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Learning Cards (Spaced Repetition)

by Madoka · GitHub ↗ · v1.1.1 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install learning-cards
Description
Spaced-repetition flashcard system backed by Feishu Bitable (multi-dimensional table). Create flashcards from any book or knowledge base, quiz the user inter...
README (SKILL.md)

Learning Cards

Spaced-repetition flashcard system on Feishu Bitable.

基于飞书多维表格的间隔复习学习卡片系统。

Full design details: references/system-design.md

完整设计文档:references/system-design.md

Prerequisites

This skill requires Feishu (Lark) Bitable access. Ensure the following before use:

本技能依赖飞书多维表格。使用前请确认以下条件:

  1. OpenClaw Feishu plugin configured — The openclaw-lark plugin must be installed and connected to a Feishu tenant. See OpenClaw Feishu setup docs.

    OpenClaw 飞书插件已配置 — 需安装 openclaw-lark 插件并连接到飞书租户。

  2. User OAuth authorization — The user must grant Bitable read/write permissions when prompted (standard Feishu OAuth flow, handled automatically by OpenClaw).

    用户 OAuth 授权 — 用户需在首次使用时授权多维表格读写权限(OpenClaw 自动处理)。

  3. Bitable app scope — The Feishu app needs scopes: bitable:app, bitable:app:readonly. These are standard in the OpenClaw Feishu plugin.

    多维表格权限范围 — 飞书应用需开通 bitable:appbitable:app:readonly 权限(OpenClaw 飞书插件标配)。

No additional API keys or credentials are needed — authentication is handled by the OpenClaw Feishu plugin's existing OAuth infrastructure.

无需额外 API 密钥 — 认证由 OpenClaw 飞书插件的 OAuth 基础设施统一处理。

Setup

Create a Bitable app with one table containing these fields.

创建一个飞书多维表格,包含以下字段。

Content fields / 内容字段

Field Type Note
概念名 Text (primary) Card title / 卡片标题
阶段 SingleSelect Major phase / 大阶段
站点 SingleSelect Chapter/unit / 章节
类型 SingleSelect e.g. 核心概念 / 隐喻 / 转变工具 / 关键问题
正面(问题) Text Question / 问题
背面(答案) Text Answer / 答案
我的理解 Text Learner's own notes / 学习者笔记

Tracking fields / 追踪字段

Field Type Note
掌握程度 SingleSelect 未学习 / 初步了解 / 基本掌握 / 深度理解
学习顺序 Number Sequential order for first pass / 首轮学习顺序
首次学习时间 DateTime First study timestamp (ms) / 首次学习时间戳
上次复习 DateTime Last study timestamp (ms) / 上次学习时间戳
下次复习时间 DateTime Next review date (ms) / 下次复习日期
学习次数 Number Total attempts / 总答题次数
连续答对次数 Number Streak — drives interval / 连对次数,决定间隔
答错次数 Number Cumulative errors / 累计答错
最近得分 Number Last score (1-5) / 最近得分

Three Learning Phases

Phase 1: Sequential coverage / 顺序覆盖

First pass through all cards in order by 学习顺序. Goal: build a mental map of the entire knowledge base.

按学习顺序首轮过一遍,建立全局地图感。

Phase 2: Spaced review / 间隔复习

After first pass. Schedule by 下次复习时间. Mix in remaining new cards.

首轮完成后按复习时间调度,穿插新卡。

Phase 3: Mastery & transfer / 融会贯通

Cross-topic quizzes, scenario-based questions, reverse prompts (give answer → guess concept).

跨章节混合抽问、情境题、反向提问(给答案猜概念)。

Quiz Flow

1. Read all cards from Bitable
2. Compute recent accuracy (last 5 scores) → determine new:review ratio
3. Select cards:
   - Review: 下次复习时间 \x3C= today, sorted by overdue days
   - New: 学习次数 = 0, sorted by 学习顺序
4. Mix per ratio, present one at a time
5. After each answer: score → show answer → update Bitable

Card presentation format / 出题格式

第 X 题(复习/新卡)
> [概念名] | [阶段] | [站点] | [类型]
[正面问题]

User can say "讲讲" to skip answering and see the explanation directly (score = 0).

用户可说"讲讲"跳过作答,直接看答案(计 0 分)。

85% Rule — Dynamic Ratio

Target ~85% accuracy for optimal learning speed (Wilson et al., Nature Communications, 2019).

目标保持 85% 正确率以获得最优学习速度。

Recent accuracy New:Review Logic
> 90% 3:1 Too easy, add new / 太轻松,加新卡
80–90% 2:1 Optimal zone / 最优区间
70–80% 1:1 More review / 多复习
\x3C 70% 1:2 Consolidate first / 先巩固

Accuracy = proportion of scores >= 4 in last 5 attempts.

