Voice Memo Sync

# Run installation script / 运行安装脚本
cd ~/.openclaw/workspace/skills/voice-memo-sync
./scripts/install.sh

# Install for Metal acceleration (recommended)
brew install whisper-cpp

Input (File/URL/Text)
        │
        ▼
┌─────────────────────────────────────┐
│     1. Source Detection            │
│     来源识别                        │
│  Voice Memo / URL / File / Text    │
└─────────────────┬───────────────────┘
                  │
                  ▼
┌─────────────────────────────────────┐
│     2. Save Source Metadata        │
│     保存源信息                      │
│  → memory/voice-memos/sources/     │
└─────────────────┬───────────────────┘
                  │
                  ▼
┌─────────────────────────────────────┐
│     3. Transcription               │
│     转录提取                        │
│  Priority: Apple > Text > summarize│
│           > Whisper-local > API    │
└─────────────────┬───────────────────┘
                  │
                  ▼
┌─────────────────────────────────────┐
│     4. Save Raw Transcript         │
│     保存原始转录                    │
│  → memory/voice-memos/transcripts/ │
└─────────────────┬───────────────────┘
                  │
                  ▼
┌─────────────────────────────────────┐
│     5. LLM Deep Processing         │
│     LLM深度整理                     │
│  • Read USER.md & MEMORY.md        │
│  • Clean up spoken language        │
│  • Extract key points & insights   │
│  • Identify TODOs & connections    │
└─────────────────┬───────────────────┘
                  │
                  ▼
┌─────────────────────────────────────┐
│     6. Save Processed Result       │
│     保存处理结果                    │
│  → memory/voice-memos/processed/   │
└─────────────────┬───────────────────┘
                  │
          ┌───────┴───────┐
          ▼               ▼
┌─────────────────┐ ┌─────────────────┐
│ 7a. Apple Notes │ │ 7b. Reminders  │
│ Structured note │ │ Create TODOs   │
│ with #hashtags  │ │ 创建提醒       │
└────────┬────────┘ └────────┬───────┘
         │                   │
         └─────────┬─────────┘
                   ▼
┌─────────────────────────────────────┐
│     8. Update Index                │
│     更新索引                        │
│  → memory/voice-memos/INDEX.md     │
└─────────────────────────────────────┘

memory/voice-memos/           # All data, searchable via memory_search
├── INDEX.md                  # Processing records index / 处理记录索引
├── sources/                  # Original file metadata / 原始文件元数据
│   └── YYYY-MM-DD_xxx.json
├── transcripts/              # Raw transcripts / 原始转录文本
│   └── YYYY-MM-DD_source_title.md
├── processed/                # LLM processed content / LLM处理后内容
│   └── YYYY-MM-DD_source_title.md
└── synced/                   # Sync records / 同步记录
    └── YYYY-MM-DD_source_title.json

🎙️ [Auto-generated Title / 智能生成的标题]

📅 Date | ⏱️ Duration | 👤 Source
🏷️ #tag1 #tag2 #tag3

━━━━━━━━━━━━━━━━━━━━━━

📌 Summary / 核心摘要
[One paragraph summarizing the content]

🎯 Key Points / 关键要点
• Point 1
• Point 2
• Point 3

💡 Deep Analysis & Reflection (For User) / 深度分析与反思
[Personalized analysis connecting to user's:
 - Current research directions (from MEMORY.md)
 - Active projects and interests (from USER.md)
 - Decision-making style and preferences
 - Critical counter-arguments and blind spots]

📋 Action Items / 行动建议
☐ Research: [specific to user's academic work]
☐ Business: [relevant to startup/investment focus]
☐ Content: [ideas for courses/articles]

🔗 Related Connections / 相关联系
• Connection to [project/memory]
• Recommended reading/research

💬 Notable Quotes / 金句摘录
• "Quote 1"
• "Quote 2"

━━━━━━━━━━━━━━━━━━━━━━

📝 Original Transcript (Cleaned) / 原始转录（已整理）
[Full transcript text, cleaned up from spoken language / 完整转录，已整理口语表达]

QTA File
├── ftyp (file type marker: "qt  ")
├── wide (extended marker)
├── mdat (audio data, typically 90%+ of file size)
└── moov (metadata container)
    ├── mvhd (movie header)
    └── trak (one or more tracks)
        ├── tkhd (track header)
        ├── mdia (media data)
        └── meta (metadata - TRANSCRIPTION HERE!)
            ├── hdlr (handler: "mdta")
            ├── keys (key list: "com.apple.VoiceMemos.tsrp")
            └── ilst (data list)
                └── data (JSON transcription payload)

{
  "locale": {"identifier": "zh-Hans_GB", "current": 1},
  "attributedString": {
    "runs": ["字",0,"符",1,"转",2,"录",3,...],
    "attributeTable": [
      {"timeRange": [0.0, 0.5]},
      {"timeRange": [0.5, 0.8]},
      ...
    ]
  }
}

# Extract plain text
python3 scripts/extract-apple-transcript.py recording.qta

# Extract with metadata (JSON output)
python3 scripts/extract-apple-transcript.py recording.qta --json

# Extract with timestamps
python3 scripts/extract-apple-transcript.py recording.qta --json --with-timestamps

sources:
  voice_memos:
    enabled: true
    path: "~/Library/Group Containers/group.com.apple.VoiceMemos.shared/Recordings/"
  icloud:
    enabled: true
    paths:
      - "~/Library/Mobile Documents/com~apple~CloudDocs/Recordings"
      - "~/Library/Mobile Documents/com~apple~CloudDocs/Meeting Recordings"
    watch_patterns: ["*.m4a", "*.mp3", "*.mp4", "*.wav", "*.mov"]

transcription:
  # Priority order / 优先级顺序
  priority: ["apple", "text", "summarize", "whisper-local"]
  whisper_model: "small"  # tiny/small/medium/large
  language: "auto"        # auto/zh/en/ja/ko/...

