3. Fill The Login Form Conservatively

Use the website's visible login flow rather than forcing a direct post.

Preferred field mapping order:

  • User identifier: email, username, account, phone
  • Secret: password
  • Verification: otp, code, verification code, security code

Before submitting:

  • Confirm the target site with the user if multiple accounts could apply
  • Mask secrets in your explanation
  • Avoid clicking "remember this device" or equivalent unless the user explicitly asks

4. Handle Verification Codes

Default behavior: ask the user to provide the verification code manually.

Only enter the email-reading branch if the user explicitly authorizes it for the current task and provides the mailbox access path. When allowed:

  • Read only the minimum mailbox content needed to locate the latest relevant code
  • Extract likely codes with scripts/extract_verification_code.py
  • Present the candidate briefly if confidence is low
  • If multiple codes are plausible, ask before submitting

If email access fails or is unavailable, fall back to asking the user to paste the code.

5. Validate Success

After submit, confirm login success using page evidence:

  • User avatar, account menu, inbox, dashboard, or "sign out" control
  • Removal of login prompt
  • Successful navigation to the requested feature

If the flow fails, stop after a small number of attempts and explain the blocker clearly. Do not loop forever on retries.

Safety Rules

  • Treat credentials as ephemeral unless the user explicitly asks for a reusable local config.
  • Do not store credentials in the skill folder.
  • Do not broaden permissions, change MFA settings, or approve trusted-device prompts without explicit user permission.
  • Refuse flows that amount to bypassing authentication or anti-abuse protections.
  • If the website requests a captcha, QR scan, physical token, or passkey confirmation, hand control back to the user.

Suggested Interaction Pattern

Use short, direct prompts like these:

  • "This page appears to require login. Do you want me to sign in with credentials you provide now, or a local config you specify?"
  • "I found a password field and an email field. Please provide the account for this site, or point me to the config path."
  • "The site is asking for a verification code. If you want, paste the code here. I can only read email for this if you explicitly authorize that mailbox for this task."

Resources

references/config-example.md

Load this when the user wants a reusable local credential format or wants to see supported fields.

scripts/read_credentials.py

Run this to normalize credentials from a JSON file or environment variables into a consistent schema.

scripts/extract_verification_code.py

Run this to extract likely one-time codes from email text or copied verification messages after the user authorizes that step.

安全使用建议
This skill is coherent with its purpose, but exercise caution before using it: 1) Only provide credentials or point to a file/env var when you explicitly intend the agent to use that account—do not point it at system/global credential stores (e.g., ~/.aws/credentials). 2) read_credentials.py reads and outputs normalized JSON to stdout, so any supplied password or token will be visible to the agent and may end up in logs—avoid using high-value credentials during initial testing. 3) There is no bundled mailbox reader; authorizing mailbox access would rely on other tooling—do not grant broad mailbox access unless you trust the environment. 4) Review the two included scripts locally (they are short and readable) and test the skill with throwaway/test accounts before using it with production accounts. If you need higher assurance, request removal of stdout printing of secrets or change the workflow to have the agent prompt you to paste secrets directly into the conversation (so they are not written to process output).
功能分析
Type: OpenClaw Skill Name: auto-login-assistant Version: 0.1.0 The skill is designed to automate website logins, which inherently involves high-risk handling of sensitive credentials. It includes a script, `scripts/read_credentials.py`, that can read arbitrary files or environment variables and parse them as JSON to extract passwords. While the `SKILL.md` instructions emphasize user consent and the scripts enforce a specific data schema, the capability to access the filesystem and environment for secrets is a significant attack surface for prompt injection, fitting the criteria for suspicious behavior despite the lack of clear malicious intent.
能力评估
Purpose & Capability
The name/description match the included scripts and SKILL.md. The two bundled scripts (read_credentials.py and extract_verification_code.py) clearly support normalizing user-provided credentials and extracting OTPs from text. No unrelated binaries, network endpoints, or cloud credentials are required.
Instruction Scope
The runtime instructions are conservative: they require explicit user consent before reading a file or env var and forbid bypassing anti-abuse controls. However, the skill does allow the agent to be pointed at a local file path or an environment variable name — if the user gives a path or env var that contains broad secrets (e.g., ~/.aws/credentials) those secrets would be read. Also, SKILL.md discusses reading mailboxes but no mailbox-reading implementation is bundled; mailbox access would depend on the surrounding agent/browser tooling, not these scripts.
Install Mechanism
There is no install spec and the skill is instruction-first with small helper scripts included. Nothing is downloaded at install time and no archives or external installers are referenced.
Credentials
The skill declares no required environment variables and requests no credentials by default. It supports using a user-specified env var or file containing a JSON blob; that is proportionate for a login helper. Warning: asking the agent to point at an env var or file gives it access to whatever that variable/file contains, so users should avoid pointing it at unrelated secrets.
Persistence & Privilege
The skill is not always-enabled. agents/openai.yaml sets allow_implicit_invocation to false, reducing implicit invocation. The skill does not modify other skill configs or request persistent system-wide privileges.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install auto-login-assistant
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /auto-login-assistant 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.1.0
Initial release: safe login-wall detection, credential normalization, and manual-first OTP handling.
元数据
Slug auto-login-assistant
版本 0.1.0
许可证
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Auto Login Assistant 是什么?

Help the agent recover from login walls on websites by detecting sign-in states, collecting user-approved credentials, filling common login forms, and handli... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 357 次。

如何安装 Auto Login Assistant?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install auto-login-assistant」即可一键安装,无需额外配置。

Auto Login Assistant 是免费的吗?

是的,Auto Login Assistant 完全免费(开源免费),可自由下载、安装和使用。

Auto Login Assistant 支持哪些平台?

Auto Login Assistant 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Auto Login Assistant?

由 ddmmddmm(@ddmmddmm)开发并维护,当前版本 v0.1.0。

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