A sophisticated analysis tool that transforms unstructured WeChat Official Account articles into structured insights, including timelines, key figures, and core facts.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install wechat-article-analyzer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install wechat-article-analyzer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is WeChat Article Deep Analyzer?
The WeChat Article Deep Analyzer is a specialized tool within the Openclaw Skills library designed to solve the challenge of extracting meaningful data from WeChat Official Account (MP) links. It leverages automated scraping techniques combined with structured NLP analysis to break down complex long-form content into digestible reports.
By processing any valid mp.weixin.qq.com link, the skill identifies the fundamental pillars of the narrative: who was involved, what happened and when, and what the strategic significance of the piece is. This makes it an essential asset for researchers, analysts, and developers building information-gathering pipelines.
WeChat Article Deep Analyzer Use Cases
- Market Research: Quickly parse competitor announcements or industry deep-dives posted on WeChat Official Accounts.
- Content Curation: Generate structured summaries and timelines for news aggregation or internal knowledge bases.
- Entity Tracking: Automatically identify and log mentions of specific companies, stakeholders, or organizations across multiple articles.
- Automated Reporting: Convert viral WeChat content into professional Markdown reports or JSON data for downstream CRM or ERP integration.
How WeChat Article Deep Analyzer Works
- Triggering: The process begins when the user provides a WeChat article link or specific keywords that activate the Openclaw Skills logic.
- Content Retrieval: The system uses a Python-based request engine with specialized User-Agent headers to fetch the raw article content.
- Structured Parsing: Using regular expressions and NLP patterns, it extracts the article title, body, and metadata.
- Dimensional Analysis: The skill analyzes the text across five dimensions: Timelines, Stakeholders, Facts, Themes, and Quotes.
- Format Generation: Finally, it compiles the findings into the requested output format, such as Markdown, JSON, or an OpenCLI adapter.
WeChat Article Deep Analyzer Setup
To get started with this skill from the Openclaw Skills collection, ensure you have Python 3.7+ installed, then set up the required dependencies:
# Install required dependencies
pip install requests pyyaml
# Basic usage to analyze an article
python3 scripts/analyze_wechat.py <WeChat_URL>
# Generate a structured Markdown report
python3 scripts/analyze_wechat.py <WeChat_URL> --format markdown --output report.md
WeChat Article Deep Analyzer Data Schema & Taxonomy
The skill organizes extracted information into a structured taxonomy to ensure consistency across different articles:
| Attribute | Description |
|---|---|
| Timeline | Chronological sequence of key events and milestones mentioned in the text. |
| Stakeholders | A curated list of individuals, companies, and organizations identified as primary actors. |
| Facts | Hard data points, including financial figures, percentages, dates, and technical identifiers. |
| Themes | High-level subject analysis focusing on core arguments and strategic intent. |
| Quotes | Direct citations from the article that carry significant weight or authority. |
WeChat Article Deep Analyzer Advanced Features
- Multi-Format Export: Support for human-readable Markdown, developer-friendly JSON, and OpenCLI YAML adapters.
- OpenCLI Integration: Seamlessly register analyzed data as an adapter for use in wider CLI-based automation workflows.
- Customizable UA Strings: Uses Mac Chrome User-Agents to ensure reliable article fetching and bypass basic bot detection.
- Verbose Mode: Detailed logging for developers to debug the extraction and NLP analysis lifecycle.
SKILL.md
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