hamsterider-m Deep Learning Orchestrator (Enhanced)
Automated deep learning workflow using NotebookLM as the core engine. Handles material collection, knowledge synthesis, and multi-format artifact generation with intelligent intent detection.
🎯 Quick Start
User provides a topic or materials:
"Help me deeply understand Kubernetes architecture"
"Research this article: https://..."
"Learn from this video: https://youtube.com/..."
"帮我学习 Rust 内存安全"
"总结一下这篇文章的核心观点"
Auto-executed workflow:
- Detect learning intent from context and keywords
- Collect materials (via
jina-reader,bilibili-subtitle,ultimate-search, etc.) - Create NotebookLM notebook and upload sources
- Generate all artifacts in parallel (report, podcast, slides, video, quiz, flashcards)
- Download and send to user via Feishu + save to Obsidian
🏗️ Architecture
deep-learning 是 orchestrator，不是 all-in-one：
deep-learning (orchestrator)
↓
content-bridge (内容摄取统一入口)
↓
独立摄取 skills (weixin/bilibili/youtube/web/document)
↓
NotebookLM (知识合成引擎)
↓
多格式产物 (report/podcast/slides/quiz)
📦 Dependencies
content-bridge: 内容摄取路由层bilibili-subtitle: B站字幕提取（通过 content-bridge 调用）notebooklm-py: NotebookLM CLIultimate-search: 网络搜索（可选）
🔥 Trigger System
Explicit Triggers
- "帮我学习 X", "深入了解 X", "研究一下 X"
- "深度学习 X", "全面了解 X", "系统学习 X"
Implicit Triggers
- "总结一下", "听不懂", "解释一下"
- "教程", "入门", "指南"
- User shares long article/URL then asks questions
When detected: Ask "是否需要深度学习？我可以生成报告、播客、PPT、测试题等。"
📦 Material Collection
Supported sources:
| Source Type | Handler | Auto-Detection |
|---|---|---|
| Web articles | jina-reader |
https:// URLs |
| YouTube videos | anything-to-notebooklm |
youtube.com, youtu.be |
| Bilibili videos | bilibili-subtitle |
bilibili.com, BV* |
| X/Twitter posts | jina-reader |
x.com, twitter.com |
| Reddit posts | jina-reader |
reddit.com |
| Medium articles | jina-reader |
medium.com |
| LinkedIn posts | jina-reader |
linkedin.com |
| Documents (PDF/DOCX) | anything-to-notebooklm |
File paths |
| Search keywords | ultimate-search + NotebookLM research |
Plain text |
Social media handling: All social platform links automatically processed via jina-reader for clean extraction.
🎨 Artifact Generation
Default Artifacts (Always Generated)
- 📄 Study Guide Report (5-15 min) - Comprehensive learning guide
- 🎙️ Audio Podcast (10-20 min) - Deep-dive audio discussion
- 📊 Slide Deck (15-30 min) - Detailed presentation (PDF)
- 🗺️ Mind Map (instant) - Visual knowledge structure
- ❓ Quiz (5-15 min) - Medium difficulty test questions
Optional Artifacts (Configurable)
- 🎬 Video Brief (10-20 min) - Narrated slideshow video (MP4)
- 📊 Infographic (5-10 min) - Visual summary (PNG)
- 🃏 Flashcards (5-10 min) - Memory aid cards (JSON)
Research Modes
| Mode | Duration | Use Case |
|---|---|---|
| Fast Research | 10-20 seconds | Quick multi-angle source collection |
| Deep Research | 2-30 minutes | Comprehensive single-topic deep dive |
Default: Deep Research (auto-selected when no materials provided)
🛠️ Scripts
# Main orchestrator
scripts/orchestrate.sh \
--topic "Kubernetes 架构" \
--materials "https://..." "https://..." \
--research-mode deep \
--artifacts "report audio slides quiz video"
# Config loader
source scripts/config_loader.sh
load_config
# Prompt selector
scripts/prompt_selector.sh --intent summarize
scripts/prompt_selector.sh --category analysis
scripts/prompt_selector.sh --random
📋 Configuration
Edit config/default.conf or set environment variables:
# Artifact generation
DEFAULT_ARTIFACTS="report audio slides mindmap quiz"
ENABLE_VIDEO=true
ENABLE_INFOGRAPHIC=false
ENABLE_FLASHCARDS=true
# Research mode
DEFAULT_RESEARCH_MODE="deep" # fast | deep
# Obsidian integration
OBSIDIAN_VAULT_PATH="$HOME/obsidian-vault"
ENABLE_OBSIDIAN_INTEGRATION=true
# Timeouts (seconds)
ARTIFACT_TIMEOUT=1800
RESEARCH_TIMEOUT_DEEP=1800
RESEARCH_TIMEOUT_FAST=60
🧠 Prompt Templates
Located in config/prompts/:
Basic (基础提问)
- Summarize key points
- Explain in simple terms
- Relate to known concepts
- Provide practical examples
- Create teaching outline
Analysis (深度分析)
- Identify core controversies
- Compare different schools of thought
- Trace historical evolution
- Predict future trends
- Clarify common misconceptions
Practical (实用场景)
- Workplace application
- Personalized advice
- Learning path design
- Resource recommendations
Creative (创意生成)
- Training curriculum design
- Beginner-friendly adaptation
- Test question generation
- Analogy-based explanations
Usage:
# Select prompt by intent
./prompt_selector.sh --intent summarize
./prompt_selector.sh --intent compare
./prompt_selector.sh --intent teach
# Select by category
./prompt_selector.sh --category analysis
# Random prompt for inspiration
./prompt_selector.sh --random
⚡ Execution Pattern
Use subagents for long-running tasks:
- Source processing (30s-10min per source)
- Artifact generation (5-45min per artifact)
- Research (2-30min for deep mode)
Main conversation continues while subagents work in background.