正确率 = 最近 5 次中得分 >= 4 的比例。

Spaced Review Algorithm

Interval based on consecutive correct answers (连续答对次数):

Streak Interval
0 1 day
1 1 day
2 3 days
3 7 days
4 14 days
5 30 days
6+ 90 days

Post-Answer Update

After each answer, immediately update the card record:

每题答完立即更新卡片记录:

学习次数 += 1
最近得分 = score
上次复习 = today
首次学习时间 = today  (if null)

if score >= 4:       连续答对次数 += 1
elif score \x3C= 2:     连续答对次数 = 0; 答错次数 += 1 (if score > 0)
else (score == 3):   streak unchanged

intervals = [1, 1, 3, 7, 14, 30, 90]
下次复习时间 = today + intervals[min(streak, 6)] days  (if score >= 4)
               today + 1 day                           (otherwise)

掌握程度:
  streak >= 5 → 深度理解
  streak >= 3 → 基本掌握
  studied    → 初步了解

Card Generation Guidelines

When creating cards from a book:

从书籍生成卡片时:

  • 2–4 cards per chapter for conceptual books / 概念类书籍每章 2-4 张
  • 1–2 per tool/API for technical books / 技术类每个工具 1-2 张
  • Target 30–50 cards total / 总量 30-50 张
  • Questions should be specific, not vague / 问题要具体
  • Answers: 150–300 chars, use ①②③ for key points / 答案 150-300 字,序号标注

User Commands

Command Action
继续学习 / continue Default 3 cards, dynamic ratio / 默认 3 张,动态配比
来 N 张 / give me N Specific count / 指定数量
讲讲 / explain Show answer directly / 直接看答案
学习进度 / progress Show overall stats / 显示整体进度
薄弱点 / weak points List cards with most errors / 列出高错卡片
Usage Guidance
This skill is coherent and appears to do what it says: it needs the OpenClaw Feishu (Lark) plugin and your Feishu OAuth consent so it can create and update records in a Feishu Bitable. Before installing/using: (1) confirm you trust the OpenClaw Feishu integration and the Feishu tenant where data will be stored, (2) be aware that your learning data (cards, answers, timestamps, scores) will be written to that Bitable and accessible to anyone with access to that app/table in the tenant, and (3) check the OAuth scopes requested by the plugin (read/write Bitable) and only grant them if acceptable. If you need offline or local-only storage, this skill’s cloud-backed design may not meet that need.
Capability Analysis
Type: OpenClaw Skill Name: learning-cards Version: 1.1.1 The 'learning-cards' skill is a legitimate spaced-repetition flashcard system that integrates with Feishu (Lark) Bitable. The code and instructions in SKILL.md and references/system-design.md focus entirely on educational logic, including the '85% Rule' for optimal learning and automated scheduling based on user performance. It uses the standard OpenClaw Feishu plugin for data storage and lacks any indicators of data exfiltration, malicious execution, or prompt injection attacks.
Capability Assessment
Purpose & Capability
Name/description match the runtime instructions: all actions described are about creating, reading, updating, and scheduling cards in a Feishu Bitable. The declared dependency on the OpenClaw Feishu plugin and Feishu OAuth is coherent with a Bitable-backed flashcard system.
Instruction Scope
SKILL.md confines behavior to reading/writing Bitable records, scheduling cards, and generating/quiz flows. It explicitly requires user OAuth via the OpenClaw Feishu plugin and lists Bitable actions (create, batch_create, table_record updates). Note: the skill will write personal learning data into the user's Feishu tenant (Bitable) and may create apps/tables in that tenant — this is expected but important for privacy/owner awareness.
Install Mechanism
Instruction-only skill with no install spec, no downloads, and no code files — lowest-risk install mechanism. It relies on platform-provided OpenClaw Feishu integration rather than bundling external packages.
Credentials
No environment variables, binaries, or secret keys are requested by the skill. The only required credentialing is the normal Feishu OAuth handled by the OpenClaw Feishu plugin, which is proportional to the stated purpose.
Persistence & Privilege
always is false and autonomous invocation is allowed by default (normal for skills). The skill does not request persistent platform privileges beyond using the OpenClaw Feishu plugin for Bitable access and does not modify other skills or system-wide settings.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install learning-cards
  3. After installation, invoke the skill by name or use /learning-cards
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.1.1
v1.1.1: Fix rendering — separate EN/CN into distinct paragraphs, use proper numbered lists and blank lines between items for clean web display.
v1.1.0
v1.1.0: Added Feishu prerequisites/auth documentation. Bilingual (EN/CN) throughout. Resolved ClawHub feedback about missing credential configuration.
v1.0.0
Initial release: Feishu Bitable-backed spaced repetition flashcard system with 85% Rule dynamic difficulty, three learning phases, and 16-field progress tracking.
Metadata
Slug learning-cards
Version 1.1.1
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 3
Frequently Asked Questions

What is Learning Cards (Spaced Repetition)?

Spaced-repetition flashcard system backed by Feishu Bitable (multi-dimensional table). Create flashcards from any book or knowledge base, quiz the user inter... It is an AI Agent Skill for Claude Code / OpenClaw, with 257 downloads so far.

How do I install Learning Cards (Spaced Repetition)?

Run "/install learning-cards" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Learning Cards (Spaced Repetition) free?

Yes, Learning Cards (Spaced Repetition) is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Learning Cards (Spaced Repetition) support?

Learning Cards (Spaced Repetition) is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Learning Cards (Spaced Repetition)?

It is built and maintained by Madoka (@guoqunabc); the current version is v1.1.1.

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