🧠 RetainCraft

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Evidence-based AI-assisted interactive learning protocol for OpenClaw

Previously known as "interactive-learning"

What is this?

An OpenClaw skill that turns 5 scientifically validated learning methods into an interactive system between you and your AI.

Not "here's some material, figure it out yourself" — your AI studies with you, quizzes you, tracks your progress, and reminds you before you forget.

Features

• 5 evidence-based methods in one protocol: spaced repetition (d=0.85), active recall (d=0.74), Feynman technique (d=0.54), interleaved practice (d=0.47), elaborative interrogation (d=0.56) — all effect sizes from Donoghue & Hattie 2021 meta-analysis (242 studies, 169k participants)
• SM-2 spaced repetition: auto-calculates review intervals, not fixed schedules
• Pre-assessment + module tests: quantifies learning with before/after comparison
• Burnout detection: auto-lowers difficulty or suggests breaks after consecutive mistakes
• Search-first policy: AI verifies facts before answering, cites sources
• Persistent memory: learning data survives across sessions
• Heartbeat integration: auto-reminds when reviews are due

Prerequisites

• Python >= 3.10
• No external dependencies — Python standard library only

Windows users: use python instead of python3 if the latter is not available.

Installation

# From ClawHub
openclaw skills install retaincraft

# Manual
git clone https://github.com/kaixiad/RetainCraft.git ~/.openclaw/workspace/skills/retaincraft

Usage

Tell your AI:

• "I want to learn linear algebra"
• "Teach me Bayes' theorem"
• "Help me make a study plan"

The AI will automatically start the full learning workflow.

File Structure

retaincraft/
├── SKILL.md                    # Main file (execution checklist + workflow)
├── README.md                   # English readme
├── README.zh-CN.md             # Chinese readme
├── LICENSE                     # MIT License
├── CHANGELOG.md                # Version history
├── CONTRIBUTING.md             # Contribution guide
├── requirements.txt            # Python version requirement
├── .github/
│   ├── workflows/ci.yml        # GitHub Actions CI/CD
│   ├── ISSUE_TEMPLATE/         # Issue templates
│   └── pull_request_template.md # PR template
├── docs/
│   └── docu-review-report.md   # Documentation audit report
└── scripts/
    ├── srs.py                  # SM-2 spaced repetition engine + level system
    ├── test_srs.py             # Unit tests (127 test cases)
    ├── scenarios.md            # Simulation scenario library (7 scenarios)
    ├── evidence.md             # Academic citations and effect sizes
    └── templates.md            # Output format templates

Data Storage

~/learn/
├── topics/{topic}/
│   ├── concepts.json           # SM-2 state per concept
│   ├── notes.md                # Learning notes
│   └── progress.md             # Mastery tracking
├── test_history.json           # Module test history
├── simulation_history.json     # Simulation history
└── config.json                 # Learning preferences

CLI Commands

python3 scripts/srs.py init <topic>              # Create a topic
python3 scripts/srs.py add <topic> <concept>     # Add a concept
python3 scripts/srs.py review <topic>            # Start review session (interactive)
python3 scripts/srs.py rate <topic> <concept> <rating>  # Rate concept (non-interactive, for AI)
python3 scripts/srs.py due                       # Show today's due reviews
python3 scripts/srs.py status [topic]            # Overview / single topic status
python3 scripts/srs.py record-test <topic> <total> <correct>  # Record module test result
python3 scripts/srs.py test-history [topic]      # View test history
python3 scripts/srs.py record-simulation <topic> <scenario> <score> [--rounds N]  # Record simulation
python3 scripts/srs.py simulation-history [topic]  # View simulation history
python3 scripts/srs.py config                    # View/set configuration

Academic References

Method	Effect Size	Source
Spaced Repetition	d=0.85	Donoghue & Hattie 2021
Active Recall	d=0.74	Donoghue & Hattie 2021
Elaborative Interrogation	d=0.56	Donoghue & Hattie 2021
Self-Explanation / Feynman	d=0.54	Donoghue & Hattie 2021
Interleaved Practice	d=0.47	Donoghue & Hattie 2021
AI Tutoring	0.63-1.3 SD	Kestin et al. 2025 (Harvard RCT, N=194)

All d values from Donoghue & Hattie (2021) meta-analysis (242 studies, 1,619 effect sizes, 169,179 participants). Dunlosky et al. (2013) uses qualitative classification (high/moderate/low utility), not Cohen's d.

Known Limitations

• SM-2 algorithm: This is a well-proven but decades-old algorithm. FSRS (a modern ML-based alternative) migration is planned for a future release.
• No user validation data yet: The learning methods are evidence-based, but this specific implementation has not yet been validated with real users at scale.
• AI judgment in Feynman test: The AI evaluates whether your explanation is correct. This relies on the underlying LLM's accuracy — cross-check critical knowledge with authoritative sources.
• Single-language interface: CLI output and documentation are primarily in Chinese. English interface support is planned.

Documentation Quality

This project underwent an independent documentation audit:

Item	Status
All citations verified as real	✅
Effect size numbers accurate	✅
Research institution attribution correct	✅
Protocol logic consistent	✅
Code tests passing	✅
Needs customization	⚠️ Generic template — adapt to your background

Full audit report: docs/docu-review-report.md

AI-Assisted Development Disclosure

This project was developed with assistance from MiMo-v2.5-Pro, OpenClaw, WorkBuddy, and CodeBuddy. Core architecture design, learning methodology selection, academic citation verification, and code review were done by hand. AI tools assisted with code generation, documentation drafting, and literature search. All AI-generated content has been manually reviewed and verified.

License

MIT License — free to use, modify, and distribute.