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study-habits学习习惯

Agent Skill

study-habits 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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

17,468

周安装

834

GitHub Stars

公开资料未说明

下载量

9,707
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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

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:study-habits(学习习惯)
来源仓库:https://github.com/clawdbot/skills
仓库路径:skills/study-habits
安装命令:
npx skills add clawdbot/skills --skill "study-habits"
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 npx skills 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。

AgentSkills.tonpx skills
npx skills add clawdbot/skills --skill "study-habits"

简介

study-habits 用于发现并推荐可用的 AI 代理技能,扩展宿主环境能力边界。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中需要动态加载新技能时使用。
  • 通过 npx 命令添加指定技能路径即可完成集成,无需复杂配置。
  • 注意部分技能可能需要特定宿主版本支持,建议先查阅文档确认兼容性。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

name
study-habits
description
Build effective study habits with spaced repetition, active recall, and session tracking
author
clawd-team
version
1.0.0
triggers

Study Habits

*Learning that sticks—through science, not stubbornness.*

What it does

This skill transforms how you absorb and retain information by combining proven cognitive techniques with persistent session tracking:

  • Study Session Tracking - Logs when you study, what topic, duration, and effectiveness rating for accountability and pattern recognition
  • Technique Suggestions - Recommends study methods based on your learning goal (memorization vs. deep understanding vs. skill practice)
  • Spaced Repetition Reminders - Intelligently schedules review sessions to hit the sweet spot where forgetting begins
  • Progress Dashboard - Shows your study velocity, topic mastery levels, and retention curves over time
  • Exam Countdown - Builds personalized prep schedules that work backward from exam date to ensure full coverage

Usage

Start study : "Start a 50-minute study session on photosynthesis" → Creates a session timer, suggests an optimal study technique, and tracks your focus

Log topic : "I just finished studying Chapter 3, felt confident" → Records the session, captures confidence level, determines next review interval

Review schedule : "When should I review calculus next?" → Shows which topics need review based on spaced repetition algorithm, prioritizes by forgetting curve

Check progress : "Show me my study stats" → Displays sessions completed, topics covered, retention trends, time invested per subject

Exam countdown : "I have an exam in 21 days on biology" → Creates a study plan that distributes chapters across available time, accounts for review cycles, flags high-risk topics

Study Techniques

Active Recall : Test yourself without looking at notes. Forces your brain to retrieve information rather than passively reread. Far more effective than review.

Spaced Repetition : Review material at increasing intervals (1 day, 3 days, 1 week, 2 weeks). This combats the forgetting curve and moves knowledge to long-term memory.

Pomodoro Technique : Study in 25-minute focused bursts with 5-minute breaks. Prevents burnout and maintains attention during sessions.

Feynman Technique : Explain a concept aloud as if teaching it to someone with no background. Exposes gaps in understanding immediately.

Interleaving : Mix different topics or problem types in one session instead of blocking them. Builds flexible knowledge and stronger pattern recognition.

Tips

  1. Track confidence, not just completion — Rate how well you understood each topic (1-10) rather than just marking it done. This surfaces weak areas early.
  1. Use active recall over rereading — Flashcards, practice problems, and explain-it-aloud beat passively reviewing notes by 10x.
  1. Study in shorter sprints, more often — Three 45-minute sessions spread across a week beat one 2-hour cramming session. Your brain consolidates overnight.
  1. Review the day after, then space out — First review should be 24 hours later, then 3 days, then a week. The algorithm handles this automatically.
  1. All data stays local on your machine — Your study history, notes, and progress never leave your device. Full privacy, full control.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

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

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。

平台分布

windsurf

29.1%
按下载量换算2,825

Codex

24.38%
按下载量换算2,367

OpenCode

19.55%
按下载量换算1,898

Cursor

12.85%
按下载量换算1,247

Claude Code

8.64%
按下载量换算839

Antigravity

3.82%
按下载量换算371

安全审计

暂无安全审计结果可展示。

权限和风险

权限需确认

当前来源未能明确判断权限范围,默认进入异常复核队列。

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。

来源信息

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