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wechat-chat-analyzer

Parse WeChat SQLite chat logs and generate relationship statistics and reports.

54|18|Updated Mar 15, 2026
Search Tags:#python#visualization#wechat#data-export#chat-analysis#sentiment-tracking#relationship-insights
One-click install
npx skills add https://github.com/UniUni2000/wechat-chat-analyzer --skill wechat-chat-analyzer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: wechat-chat-analyzer
Source: https://github.com/UniUni2000/wechat-chat-analyzer/tree/main/wechat-chat-analyzer
Command: npx skills add https://github.com/UniUni2000/wechat-chat-analyzer --skill wechat-chat-analyzer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pycryptodome, matplotlib, wordcloud, jieba, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

WeChat's encrypted local databases are difficult to explore manually, so deriving intimacy insights, emotional trends, and behavior patterns from one-on-one chats is time-consuming and error-prone. This skill automates the decryption, data loading, and conversation parsing so the agent can deliver a data-backed report without the user touching SQL or plotting code.

Core Features & Use Cases

  • Automated decryption and retrieval: vendor helpers pull AES keys from a logged-in Windows WeChat or accept pre-decrypted macOS data so the pipeline can immediately load contacts and messages.
  • Deep quantitative analysis: the stats engine computes initiative rates, reply speed tiers, monthly and hourly distributions, high-frequency words, and milestone moments for the named friend.
  • Visual exports and summaries: generate heatmaps, reply pie charts, word clouds, TXT/CSV/HTML exports, and a JSON report that the LLM reads to craft an insightful relationship narrative.

Quick Start

Ask the skill to analyze my chat with 张三 by saying 分析我和张三的微信聊天记录 so it runs the automated Python analyzer immediately.

Frequently Asked Questions about wechat-chat-analyzer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze WeChat chat history to extract conversation metrics and emotional trends?▼

To analyze WeChat chat history, you run a local Python tooling script that parses SQLite message tables, computes initiative and reply metrics, and generates visualizations. This automated process extracts emotional trends and relational habits without manual SQL or plotting code.

How do I decrypt and read WeChat local SQLite databases on Windows?▼

You decrypt WeChat local SQLite databases on Windows by using vendor helpers that pull AES keys from a logged-in client. This unlocks the encrypted chat data, allowing the pipeline to immediately load contacts and parse one-on-one messages for analysis.

Can I generate word clouds and visual charts from exported WeChat messages?▼

Yes, you can generate visual charts from exported WeChat messages using the built-in Python visualization engine. It creates heatmaps, reply pie charts, and word clouds from high-frequency words to visualize monthly distributions and milestone moments.

Does this WeChat data analysis tool work on macOS without manual database decryption?▼

Yes, the WeChat data analysis tool works on macOS by accepting pre-decrypted data directly. This bypasses the need for manual database decryption, allowing the pipeline to load contacts and run the relationship analyzer script immediately on macOS systems.

What is the best way to export WeChat chat logs to CSV and HTML reports?▼

The best way to export WeChat chat logs to CSV and HTML is by running the automated Python analyzer. It computes conversation statistics and saves TXT, CSV, and HTML exports along with a JSON report summarizing relational habits and behavior patterns.

Are there limitations when extracting intimacy insights from WeChat one-to-one chats?▼

A key limitation when extracting intimacy insights is that the tool requires local execution on Windows or macOS to access the decrypted chat database. All data processing and report generation happen locally without transmitting data externally.

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