Setup
On first use, read setup.md for activation boundaries and context capture priorities.
When to Use
Use this skill when the user is studying with Quizlet and needs better set design, mode selection, session planning, or recovery from weak retention.
Architecture
Memory lives in ~/quizlet/. See memory-template.md for structure and status fields.
CODEBLOCK0
Quick Reference
Use the smallest relevant file for faster and more accurate recommendations.
| Topic | File |
|---|
| Setup process | INLINECODE3 |
| Memory template |
memory-template.md |
| Building high-yield sets |
set-design.md |
| Choosing study modes |
study-modes.md |
| Diagnosing poor retention |
diagnostics.md |
| Import and cleanup workflows |
imports.md |
Core Rules
1. Start from the Assessment Goal
- - Confirm course, exam date, and target outcome before proposing any card creation workflow.
- If the goal is unclear, ask one short question before giving detailed steps.
2. Keep Every Card Atomic and Testable
- - One prompt must test one fact, one concept, or one decision.
- Rewrite multi-answer prompts immediately because they create false confidence.
3. Match Study Mode to the Objective
- - Use Learn for early acquisition, Test for exam simulation, and Flashcards only for fast recall warmups.
- If the user has little time, prioritize modes that expose weak recall instead of passive review.
4. Convert Misses into Card Improvements
- - After every missed answer pattern, recommend a concrete rewrite to reduce ambiguity.
- Track recurring misses in
~/quizlet/weak-cards.md to prevent repeating the same mistakes.
5. Preserve Context and Terminology
- - Keep subject tags, source context, and domain-specific wording on each card set.
- Avoid generic prompts that can apply to multiple domains without clear cues.
6. Keep Advice Platform-Realistic
- - Recommend only workflows supported by Quizlet set editing, import format, and study modes.
- If a requested feature is not native, offer a practical workaround instead of pretending it exists.
7. Protect Data Boundaries
- - Store only study preferences and workflow notes in
~/quizlet/. - Never request login secrets, payment information, or unrelated personal data.
Common Traps
- - Writing definition lists instead of atomic prompts -> lower retention and weak transfer under exam pressure.
- Spending all time in Flashcards mode -> recognition improves while recall under test conditions stays weak.
- Keeping distractors obviously wrong -> test scores look high without real understanding.
- Importing raw notes without cleanup -> duplicated or noisy cards increase review fatigue.
- Ignoring missed-question patterns -> the same weak cards fail repeatedly.
- Mixing unrelated topics in one set -> context switching reduces recall speed.
Data Storage
- - Local notes only in
~/quizlet/ for memory, weak-card logs, and reusable set patterns. - Keep stored data minimal: study goals, performance patterns, and approved workflows.
- Do not store passwords, private identifiers, or unnecessary personal information.
Security & Privacy
Data that leaves your machine:
- - None by default. This skill provides workflow guidance and local note structure only.
Data that stays local:
- - Study context and planning notes in
~/quizlet/.
This skill does NOT:
- - Log in to Quizlet automatically.
- Scrape private user data from browser sessions.
- Make undeclared network requests.
- Store files outside
~/quizlet/ for memory. - Modify its own skill definition files.
Related Skills
Install with
clawhub install <slug> if user confirms:
- -
anki - Spaced repetition card design and retention tuning for Anki workflows. - INLINECODE16 - Core flashcard writing rules and question quality patterns.
- INLINECODE17 - Quiz construction and scoring logic for assessment scenarios.
- INLINECODE18 - Structured study planning and session management workflows.
- INLINECODE19 - Exam-specific preparation, prioritization, and review strategy.
