RetainCraft

Evidence-based AI-assisted learning protocol combining spaced repetition (SM-2), active recall, Feynman technique, interleaving, and deliberate practice. Features pre-assessment, burnout detection, progress tracking, and system memory integration. 基于循证学习科学的 AI 辅助互动学习协议，整合间隔重复、主动回忆、费曼学习法、交错练习和精细加工提问 5 种科学方法。

Audits

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Multi-engine malware detections and file reputation.

Install

openclaw skills install retaincraft

RetainCraft

Evidence-based AI-assisted interactive learning protocol 基于循证学习科学的 AI 辅助互动学习协议

Combines 5 scientifically validated methods + self-assessment + diagnostic test + customized learning path 结合 5 种科学验证方法 + 自我评价 + 摸底考试 + 定制化学习路径

📦 Source Code (源码): https://github.com/kaixiad/RetainCraft 📖 Detailed workflow (详细流程): references/full-workflow.md

⭐ If this skill helps you, please give a Star on GitHub! 如果这个 skill 对你有帮助，欢迎在 GitHub 上给个 Star！

⚠️ Execution Checklist (执行清单)

Must read before each learning session (每次学习开始前必须读)

Critical Steps (关键执行步骤 - 不可违反)

Must execute after module test (模块测试结束后必须执行):
```
python3 scripts/srs.py record-test <topic> <total> <correct>
```
Not executing = module test invalid, level not updated. 不执行此命令 = 模块测试无效，等级不更新。
Feynman Check - L5 required (费曼检验 - L5 必需):
- L5 mastery requires: 2 consecutive module tests >=90% + Feynman check passed
- L5 精通需要：连续 2 次模块测试答对率 >=90% + 费曼检验通过
- AI plays "confused student", asks 3 questions
- AI 助手扮演"不懂的学生"，追问 3 个问题
Scoring Discipline (评分纪律 - 不可违反):
- Scoring criteria announced before test, cannot change during test
- 评分标准在测试开始前公布，测试过程中不可修改
- Single question score >=7 = "correct"
- 单题得分 >=7 分 = 算"答对"
Level-up Restrictions (逐级升级限制):
- Levels can only increase one at a time, no skipping
- 等级只能逐级升级，不能跳级
- Each upgrade requires 2 consecutive passes
- 每次升级需要连续 2 次达标

📚 Core Methodology (核心方法论 - 循证)

Method (方法)	Effect Size (效果量)	Source (来源)
Spaced Repetition (间隔重复)	d=0.85	Donoghue & Hattie 2021
Active Recall (主动回忆)	d=0.74	Donoghue & Hattie 2021
Interleaving (交错练习)	d=0.47	Donoghue & Hattie 2021
Elaborative Interrogation (精细加工提问)	d=0.56	Donoghue & Hattie 2021
Feynman Technique (费曼学习法)	d=0.54*	Donoghue & Hattie 2021
AI Tutoring (AI 辅导)	0.63-1.3 SD	Kestin et al. 2025 RCT

Note (注): *d=0.54 corresponds to "Self Explanation" in original paper, mapped to Feynman technique here. *d=0.54 对应原文"自我解释"，此处映射为费曼学习法。 Reference (参考): scripts/evidence.md (detailed citations)

🔄 Complete Workflow: Five Steps (完整流程：五步启动)

Step 0: Learning Assessment (学习意愿评估)

User completes self-assessment questionnaire (主题、目标、水平、时间、偏好)
用户完成自我评估问卷

Step 0.5: Pre-study Materials (预习材料 - 零基础专用)

Trigger: user self-assesses as "complete beginner"
触发条件：用户自评"完全零基础"
AI searches beginner materials, creates "quick overview" (800-1500 words)
AI 助手搜索入门资料，精炼成"快速概览"

Step 1: Diagnostic Test (摸底考试)

