Whatsapp Parser

Directory: launch-agent-skills/skills/whatsapp-parser/skill.md

name: WhatsApp Chat Parser description: Parse WhatsApp chat exports into structured JSON data with participants, timestamps, and messages triggers: - whatsapp - chat export - parse chat - message history - chat file

WhatsApp Chat Parser

Purpose

Parse WhatsApp chat export files (.txt) and extract structured data including:

• Participant list
• Message count and date range
• Individual messages with timestamps and senders
• Media placeholders and system messages

WhatsApp Export Format

WhatsApp exports follow this pattern (may vary by locale):

[DD/MM/YYYY, HH:MM:SS] Sender Name: Message content

Format Variations

Special Message Types

• Media: <Media omitted> or <image omitted>, <video omitted>
• System: Messages without sender (e.g., "Alice added Bob to the group")
• Multi-line: Continuation lines without timestamp prefix
• Links: URLs embedded in message text
• Replies: May include quoted text with formatting

Instructions

Step 1: Read the Chat File

with open(chat_file_path, 'r', encoding='utf-8') as f:
    content = f.read()

Step 2: Parse Messages Using Regex

import re
from datetime import datetime

# Pattern for WhatsApp message format (handles multiple date formats)
patterns = [
    # EU format: [DD/MM/YYYY, HH:MM:SS]
    r'\[(\d{2}/\d{2}/\d{4}),\s*(\d{2}:\d{2}:\d{2})\]\s*([^:]+):\s*(.*)',
    # US format: [MM/DD/YY, H:MM:SS AM/PM]
    r'\[(\d{2}/\d{2}/\d{2}),\s*(\d{1,2}:\d{2}:\d{2}\s*[AP]M)\]\s*([^:]+):\s*(.*)',
]

messages = []
current_message = None

for line in content.split('\n'):
    matched = False
    for pattern in patterns:
        match = re.match(pattern, line)
        if match:
            if current_message:
                messages.append(current_message)
            
            date_str, time_str, sender, text = match.groups()
            current_message = {
                'date': date_str,
                'time': time_str,
                'sender': sender.strip(),
                'content': text.strip(),
                'is_media': '<media omitted>' in text.lower() or '<omitted>' in text.lower()
            }
            matched = True
            break
    
    # Handle multi-line messages
    if not matched and current_message and line.strip():
        current_message['content'] += '\n' + line

if current_message:
    messages.append(current_message)

Step 3: Extract Metadata

participants = list(set(msg['sender'] for msg in messages))
date_range = {
    'start': messages[0]['date'] if messages else None,
    'end': messages[-1]['date'] if messages else None
}
message_count = len(messages)
media_count = sum(1 for msg in messages if msg['is_media'])

Step 4: Return Structured Output

{
  "metadata": {
    "participants": ["Alice Developer", "Bob Engineer"],
    "message_count": 42,
    "media_count": 3,
    "date_range": {
      "start": "28/01/2026",
      "end": "28/01/2026"
    }
  },
  "messages": [
    {
      "timestamp": "28/01/2026 09:15:32",
      "sender": "Alice Developer",
      "content": "Hey Bob, did you push the code?",
      "is_media": false
    }
  ]
}

Edge Cases

Example Usage in Claude Code

User: "Parse the WhatsApp chat in examples/sample-whatsapp-chat.txt"

Claude should:

1. Read the file using the path provided
2. Apply the parsing logic above
3. Return structured JSON with metadata and messages
4. Highlight interesting stats (most active participant, busiest hour, etc.)

Related Skills

• whatsapp-summarizer - Generate summaries from parsed chats
• whatsapp-action-extractor - Find TODOs and action items in messages