Feedback
- - If useful: INLINECODE20
- Stay updated: INLINECODE21
设置
首次使用时,请阅读 setup.md 以了解激活边界和上下文捕获优先级。
使用时机
当用户正在使用 Quizlet 学习,需要更好的学习集设计、模式选择、学习计划制定,或从薄弱记忆状态中恢复时,使用本技能。
架构
记忆文件存储在 ~/quizlet/ 目录下。结构及状态字段请参见 memory-template.md。
text
~/quizlet/
|-- memory.md # 状态、激活边界及学习上下文
|-- set-playbooks.md # 按学科和目标分类的可复用学习集模式
|-- weak-cards.md # 重写的卡片及反复出现的失败模式
-- session-plans.md # 限时学习计划及考试倒计时策略
快速参考
使用最相关的文件,以获得更快、更准确的建议。
memory-template.md |
| 构建高效学习集 | set-design.md |
| 选择学习模式 | study-modes.md |
| 诊断记忆薄弱 | diagnostics.md |
| 导入与清理工作流 | imports.md |
核心规则
1. 从评估目标出发
- - 在提出任何卡片创建工作流之前,先确认课程、考试日期和预期成果。
- 如果目标不明确,在给出详细步骤前先问一个简短的问题。
2. 保持每张卡片的原子性和可测试性
- - 一个提示应只测试一个事实、一个概念或一个决策。
- 立即重写多答案提示,因为它们会造成虚假的自信。
3. 根据目标匹配学习模式
- - 早期学习使用学习模式,考试模拟使用测试模式,闪卡模式仅用于快速回忆热身。
- 如果用户时间有限,优先选择能暴露薄弱回忆的模式,而非被动复习。
4. 将错误转化为卡片改进
- - 每次出现错误答案模式后,建议进行具体的重写以减少歧义。
- 在 ~/quizlet/weak-cards.md 中跟踪反复出现的错误,防止重复犯同样的错误。
5. 保留上下文和术语
- - 在每个卡片组中保留学科标签、来源上下文和领域特定用语。
- 避免使用没有明确线索、可适用于多个领域的通用提示。
6. 保持建议的平台可行性
- - 仅推荐 Quizlet 学习集编辑、导入格式和学习模式所支持的工作流。
- 如果请求的功能并非原生支持,提供实用的变通方案,而非假装其存在。
7. 保护数据边界
- - 仅在 ~/quizlet/ 中存储学习偏好和工作流笔记。
- 绝不要求提供登录密码、支付信息或无关的个人数据。
常见陷阱
- - 编写定义列表而非原子化提示 → 记忆效果差,考试压力下知识迁移能力弱。
- 将所有时间花在闪卡模式上 → 识别能力提升,但考试条件下的回忆能力依然薄弱。
- 干扰项设置明显错误 → 考试成绩看似很高,实则缺乏真正理解。
- 导入原始笔记而不清理 → 重复或杂乱的卡片增加复习疲劳。
- 忽略错题模式 → 同一薄弱卡片反复出错。
- 在一个学习集中混合不相关主题 → 上下文切换降低回忆速度。
数据存储
- - 仅在 ~/quizlet/ 中存储本地笔记,用于记忆、薄弱卡片日志和可复用学习集模式。
- 保持存储数据最小化:学习目标、表现模式及经批准的工作流。
- 不存储密码、私人标识符或不必要的个人信息。
安全与隐私
离开你设备的数据:
- - 默认无。本技能仅提供工作流指导和本地笔记结构。
保留在本地设备的数据:
- - ~/quizlet/ 中的学习上下文和计划笔记。
本技能不会:
- - 自动登录 Quizlet。
- 从浏览器会话中抓取私人用户数据。
- 发起未声明的网络请求。
- 在 ~/quizlet/ 之外存储记忆文件。
- 修改自身的技能定义文件。
相关技能
如果用户确认,使用 clawhub install 安装:
- - anki - 针对 Anki 工作流的间隔重复卡片设计与记忆调优。
- flashcards - 核心闪卡编写规则与问题质量模式。
- quiz - 针对评估场景的测验构建与评分逻辑。
- study - 结构化学习计划与学习过程管理工作流。
- exam - 针对考试的备考、优先级排序与复习策略。
反馈
- - 如果觉得有用:clawhub star quizlet
- 保持更新:clawhub sync