Analyseur de Chats WhatsApp

VérifiéSûr

Analyse les fichiers d'exportation de chat WhatsApp (.txt) pour les convertir en données JSON structurées, en extrayant les participants, les horodatages, les messages, les indicateurs de médias et les messages système. Prend en charge plusieurs formats de date (US, UE, ISO) et les cas particuliers comme les messages multilignes et les problèmes d'encodage. Utile lorsque vous devez analyser ou transformer des conversations WhatsApp de manière programmatique.

Spar Skills Guide Bot
Data & IAIntermédiaire
8002/06/2026
Claude Code
#whatsapp#chat-parser#data-extraction#text-processing

Recommandé pour

Notre avis

Ce skill parse les exports de chats WhatsApp (.txt) pour en extraire des données structurées en JSON : participants, horodatages, messages.

Points forts

  • Gère plusieurs formats de date (EU, US, ISO)
  • Détecte les messages média et système
  • Supporte les messages multi-lignes
  • Fournit des métadonnées comme le nombre de messages et la plage de dates

Limites

  • Dépend de l'encodage UTF-8 (avec fallback latin-1)
  • Ne gère pas les pièces jointes ou médias réels
  • Peut échouer sur des formats non standard ou des variantes régionales
Quand l'utiliser

Pour analyser des historiques de discussion WhatsApp et les convertir en données exploitables pour du traitement ou de l'analyse.

Quand l'éviter

Si vous avez besoin de traiter des fichiers binaires ou des messages chiffrés de bout en bout, ou si le format d'export n'est pas textuel.

Analyse de sécurité

Sûr
Score qualité92/100

The skill only reads a specified file and parses its text content, with no destructive operations, network access, or execution of external commands. All code is standard Python, and there is no risk of exfiltration or system compromise.

Aucun point d'attention détecté

Exemples

Parse a WhatsApp chat file
Parse the WhatsApp chat in examples/sample-whatsapp-chat.txt
Extract participants and messages
Extract all participants and messages from this WhatsApp export file: /data/chat.txt. Return JSON.
Count media messages
Parse this WhatsApp chat and tell me how many media messages were sent, and by whom.

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

| Region | Date Format | Example | |--------|-------------|---------| | US | MM/DD/YY | [01/28/26, 9:15:32 AM] | | EU/UK | DD/MM/YYYY | [28/01/2026, 09:15:32] | | ISO | YYYY-MM-DD | [2026-01-28, 09:15:32] |

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

| Scenario | How to Handle | |----------|---------------| | System messages | Sender = "System" or skip | | Empty lines | Skip or treat as message separator | | Emoji-only messages | Parse normally, emoji preserved | | Very long messages | No truncation, preserve full content | | Non-UTF8 encoding | Try latin-1 fallback |

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
Skills similaires