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Agent skill

Scheduled Automation

by OPPO-Mente-Lab in OPPO-Mente-Lab/X-OmniClaw

Schedule app automation tasks such as opening an app and performing actions at a specific time.

Apache-2.0Auto-check passedProductivity & Automation

Install Scheduled Automation

skills CLI
$ npx skills add OPPO-Mente-Lab/X-OmniClaw --skill scheduled-automation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install OPPO-Mente-Lab/X-OmniClaw scheduled-automation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/OPPO-Mente-Lab/X-OmniClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/app/src/main/assets/skills/scheduled-automation .claude/skills/scheduled-automation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
scheduled-automation
GitHub stars
265
Token cost
~1.5k tokens
SKILL.md length
532 words
Files
1
Skills in repo
14
Repo updated
First seen
Licence
Apache-2.0

At a glance

Schedule app automation tasks such as opening an app and performing actions at a specific time.

  • Works in 3 steps: LLM extraction → Rule parsing → Ambiguity check
  • Tasks that involve App automation through connectors
  • SKILL.md covers Extraction Workflow, Preferred Tool, Tool Parameters and When to fall back to…, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Scheduled Automation is an agent skill from OPPO-Mente-Lab/X-OmniClaw. Schedule app automation tasks such as opening an app and performing actions at a specific time.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Productivity & Automation, covering App automation through connectors. The repository describes itself as: An edge-native Multimodal Android Agent that integrates multimodal perception, memory and action. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve App automation through connectors

Example prompts

  • “/scheduled-automation”

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. LLM extraction
  2. Rule parsing
  3. Ambiguity check

What it can do on your machine

Read from SKILL.md and the folder at commit 8614c93. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Scheduled Automation loads about 1.5k tokens when it runs. Until then it costs about 29 tokens; SKILL.md has 532 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~29
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from OPPO-Mente-Lab/X-OmniClaw at commit 8614c93, republished under its Apache-2.0 licence (© OPPO-Mente-Lab). 532 words, ~1,459 tokens.

Download SKILL.mdSave it as .claude/skills/scheduled-automation/SKILL.md (or your agent's skills folder).
name
scheduled-automation
description
Schedule app automation tasks such as opening an app and performing actions at a specific time.

Scheduled Automation

Use this skill when the user wants the phone to do something later, every day, on specific weekdays, on workdays, or at a fixed minute interval.

Extraction Workflow

For app-centric natural-language requests, use this two-stage workflow:

  1. LLM extraction:
    • extract app_name
    • extract operation
    • extract schedule_phrase
  2. Rule parsing:
    • let schedule_app_task parse schedule_phrase
    • convert it into repeat, daily_time, days_of_week, or interval_minutes
  3. Ambiguity check:
    • if app_name, operation, or schedule_phrase is still ambiguous
    • let the tool return clarification questions first instead of creating the wrong task

Important boundary:

  • This skill is only for creating, listing, or cancelling future schedules.
  • When an existing schedule fires, do not call schedule_app_task again.
  • The saved execution instruction must be immediate and one-shot, e.g. 打开小红书，然后搜索新闻总结后发给我.
  • Never keep words such as 每天晚上8点, 每周三, 定时, or 到点 inside operation; those belong only in schedule_phrase.

Example:

  • user request: 每周三早上10点打开小红书搜新闻并总结
  • LLM extraction:
    • app_name = 小红书
    • operation = 搜新闻并总结
    • schedule_phrase = 每周三早上10点
  • rule parsing result:
    • repeat = weekly
    • days_of_week = [3]
    • daily_time = 10:00

Typical requests:

  • "每天晚上12点打开微信给张三发消息"
  • "每天中午12点打开小红书搜索 AI 新闻并总结"
  • "每周三早上10点打开小红书搜索 AI 新闻并总结"
  • "每个工作日上午10点打开企业微信提醒我打卡"
  • "每隔45分钟打开某个 App 检查一次状态"
  • "明天早上8点自动打开企业微信提醒我打卡"
  • "帮我定时打开某个 APP 去做某事"
  • "每天晚上扫描相册并更新用户画像"

Preferred Tool

schedule_app_task

Use schedule_app_task as the high-level first choice for natural-language scheduling requests about apps and follow-up operations.

It is better than the lower-level schedule_task when the request is naturally phrased as:

  • at what time
  • open which app
  • then do what

If the user says:

  • "每天晚上12点打开微信去做 X 操作"

Prefer extracting fields first, then call:

json
{
  "action": "create",
  "task_name": "daily-wechat-task",
  "app_name": "微信",
  "operation": "做 X 操作",
  "schedule_phrase": "每天晚上12点"
}

Tool Parameters

schedule_app_task
  • action: create | list | cancel
  • task_name: task display name
  • app_name: target app name, e.g. 微信
  • package_name: optional package name for higher precision
  • operation: what to do after opening the app
  • schedule_phrase: preferred natural-language schedule phrase extracted by the LLM, e.g. 每周三早上10点
  • repeat: daily | once | weekly | workday | interval
  • time_text: backward-compatible alias of schedule_phrase
  • days_of_week: used with repeat=weekly, e.g. ["mon", "wed"] or ["周三"]
  • interval_minutes: used with repeat=interval, e.g. 30 or 45
  • run_at: one-time target time
  • delay_seconds: one-time delay
  • task_id: used when cancelling
Show full SKILL.md (217 more words)Show less

When to fall back to schedule_task

Use lower-level schedule_task when:

  • the task is not app-centric
  • the user wants to schedule a generic agent instruction
  • the execution target is not just "open app then do something"
  • the task is a memory maintenance workflow such as gallery syncing or profile rebuilding

If the user says:

  • "每天晚上 10 点扫描相册并更新用户画像"

Prefer using schedule_task with a clear instruction that tells the agent to use gallery_memory:

json
{
  "action": "create",
  "task_name": "daily-gallery-memory-sync",
  "instruction": "使用 gallery_memory 工具同步相册中的新增图片记忆，并更新用户画像。",
  "repeat": "daily",
  "daily_time": "22:00"
}

Best Practices

  1. Prefer schedule_app_task for "定时打开 App 并操作" requests.
  2. Preserve the user's original business intent inside operation.
  3. If the target app is ambiguous, ask for clarification or include package_name.
  4. For first-time setup, remind the user to verify the task once on a real device.
  5. When the task is critical, suggest checking exact alarm permission and background restrictions.
  6. If the extracted fields still look vague, do not force task creation; let the tool ask follow-up questions first.
  7. Creation-time query and execution-time instruction are different: creation uses schedule_phrase; execution must contain only the action to perform now.

Example Commands

Daily task
json
{
  "action": "create",
  "task_name": "daily-xhs-news",
  "app_name": "小红书",
  "operation": "搜索 AI 新闻，并总结前三条结果",
  "schedule_phrase": "每天中午12点"
}
Weekly task
json
{
  "action": "create",
  "task_name": "weekly-xhs-news",
  "app_name": "小红书",
  "operation": "搜索 AI 新闻，并总结前三条结果",
  "schedule_phrase": "每周三早上10点"
}
Workday task
json
{
  "action": "create",
  "task_name": "workday-checkin",
  "app_name": "企业微信",
  "operation": "提醒我打卡",
  "schedule_phrase": "每个工作日上午10点"
}
Fixed interval task
json
{
  "action": "create",
  "task_name": "interval-status-check",
  "app_name": "小红书",
  "operation": "检查一次首页热点并总结",
  "schedule_phrase": "每隔45分钟"
}
One-time task
json
{
  "action": "create",
  "task_name": "wechat-reminder-tonight",
  "app_name": "微信",
  "operation": "给张三发送消息：明天记得交日报",
  "repeat": "once",
  "delay_seconds": 600
}
Cancel task
json
{
  "action": "cancel",
  "task_id": "your-task-id"
}

Verification Hint

After creating a task, you can use schedule_app_task(action="list") to confirm:

  • task exists
  • next trigger time is correct
  • generated instruction matches the user's intent

© OPPO-Mente-Lab, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in app/src/main/assets/skills/scheduled-automation of OPPO-Mente-Lab/X-OmniClaw.

Open the folder on GitHubat commit 8614c93

Compare with similar skills

Scheduled Automation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Scheduled Automation compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Scheduled Automation this skillOPPO-Mente-Lab/X-OmniClaw265—~1.5kAutomated safety check: PassApache-2.0
Composioyc-software/qm15k—~1.6kAutomated safety check: PassMIT
Connect Apps with ComposioComposioHQ/awesome-claude-skills77k3 repos~557Automated safety check: PassNone
Abuselpdb AutomationComposioHQ/awesome-claude-skills77k3 repos~738Automated safety check: PassNone
Slack Fileblockscout/frontend307—~540Automated safety check: PassCustom licence
Add Connection Typebagofwords1/bagofwords459—~2.3kAutomated safety check: PassCustom licence

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Categories

Questions about Scheduled Automation

What does Scheduled Automation do?

Schedule app automation tasks such as opening an app and performing actions at a specific time. Scheduled Automation is an agent skill from OPPO-Mente-Lab/X-OmniClaw. Schedule app automation tasks such as opening an app and performing actions at a specific time.

When should I use Scheduled Automation?

Scheduled Automation fits situations like: tasks that involve App automation through connectors.

How do I install Scheduled Automation in Claude Code?

Run `npx skills add OPPO-Mente-Lab/X-OmniClaw --skill scheduled-automation -a claude-code`. Or copy the skill folder (app/src/main/assets/skills/scheduled-automation in OPPO-Mente-Lab/X-OmniClaw) into .claude/skills/scheduled-automation in your project. Claude Code loads it when a task matches its description.

How do I install Scheduled Automation in Codex?

Run `npx skills add OPPO-Mente-Lab/X-OmniClaw --skill scheduled-automation -a codex`. Or copy the skill folder (app/src/main/assets/skills/scheduled-automation in OPPO-Mente-Lab/X-OmniClaw) into .agents/skills/scheduled-automation in your project. Codex loads it when a task matches its description.

Can I use Scheduled Automation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add OPPO-Mente-Lab/X-OmniClaw --skill scheduled-automation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scheduled-automation, .gemini/skills/scheduled-automation, .github/skills/scheduled-automation and .opencode/skills/scheduled-automation in your project.

What does Scheduled Automation need to run?

SKILL.md names no scripts, command-line tools or credentials: Scheduled Automation is instructions for the agent only.

Does Scheduled Automation access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Scheduled Automation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Scheduled Automation use?

Scheduled Automation is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scheduled Automation use?

About 1.5k tokens (SKILL.md is roughly 5.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Scheduled Automation?

Skills that share tags, products or a category with Scheduled Automation: Composio (yc-software/qm, 15k stars), Connect Apps with Composio (ComposioHQ/awesome-claude-skills, 77k stars), Abuselpdb Automation (ComposioHQ/awesome-claude-skills, 77k stars) and Slack File (blockscout/frontend, 307 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scheduled Automation?

OPPO-Mente-Lab (a GitHub organization) maintains it in OPPO-Mente-Lab/X-OmniClaw, which has 265 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on May 22, 2026.

Source: OPPO-Mente-Lab/X-OmniClaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.