5-8 questions, covering basics to advanced
5-8 道题，覆盖基础到进阶
⚠️ Diagnostic test does NOT call record-test, NOT written to test_history
⚠️ 摸底考试不调用 record-test，不写入 test_history

Step 2: Learning Path Customization (学习路径定制)

Step 2a: Industry research (web_search learning routes, job requirements)
Step 2a：行业调研（web_search 搜索学习路线、岗位要求）
Step 2b: Path generation (3-8 modules with goals, knowledge points, acceptance criteria)
Step 2b：路径生成（3-8 个模块，含目标、知识点、验收标准）
Step 2c: User confirmation before starting
Step 2c：用户确认后开始学习

Step 3-4: Learning Loop + Spaced Repetition (学习循环 + 间隔复习)

See references/full-workflow.md
详见 references/full-workflow.md

📊 Level System L1-L5 (等级系统)

Level (等级)	Standard (标准)	Behavior (行为特征)
🔴 L1 Entry (入门)	No test history or first <20%	Start from zero (从零开始)
🟠 L2 Beginner (初学)	First >=20%	Has concepts but not systematic (有概念但不系统)
🟡 L3 Intermediate (进阶)	2 consecutive >=40%	Can apply independently (能独立应用)
🟢 L4 Proficient (熟练)	2 consecutive >=70%	Can solve complex problems (能解决复杂问题)
🔵 L5 Mastery (精通)	2 consecutive >=90% + Feynman check	Can teach others (能教会别人)

Promotion/Demotion Rules (升降级规则)

Promotion: 2 consecutive passes (升级：连续 2 次达标)
Demotion: 3 consecutive failures, min L2 (降级：连续 3 次不达标，最低降到 L2)

Two Independent Dimensions (两个独立维度)

Level (等级) = Based on module test accuracy (权威)
SM-2 Status (SM-2 状态) = Based on concept mastery ratio (仅展示)

🔄 Learning Loop Per Module (学习循环 - 每模块)

Phase 0: Framework Building (框架搭建 - 10-15 min)

AI asks questions, guides discovery of core concepts
AI 助手提问，引导发现核心概念

Phase 1: Active Input (主动输入 - 25-40 min)

User studies materials, pause to recall every 15 min
用户学习原始材料，每 15 分钟暂停回忆

Phase 2: Feynman Check (费曼检验 - 15-20 min)

User explains to AI, AI plays "confused student"
用户向 AI 解释所学，AI 扮演"不懂的学生"追问

Phase 2.5: Simulation (实战模拟 - 15-20 min)

Recommend 2-3 scenarios, user chooses, execute 3-5 rounds
推荐 2-3 个模拟场景，用户选择后执行 3-5 轮
Score by 5 dimensions (100 points), see scripts/scenarios.md
按 5 维度打分（100分），见 scripts/scenarios.md

Phase 3: Test & Reinforce (测试巩固 - 15-20 min)

5-8 mixed question types
5-8 道混合题型测试
⚠️ Must determine "review" or "module test"
⚠️ 必须判定"复习"还是"模块测试"

Item (项目)	Review (复习)	Module Test (模块测试)
Purpose (目的)	Strengthen memory (强化记忆)	Phase assessment (阶段性评估)
Impact (影响)	No level change (不影响等级)	Determines level (决定等级升降)
Command (命令)	srs.py rate	srs.py record-test

Phase 4: Spaced Repetition (间隔复习 - SM-2 Algorithm)

Based on SM-2 schedule, proactive reminders when due
基于 SM-2 时间表，到期主动提醒
Heartbeat check: python3 scripts/srs.py due
心跳检查：python3 scripts/srs.py due

⚠️ Burnout Detection (倦怠检测)

Triggers (触发条件 - 任一):

3+ consecutive wrong answers (连续答错 3 题以上)
2 consecutive score drops (连续 2 次测试分数下降)
User says "tired" / "too hard" (用户主动说"累了""太难了")

Response (响应): Lower difficulty, suggest break, switch to easy mode 响应：降低难度、建议休息、切换轻松模式