📤 Output & Delivery
All artifacts are:
- Downloaded to
~/.openclaw/workspace/deep-learning-output/<notebook-id>/ - Sent to user via Feishu with formatted summary
- Saved to Obsidian Inbox (if enabled)
- Quiz questions sent as interactive message (if supported)
Feishu message template:
📚 深度学习完成：[主题]
✅ 已保存到 Obsidian Inbox
📂 Research Mode: deep
产物：
📄 学习报告
📊 PPT 讲义
🎙️ 音频播客
❓ 测试题
🎬 视频解说
🔗 NotebookLM: https://notebooklm.google.com/notebook/[id]
🔧 Error Handling
Source processing fails:
- Log warning, continue with successful sources
- Minimum 1 source required to proceed
- Retry once for transient failures
Artifact generation fails:
- Retry once after 5 minutes
- If still fails, skip that artifact and continue
- Report which artifacts succeeded/failed
Rate limiting:
- Wait 10 minutes, retry once
- If persistent, suggest manual retry later
- Provide notebook URL for manual access
📊 Time Estimates
| Phase | Typical Duration |
|---|---|
| Material collection | 1-5 min |
| Source processing | 2-10 min |
| Fast Research | 10-20 sec |
| Deep Research | 2-30 min |
| Report generation | 5-15 min |
| Podcast generation | 10-20 min |
| Slide generation | 15-30 min |
| Video generation | 10-20 min |
| Quiz generation | 5-15 min |
| Download & send | 1-2 min |
Total: 10-60 minutes (most work happens in parallel via subagents)
📝 Dependencies
Required skills:
notebooklm- Core NotebookLM operationsjina-reader- Web page extraction (social media support)
Optional skills (auto-detected):
bilibili-subtitle- Bilibili video transcriptionultimate-search- Web search enhancementanything-to-notebooklm- Multi-format document support
🎯 Best Practices
- Always specify topic clearly - Better input = better output
- Provide materials when available - More control over sources
- Use Fast Research for quick overviews - Save time on simple topics
- Use Deep Research for complex subjects - Comprehensive coverage
- Enable video for visual learners - Great for presentations
- Review quiz questions - Adjust difficulty if needed
🚀 Examples
Example 1: Learn from URL
User: "帮我学习这篇文章 https://example.com/kubernetes
Assistant: 🚀 Starting deep learning workflow...
[Collects article, creates notebook, generates artifacts]
✅ 深度学习完成：Kubernetes 架构
Example 2: Keyword Research
User: "我想深入了解 Rust 的所有权机制"
Assistant: 🔬 No materials provided, activating Deep Research mode...
[Searches for 15+ sources, creates comprehensive notebook]
✅ 深度学习完成：Rust 所有权机制
Example 3: Video Tutorial
User: "把这个 B 站视频做成学习材料 https://bilibili.com/video/BV1xx"
Assistant: 📥 Collecting materials...
[Extracts subtitles, generates video brief + slides + quiz]
✅ 深度学习完成：[Video topic]
Example 4: Quick Summary
User: "总结一下这个概念，太快了看不懂"
Assistant: ⚡ Fast Research mode activated...
[Quick 10-20 second research, generates summary report]
✅ 快速总结完成