Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Communication & Collaboration
iMessage & Signal Analyzer
Analyze iMessage (macOS) and Signal conversation history to reveal relationship dynamics — message volume, initiation patterns, silence gaps, tone samples, a...
skill openclawclawhub Free
Analyze iMessage (macOS) and Signal conversation history to reveal relationship dynamics — message volume, initiation patterns, silence gaps, tone samples, a...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/analyze.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
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New install
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- terellison
- Source page
- View original listing
- Canonical URL
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Documentation
iMessage & Signal Analyzer
Analyze iMessage (macOS) and Signal conversations to produce relationship reports.
macOS (iMessage)
iMessage data is stored locally on macOS. Depending on your security settings, you may need to grant Full Disk Access: Option 1: Run the script directly with Python (no special permissions needed if you have read access to ~/Library/Messages/chat.db) Option 2: If you get a permission error, grant Full Disk Access: Open System Settings → Privacy & Security → Full Disk Access Click + and add Python or your terminal app
Linux / Windows (Signal only)
iMessage is not available on Linux/Windows Signal analysis works via exported JSON
Signal (All Platforms)
Install signal-cli: brew install signal-cli (macOS) or see https://github.com/AsamK/signal-cli Link your device: signal-cli link and scan QR code Export messages: signal-cli export --output ~/signal_export.json
iMessage Analysis
python3 skills/message-analyzer/scripts/analyze.py imessage <phone_or_handle> Examples: python3 skills/message-analyzer/scripts/analyze.py imessage "+15551234567" python3 skills/message-analyzer/scripts/analyze.py imessage "+15551234567" --limit 500
Signal Analysis
First, export your Signal data (one-time): signal-cli export --output ~/signal_export.json Then analyze: python3 skills/message-analyzer/scripts/analyze.py signal ~/signal_export.json <phone_or_name> Examples: python3 skills/message-analyzer/scripts/analyze.py signal ~/signal_export.json "+15551234567" python3 skills/message-analyzer/scripts/analyze.py signal ~/signal_export.json "+15559876543"
iMessage
If you have a name but not a number: DB=$(ls ~/Library/Application\ Support/AddressBook/Sources/*/AddressBook-v22.abcddb 2>/dev/null | head -1) sqlite3 "$DB" "SELECT ZFIRSTNAME, ZLASTNAME FROM ZABCDRECORD WHERE ZFIRSTNAME LIKE '%Name%';" If AddressBook returns no results, ask the user for the number.
Signal
Signal exports include phone numbers in the JSON. Search by name or number.
iMessage
Your sent messages may only exist from the current device's setup date — older sent messages are lost when switching devices. This skews initiation stats. Binary messages (attributedBody) are partially decoded — some formatting artifacts like +@ prefixes may appear in samples; these are normal. Multiple handles: One contact may have 2–3 duplicate handles (iMessage + SMS + RCS). The script aggregates them automatically.
Signal
Export required: You must export Signal data first using signal-cli export Media: Exported JSON contains message text; media (images, files) is not included Reactions: Emoji reactions are included as separate message entries
Analysis Output
The script produces: Total message count (you vs. them) Date range Messages per year with volume bar Conversation initiation breakdown (new convo = gap > 4 hours) Notable silences (>30 days) Sample messages by year Most recent 10 messages
Interpreting Results
After running the script, synthesize findings conversationally: Volume patterns: When was the friendship most active? Any notable surges or drops? Initiation skew: Who reaches out first? (Note: your sent messages may be missing from old periods) Gaps: Were long silences mutual drift or explainable (device switch, platform change, life event)? Tone/content: What do the sample messages reveal about the relationship's energy? Context from user: Always ask the user to fill in context gaps Present the analysis conversationally, not just as raw numbers. Offer a genuine take on the relationship dynamic.
Category context
Messaging, meetings, inboxes, CRM, and teammate communication surfaces.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs1 Scripts
- SKILL.md
- scripts/analyze.py