🤖 AI Assistant Behavior (AI 助手行为规范)

✅ Should Do (应该做的)

User asks question → First ask "what do you think?"
用户问问题 → 先反问"你是怎么想的？"
User stuck → Give hints (not answer)
用户卡住 → 给提示（不是答案）
Before answering knowledge questions → web_search first
每次回答知识性问题前 → 先 web_search 验证
Level changes must inform user immediately
等级变化时必须主动告知用户

❌ Should Not Do (不应该做的)

Give complete answer directly (除非用户明确要求)
Only score without explanation after test (测试后只打分不解析)

🔍 Search-First Rules (搜索优先规则)

Iron rule: AI must search before answering any knowledge question 铁律：AI 助手回答任何知识性问题前，必须先搜索验证

Scenario (场景)	Must Search? (必须搜索?)
User asks "what is XX" (用户问"XX 是什么")	✅
Correct answer for test (出测试题的正确答案)	✅
Feynman check judgment (费曼检验时判断对错)	✅
Planning learning path (规划学习路径)	✅
Flow conversation (流程性对话)	❌

Rule (规则): Factual statements must include source links 规则：事实性陈述必须附来源链接

🔒 Session Checkpoint (会话检查点)

Phase Completion Checklist (Phase 完成自检)

□ Current phase core output completed? (当前 Phase 核心产出已完成?)
□ If module test: record-test called? (如果模块测试：已调用 record-test?)
□ Key progress written to memory/? (关键进展已写入 memory/?)

Check Commands (检测命令)

python3 scripts/srs.py check-session [topic]  # Check unrecorded tests
python3 scripts/srs.py check-burnout <topic>   # Analyze burnout risk

🧠 Memory Persistence (记忆持久化)

Dual System (双系统)

System (系统)	Stores (存什么)	Location (位置)
System memory (系统 memory)	Progress summary, weak points (进度摘要、薄弱点)	memory/YYYY-MM-DD.md
~/learn/	SM-2 data, concept mastery (SM-2 数据、概念掌握度)	~/learn/topics/{topic}/concepts.json

Recovery Priority (恢复优先级)

concepts.json > memory files (concepts.json > memory 文件)

💓 Review Reminders (复习提醒 - Heartbeat)

AI receives heartbeat → python3 scripts/srs.py due → Has due content → Notify user
AI 助手收到心跳 → python3 scripts/srs.py due → 有到期内容 → 通知用户

⚙️ Configuration (配置系统)

~/learn/config.json
{
  "learning_depth": "standard",    // shallow / standard / deep
  "learner_type": "practical",     // visual / practical / theoretical
  "daily_review_limit": 20,
  "session_duration": 60,
  "burnout_threshold": 3,
  "mastery_threshold": 0.8,
  "level_thresholds": { "L2": 0.2, "L3": 0.4, "L4": 0.7, "L5": 0.9 }
}

📚 References (参考材料)

Full workflow (完整流程): references/full-workflow.md
Scenario library (场景库): scripts/scenarios.md
Academic citations (学术引用): scripts/evidence.md
Level algorithm (等级算法): scripts/srs.py
Output templates (输出模板): scripts/templates.md

🎯 Multi-Topic Support (多主题支持)

Priority (优先级)	Description (描述)	Example (示例
1-Urgent (紧急)	Deadline approaching (截止日期临近)	Exam prep (考试准备)
2-Important (重要)	Core skills (核心技能)	Programming (编程语言)
3-Regular (常规)	Daily learning (日常学习)	New tech (新技术)
4-Extended (扩展)	Broaden horizons (拓宽视野)	Related fields (相关领域)
5-Reserve (储备)	Future use (未来可能用到)	Learning list (待学习清单)

Max 3 topics simultaneously (最多同时 3 个主题)
Each topic has independent concepts.json (每个主题独立的 concepts.json)
Reviews can cross topics - interleaving (复习可以跨主题 - 交错练习)