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learning-checkin学习签到

Agent Skill

learning-checkin 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 OpenClaw 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

11,663

周安装

501

GitHub Stars

1

下载量

4,088
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:learning-checkin(学习签到)
来源仓库:https://github.com/daizongyu/learning-checkin
安装命令:
openclaw skills install learning-checkin
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install learning-checkin

简介

用于培养日常学习习惯,支持签到与智能提醒功能。

  • 适合在 OpenClaw 中帮助用户建立规律学习节奏的场景。
  • 可记录学习进度与反馈,促进持续改进。learning-checkin 属于效率类 Skill,可作为该场景下的辅助能力补充。
  • 安装命令:openclaw skills install learning-checkin。
  • 建议确认权限范围及是否会触发通知或数据持久化。

SKILL.md

name
learning-checkin
description
Daily learning habit builder with check-ins and smart reminders
metadata
{ "copaw": { "emoji": "📚" } }

Learning Check-in Skill

Help users build a daily learning habit through simple check-ins and intelligent reminders.

Overview

This skill enables users to track their daily learning with:

  • Simple daily check-in (just say "I'm done" or "check-in complete")
  • Automatic streak tracking
  • Optional smart reminders

Data Storage

All data is stored locally in a data subfolder next to the skill:

<skill_directory>/data/
├── rule.md           - User's customizable rules
├── records.json      - Check-in history
├── version.txt       - Current skill version
├── cron_status.json  - Reminder configuration status
└── reminder_log.json - Reminder sending log

The data folder is automatically created on first use.

Commands

1. Initialize (First Time)

python <skill_path>/learning_checkin.py init

Returns:

  • welcome_message - Welcome text for the user
  • environment - Only contains user_language (for message display)
  • reminder_strategy - Suggested reminder times
  • cron_status - Current reminder configuration status

Agent action:

  1. Run the init command
  2. Show welcome message and explain the check-in process
  3. Ask user if they want daily reminders
  4. Ask user to start their first check-in

2. Check-in

python <skill_path>/learning_checkin.py checkin

Returns:

  • success - Whether check-in was recorded
  • streak - Current streak count
  • message - Celebration message (in English, translate to user's language)

3. Status

python <skill_path>/learning_checkin.py status

Returns:

  • checked_in_today - Whether user has checked in today
  • streak - Current streak count
  • total_checkins - Total days checked in
  • message - Status message (in English)

4. Get User Language

python <skill_path>/learning_checkin.py env

Returns:

  • user_language - Detected language (zh/en)

Why needed: Only to display messages in the user's preferred language.

5. Get Reminder Message

python <skill_path>/learning_checkin.py message <time>

Where <time> is one of: 09:00, 17:00, 20:00

Returns:

  • message - Reminder text (in English, translate to user's language)

6. Check Reminder Status

python <skill_path>/learning_checkin.py reminder <time>

Returns:

  • should_send - Whether reminder should be sent
  • checked_in - Whether user has already checked in today

7. Update Cron Status

python <skill_path>/learning_checkin.py update-cron <times>

When to use: After setting up reminders (optional).

8. Get Cron Status

python <skill_path>/learning_checkin.py cron-status

Returns:

  • configured - Whether reminders are set up
  • times - Configured reminder times

Default Behavior

Check-in Rule

  • User checks in once per day
  • Simply tell the Agent "I'm done" or "check-in complete"

Reminder Strategy (Suggested)

If user wants reminders, Agent can use any scheduling method:

  • Evening (20:00) is recommended as default
  • Or user's preferred time

The skill will check if user already checked in before sending reminders.

Streak System

  • Consecutive days = streak
  • Miss a day = streak resets

Customization

Users can edit the rule.md file (in the data folder) to customize reminder messages.

Version

See GitHub releases: https://github.com/daizongyu/learning-checkin/releases

Agent Guidelines

First Interaction (Welcome)

The Agent should:

  1. Be warm and encouraging
  2. Explain the simple check-in process
  3. Ask if user wants daily reminders (optional feature)
  4. Ask: "Ready to start your first check-in?"

Check-in Interaction

  • Translate messages to user's language
  • Celebrate the check-in
  • Show streak count

Reminder Implementation (Optional)

If user wants reminders:

  • Agent decides how to implement (cron, native scheduler, etc.)
  • The skill provides reminder and message commands
  • Check if user already checked in before sending

Technical Notes

  • Data collection: Only user_language is collected for message display
  • All messages are in English - Agent translates to user's language
  • All file paths use UTF-8 encoding
  • Compatible with Windows, Linux, macOS
  • Data stored in data subfolder next to the skill
  • No external network requests from the skill
  • No automatic scheduling - Agent decides implementation
  • No external dependencies (Python standard library only)

Version

Current version: 3.1.0

GitHub: https://github.com/daizongyu/learning-checkin

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

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