Query the macOS iMessage SQLite database (~/Library/Messages/chat.db) to retrieve conversation history, decode messages stored in binary format, and build sourced timelines with precise timestamps.
Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.
When to Use
Retrieving iMessage conversation history for a specific contact
Building sourced timelines with timestamps from text messages
Searching for keywords across all conversations
Debugging messages that appear empty but contain recoverable text
Extracting message content that iOS stored in binary attributedBody format
Prerequisites
macOS only — chat.db is a macOS-specific database
Full Disk Access — The terminal running Claude Code must have FDA granted in System Settings > Privacy & Security > Full Disk Access
Read-only — Never write to chat.db. Always use read-only SQLite access.
Optional: pip install pytypedstream — Enables tier 1 decoder (proper typedstream deserialization). Script works without it (falls through to pure-binary tiers 2/3).
Critical Knowledge - The text vs attributedBody Problem
IMPORTANT: Many iMessage messages have a NULL or empty text column but contain valid, recoverable text in the attributedBody column. This is NOT because they are voice messages — iOS stores dictated messages, messages with rich formatting, and some regular messages in attributedBody as an NSAttributedString binary blob.
How to detect
-- Messages with attributedBody but no text (these are NOT necessarily voice messages)SELECTCOUNT(*) as hidden_messages
FROM message m
JOIN chat_message_join cmj ON m.ROWID = cmj.message_id
JOIN chat c ON cmj.chat_id = c.ROWID
WHERE c.chat_identifier ='<CHAT_IDENTIFIER>'AND (m.text ISNULLOR length(m.text) =0)
AND m.attributedBody ISNOT NULLAND length(m.attributedBody) >100AND m.associated_message_type =0AND m.cache_has_attachments =0;
How to distinguish message types when text is NULL
cache_has_attachments
attributedBody length
Likely type
0
> 100 bytes
Dictated/rich text — recoverable via decode script
1
any
Attachment (image, file, voice memo) — text may be in attributedBody too
0
< 50 bytes
Tapback reaction or system message — usually noise
How to decode
Use the bundled decode script for reliable extraction (v4 — 3-tier decoder + native pitfall protections):
m.date / 1000000000 — Convert nanoseconds to seconds
+ 978307200 — Add offset from Unix epoch (1970) to Apple epoch (2001)
'unixepoch' — Tell SQLite this is a Unix timestamp
'localtime' — Convert to local timezone (CRITICAL — omitting this gives UTC)
Quick Start Queries
1. List all conversations
sqlite3 ~/Library/Messages/chat.db \
"SELECT c.chat_identifier, c.display_name, COUNT(cmj.message_id) as msg_count
FROM chat c
JOIN chat_message_join cmj ON c.ROWID = cmj.chat_id
GROUP BY c.ROWID
ORDER BY msg_count DESC
LIMIT 20"
2. Get conversation thread (text column only)
sqlite3 ~/Library/Messages/chat.db \
"SELECT datetime(m.date/1000000000 + 978307200, 'unixepoch', 'localtime') as ts,
CASE WHEN m.is_from_me = 1 THEN 'Me' ELSE 'Them' END as sender,
m.text
FROM message m
JOIN chat_message_join cmj ON m.ROWID = cmj.message_id
JOIN chat c ON cmj.chat_id = c.ROWID
WHERE c.chat_identifier = '<CHAT_IDENTIFIER>'
AND length(m.text) > 0
AND m.associated_message_type = 0
ORDER BY m.date DESC
LIMIT 50"
3. Get ALL messages including attributedBody (use decode script)
Tapback reactions (likes, loves, emphasis, etc.) are stored as separate message rows with associated_message_type != 0. Always filter:
AND m.associated_message_type =0
Shell escaping in zsh
The != operator can cause issues in zsh. Use positive assertions instead:
-- BAD (breaks in zsh)AND m.text !=''-- GOOD (works everywhere)AND length(m.text) >0
Using the Decode Script
The bundled decode_attributed_body.py handles all edge cases:
# Basic usage - get last 50 messages from a contact
python3 <skill-path>/scripts/decode_attributed_body.py --chat "+1234567890" --limit 50
# Search for keyword
python3 <skill-path>/scripts/decode_attributed_body.py --chat "+1234567890" --search "meeting"# Search with surrounding context (3 messages before and after each match)
python3 <skill-path>/scripts/decode_attributed_body.py --chat "+1234567890" --search "meeting" --context 3
# Date range
python3 <skill-path>/scripts/decode_attributed_body.py --chat "+1234567890" --after "2026-01-01" --before "2026-02-01"# Only messages from the other party
python3 <skill-path>/scripts/decode_attributed_body.py --chat "+1234567890" --sender them
# Only messages from me
python3 <skill-path>/scripts/decode_attributed_body.py --chat "+1234567890" --sender me
# Export conversation to NDJSON for offline analysis
python3 <skill-path>/scripts/decode_attributed_body.py --chat "+1234567890" --after "2026-02-01" --export thread.jsonl
Output format: timestamp|sender|text (pipe-delimited, one message per line)
Context Search (--context N)
When --search is combined with --context N, the script shows N messages before and after each match:
Matches are prefixed with [match]
Non-contiguous context groups are separated by --- context ---
Overlapping context windows are deduplicated
NDJSON Export (--export)
Exports messages to a NDJSON (.jsonl) file for offline analysis:
{"ts":"2026-02-13 18:30:17","sender":"them","is_from_me":false,"text":"Message text here","decoded":true,"type":"text","edited":true,"service":"SMS","effect":"slam","reply_to":{"ts":"2026-02-13 18:00:00","sender":"me","text":"Original message..."}}
Fields edited, service, effect, reply_to are optional — only present when applicable. The type field is always present ("text", "audio", or "attachment").
