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在 OpenClaw 中安装
/install screen-life
功能描述
macOS 数字生活日报：自动监控你每天在电脑上做什么，生成可读的行为报告。零配置，一键安装，后台静默运行。当用户想看今天用电脑做了什么、分析效率、查看应用使用时长时触发。
安全使用建议
Before installing: 1) Do not install or run the install.sh until you inspect the missing files — the package references daemon.py (activity daemon) which is not included; installation may fail or leave a LaunchAgent pointing at a missing script. 2) The privacy claim in SKILL.md is inaccurate: the handler will send report text to an external LLM endpoint (OPENCLAW_LLM_BASE_URL) if OpenClaw injects those env vars, and can post to a FEISHU webhook if FEISHU_WEBHOOK_URL is set. If you want strictly local-only operation, run handler.py with --no-llm and avoid setting any webhook env; still inspect the code for any network calls. 3) The installer writes a LaunchAgents plist and persists a daemon and logs under ~/.orbitos-monitor — review and backup before installing. 4) Check for expected missing components (daemon.py, any browser-history readers) and ask the maintainer for the complete source; the current bundle is incomplete and could be a packaging error or an attempt to mislead. 5) If you lack confidence in the package, prefer running the analysis on a disposable/macOS test account or sandbox, or request the full source and a human code review focusing on the daemon and any code that reads browser history or uploads data.
功能分析
Type: OpenClaw Skill
Name: screen-life
Version: 1.0.0
The skill implements a background monitoring system for macOS that tracks user activity, including application usage and browser history. It establishes persistence via a launchd agent (com.screen-life.daemon.plist) and collects sensitive data into a local directory (~/.orbitos-monitor/). While these actions align with the stated purpose of a 'digital life report,' the combination of silent background operation, access to browser databases, and the capability to exfiltrate summarized activity data to external endpoints (Feishu webhooks and LLM providers in handler.py) presents a significant privacy and security risk.
能力评估
Purpose & Capability
The README/description says the skill will read app usage, browser history, Obsidian git, Whisper, etc., and run silently in the background. The packaged files only include handler.py and install.sh; there is no daemon.py or explicit Chrome/Safari/history-reading implementation included. install.sh attempts to copy a daemon.py (and handler.py -> report_generator) but daemon.py is missing from the package, so the declared background-monitoring capability is not actually present in the bundle as provided.
Instruction Scope
handler.py reads local logs (~/.orbitos-monitor/*), may load a local .env, and will POST report content to an LLM endpoint if OpenClaw-injected LLM envs exist. The SKILL.md claims '不上传任何内容' (no uploading) which contradicts run_llm_analysis (sends report text to base_url) and push_feishu (posts to FEISHU_WEBHOOK_URL). Feishu webhook env is used but not declared in requires.env. The instructions therefore permit transmitting local activity to external endpoints and also allow reading environment variables or .env files beyond what was documented.
Install Mechanism
There is no formal package install spec, but install.sh will create ~/.orbitos-monitor, write a LaunchAgents plist into ~/Library/LaunchAgents, and attempt to copy scripts into that directory and launch a persistent daemon via launchctl. However, the referenced daemon.py is not included in the package, so the install script is incomplete and may fail or leave a plist pointing to a missing binary. The script writes persistent system files (plist, logs) which is expected for a monitor but is higher-impact than a purely CLI skill.
Credentials
SKILL.md declares reliance on OpenClaw-injected LLM envs (OPENCLAW_LLM_API_KEY, OPENCLAW_LLM_BASE_URL, OPENCLAW_LLM_MODEL) which handler.py uses to send report content to a remote LLM — this is proportionate only if the user understands reports will be transmitted externally. However, the skill also reads a local .env and uses FEISHU_WEBHOOK_URL if present (not declared), meaning it may access and transmit sensitive tokens not listed in requires.env. The privacy statement claiming 'no upload' is contradicted by the code that posts data externally.
Persistence & Privilege
The installer creates a user LaunchAgent plist that RunAtLoad and KeepAlive, so the monitor will persist across logins and run continuously. The skill is not marked always:true in metadata (so it won't be auto-enabled in every agent run), but the install script gives it persistent system presence in the user's account — appropriate for a monitor, but a higher-privilege action that should be explicitly consented to. Combined with the ability to send data externally, this increases sensitivity.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install screen-life - 安装完成后，直接呼叫该 Skill 的名称或使用
/screen-life触发 - 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
v1.0.0
Initial release on ClawHub
元数据
常见问题
screen-life 是什么？
macOS 数字生活日报：自动监控你每天在电脑上做什么，生成可读的行为报告。零配置，一键安装，后台静默运行。当用户想看今天用电脑做了什么、分析效率、查看应用使用时长时触发。 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 66 次。
如何安装 screen-life？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install screen-life」即可一键安装，无需额外配置。
screen-life 是免费的吗？
是的，screen-life 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
screen-life 支持哪些平台？
screen-life 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 screen-life？
由 vine.xio（@vineindalvik）开发并维护，当前版本 v1.0.0。
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