notes:
  folder: "Voice Memos"   # Apple Notes folder name
  include_quotes: true
  include_original: true

reminders:
  enabled: true
  list: "Reminders"
  auto_create: true

YouTube URL      → summarize extract
Bilibili URL     → yt-dlp download + whisper
.qta/.m4a        → Apple transcript extraction
Other audio/video → whisper transcription
.txt/.md file    → direct read
.doc/.docx       → textutil convert

# Record to memory/voice-memos/sources/
echo '{"input":"...", "type":"...", "date":"YYYY-MM-DD"}' > sources/xxx.json

# Save to memory/voice-memos/transcripts/YYYY-MM-DD_source_title.md
# Include: source info + full raw transcript

Read USER.md and MEMORY.md, combining user context.

**MODE SELECTION (Auto-detect or Manual Override) / 模式选择:**

┌─────────────────────────────────────────────────────────────────┐
│  Mode A: Solo Memo (Default) / 短语音                           │
│  Trigger: < 5 min, single speaker, casual                       │
│  Output: Clean text + Key points + TODOs + Connections          │
└─────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────┐
│  Mode B: Deep Meeting / 深度会议                                │
│  Trigger: 15-60 min, multi-speaker with labels                  │
│  Output:                                                        │
│    1. Executive Summary (1 paragraph)                           │
│    2. Chronological Detail by time blocks                       │
│    3. Debate Flow (who said what, conflicts)                    │
│    4. Decision Matrix (Issue → Decision → Rationale)            │
│    5. Action Items with owners                                  │
│    6. Vital Quotes (preserve Voice)                             │
└─────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────┐
│  Mode C: Lecture / Talk / 讲座模式 (NEW)                        │
│  Trigger: Single speaker, 30min-3hr, structured presentation    │
│  Output:                                                        │
│    1. Executive Summary (1 paragraph)                           │
│    2. **Argument Structure (论点层级)**:                        │
│       - Core Thesis (核心论点)                                  │
│       - Supporting Arguments (分论点 1, 2, 3...)                │
│       - Key Evidence/Examples for each argument                 │
│       - Counter-arguments addressed (if any)                    │
│    3. Key Definitions (关键定义/概念)                           │
│    4. Notable Quotes (金句, with timestamps if available)       │
│    5. Connections to User's Work (个人关联)                     │
│    6. Questions Raised / Gaps (讲座未解决的问题)                │
└─────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────┐
│  Mode D: Lecture + Q&A / 讲座+问答 (NEW)                        │
│  Trigger: First part monologue, second part Q&A                 │
│  Output:                                                        │
│    **Part I: Lecture Section** (use Mode C structure)           │
│    **Part II: Q&A Section**                                     │
│       - Group questions by theme/topic (not chronological)      │
│       - Format: Q1 → A1 (summary), Q2 → A2...                   │
│       - Highlight: Best Questions, Surprising Answers           │
└─────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────┐
│  Mode E: Long-form No-Speaker-Label / 超长无标注会议 (NEW)      │
│  Trigger: > 90 min, NO speaker diarization (text is a blob)     │
│  Strategy:                                                      │
│    1. **Chunking**: Split into ~30min segments for processing   │
│    2. **Topic Detection**: Identify topic shift points          │
│       (Don't force time blocks; use semantic breaks)            │
│    3. **Abandon Attribution**: Don't guess who said what        │
│  Output:                                                        │
│    1. Executive Summary                                         │
│    2. **Topic Blocks** (not time blocks):                       │
│       - Topic 1: [Summary] + [Key points] + [Quotes]            │
│       - Topic 2: ...                                            │
│    3. Unresolved Issues / Open Questions                        │
│    4. Action Items (may lack owners)                            │
│    5. Full Cleaned Transcript (appended or linked)              │
└─────────────────────────────────────────────────────────────────┘

**TWO-PASS PROCESSING for Long Content (> 60 min):**
- Pass 1 (Quick Scan): Identify structure type, speaker presence, topic shifts
- Pass 2 (Deep Process): Apply appropriate mode to each segment

**OUTPUT DENSITY LEVELS (User can request):**
- Level 1: Executive Only (1 page, for busy stakeholders)
- Level 2: Structured Summary (5-10 pages, default)
- Level 3: Full Annotated Transcript (everything, with margin notes)

# Save to memory/voice-memos/processed/YYYY-MM-DD_source_title.md

# 1. Convert Markdown to HTML using pandoc (REQUIRED)
pandoc /path/to/processed.md -f markdown -t html -o /tmp/note-content.html

# 2. Create note with HTML content via AppleScript
osascript <<'EOF'
set htmlContent to do shell script "cat /tmp/note-content.html"
set noteTitle to "🎙️ Note Title"

tell application "Notes"
    set folderName to "Voice Memos"
    set targetFolder to missing value
    
    repeat with f in folders
        if name of f is folderName then
            set targetFolder to f
            exit repeat
        end if
    end repeat
    
    if targetFolder is missing value then
        make new folder with properties {name:folderName}
        delay 1
        set targetFolder to folder folderName
    end if
    
    tell targetFolder
        make new note with properties {name:noteTitle, body:htmlContent}
    end tell
end tell
EOF