Retracted messages are NEVER exported — they are deterministically excluded (see Native Protections below).
Export-first workflow (recommended for multi-query analysis):
# Step 1: Export once
python3 <skill-path>/scripts/decode_attributed_body.py --chat "+1234567890" \
--after "2026-02-01" --export thread.jsonl
# Step 2: Analyze many times without re-querying SQLite
grep -i "keyword" thread.jsonl
jq 'select(.text | test("reference"; "i"))' thread.jsonl
jq 'select(.sender == "them")' thread.jsonl
Native Protections (v4)
The decode script natively handles these pitfalls — no manual SQL workarounds needed:
Protection
Column Used
Behavior
Retracted messages (Undo Send)
date_retracted, date_edited
Excluded from output — content wiped by iOS, not admissible
Edited messages
date_edited
Flagged with [edited] / "edited": true
Audio/voice messages
is_audio_message
Identified as [audio message] — not misclassified as empty
Inline quotes (swipe-to-reply)
thread_originator_guid
Resolved to quoted message text via GUID index
Attachments without text
cache_has_attachments, attachment table
Surfaced as [attachment: filename] instead of silently dropped
Message effects
expressive_send_style_id
Decoded to human-readable names (slam, loud, gentle, invisible_ink)
Service type
service
Flagged when SMS instead of iMessage
Tapback reactions
associated_message_type
Filtered (only = 0 included)
Anti-Patterns to Avoid
Searching multiple chat identifiers blindly — Always run --stats first to confirm the right chat identifier has messages in the expected date range
Keyword search without context — Always use --context 5 (or more) with --search to understand conversational meaning around matches
Repeated narrow-window SQLite queries — Export the full date range to NDJSON first, then grep/jq the file for all subsequent analysis
Note: Replace <skill-path> with the actual installed skill path. To find it:
Query Patterns — Reusable SQL templates for common operations
Known Pitfalls — Every gotcha discovered and how to handle it
Cross-Repo Analysis — Comparison of 5 OSS decoder implementations and what we adopted
TodoWrite Task Templates
Template A - Retrieve Conversation Thread
1. Identify chat_identifier for the contact (phone number or email)
2. Run decode script with --chat and appropriate date range
3. Review output for attributedBody-decoded messages (marked with [decoded])
4. If searching for specific topic, add --search flag
5. Format results as needed for the task
Template B - Debug Empty Messages
1. Query messages where text IS NULL but attributedBody IS NOT NULL
2. Check cache_has_attachments to distinguish voice/file from dictated text
3. Run decode script to extract hidden text content
4. Verify decoded content makes sense in conversation context
5. Document any new decode patterns in known-pitfalls.md
Template C - Build Sourced Timeline
1. Identify all relevant chat_identifiers
2. Run decode script for each contact with date range
3. Merge and sort by timestamp
4. Format as sourced quotes with timestamps for documentation
5. Verify no messages were missed (compare total count vs decoded count)
Template D - Export-First Deep Analysis
1. Run --stats to confirm chat_identifier and date range
2. Export full date range to NDJSON: --export thread.jsonl
3. Use grep/jq on the NDJSON file for all keyword searches
4. Use --search with --context 5 for contextual understanding of specific matches
5. All subsequent analysis reads from the NDJSON file (no more SQLite queries)
Post-Change Checklist
After modifying this skill:
YAML frontmatter valid (name, description with triggers)
No private data (phone numbers, names, emails) in any file
All SQL uses parameterized placeholders
Decode script works with python3 (pytypedstream optional, tiers 2/3 are stdlib-only)
Did the command succeed? — If not, fix the instruction or error table that caused the failure.
Did parameters or output change? — If the underlying tool's interface drifted, update Usage examples and Parameters table to match.
Was a workaround needed? — If you had to improvise (different flags, extra steps), update this SKILL.md so the next invocation doesn't need the same workaround.
Only update if the issue is real and reproducible — not speculative.
