Skill: terellison/imessage-signal-analyzer (version 1.0.0)

--- name: imessage-signal-analyzer description: Analyze iMessage (macOS) and Signal conversation history to reveal relationship dynamics — message volume, initiation patterns, silence gaps, tone samples, and recent exchanges. Use when asked to analyze messages, read message history, check conversation patterns, or evaluate a relationship based on text history. Works on macOS (iMessage + Signal), Linux/Windows (Signal only). --- # iMessage & Signal Analyzer Analyze iMessage (macOS) and Signal conversations to produce relationship reports. ## Prerequisites ### 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` ## Usage ### iMessage Analysis ```bash python3 skills/message-analyzer/scripts/analyze.py imessage <phone_or_handle> ``` **Examples:** ```bash 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): ```bash signal-cli export --output ~/signal_export.json ``` Then analyze: ```bash python3 skills/message-analyzer/scripts/analyze.py signal ~/signal_export.json <phone_or_name> ``` **Examples:** ```bash python3 skills/message-analyzer/scripts/analyze.py signal ~/signal_export.json "+15551234567" python3 skills/message-analyzer/scripts/analyze.py signal ~/signal_export.json "+15559876543" ``` ## Finding a Contact's Number ### iMessage If you have a name but not a number: ```bash 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. ## Key Data Caveats ### 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.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)