wechat-article-search-api-skill
WeChat Article Search API
📖 Introduction
This skill provides users with automated WeChat article extraction through the BrowserAct WeChat Article Search API template. It allows for the direct extraction of full-content, structured WeChat articles based on keyword searches. Simply provide search keywords and optional date filters, and you can obtain comprehensive article data including the full body text.
✨ Features
- No hallucinations, ensuring stable and precise data extraction: Pre-configured workflows avoid AI-generated hallucinations.
- No CAPTCHA issues: No need to handle reCAPTCHA or other verification challenges.
- No IP restrictions or geo-blocking: No need to handle regional IP limitations.
- Faster execution: Task execution is faster compared to pure AI-driven browser automation solutions.
- Extremely high cost-effectiveness: Significantly reduces data acquisition costs compared to AI solutions that consume a large number of tokens.
🔑 API Key Guidance Flow
Before running, check the BROWSERACT_API_KEY environment variable. If not set, do not take other actions; request and wait for the user to provide it.
The Agent must inform the user:
"Since you have not configured the BrowserAct API Key, please go to the BrowserAct Console to get your Key."
🛠️ Input Parameters
When invoking the script, the Agent should flexibly configure the following parameters based on user needs:
-
keywords (Search Keywords)
- Type:
string - Description: Search keywords used to find WeChat articles. Can be an industry term, topic, or specific phrase.
- Example:
openclaw,AI agent,browser automation
- Type:
-
Date_limit (Extraction Limit)
- Type:
number - Description: Maximum number of articles to extract. For the first run, a smaller default value is recommended.
- Default Value:
10 - Suggestions: Use
5to10for quick testing, larger numbers for batch research.
- Type:
-
publication_date (Publication Date Filter)
- Type:
string - Description: Filter articles by their publication date.
- Example:
3月11日,March 10,2026-03-11
- Type:
🚀 Invocation Method
The Agent should execute the following independent script to achieve "one command, direct results":
# Example invocation
python -u ./scripts/wechat_article_search_api.py "keywords" limit "publication_date"
⏳ Run Status Monitoring
Because this task involves automated browser operations, it may take a long time (several minutes). While running, the script will continuously output timestamped status logs (e.g., [14:30:05] Task Status: running).
Agent Instructions:
- Keep monitoring the terminal output while waiting for the script to return results.
- As long as the terminal continues to output new status logs, it means the task is running normally; do not misjudge it as deadlocked or unresponsive.
- Only consider triggering the retry mechanism if the status remains unchanged for a long time, or the script stops outputting without returning results.
📊 Output Data Explanation
Upon successful execution, the script will parse and print the results directly from the API response. The results include:
url_link: Original article URLpublication_date: Article publication dateauthor: Article author or publishing account nameimage_url: Main image URL or article cover image URLbody_content: Full body content of the articletitle: Full article title
⚠️ Error Handling & Retry
During script execution, if an error occurs (such as network fluctuation or task failure), the Agent should follow this logic:
-
Check the output content:
- If the output contains
"Invalid authorization", it means the API Key is invalid or expired. In this case, do not retry; guide the user to re-check and provide the correct API Key. - If the output does not contain
"Invalid authorization"but the task fails (e.g., output starts withError:or returns an empty result), the Agent should automatically try to execute the script one more time.
- If the output contains
-
Retry limit:
- Automatic retry is limited to once. If the second attempt still fails, stop retrying and report the specific error message to the user.
🌟 Typical Use Cases
- Content Monitoring: Track mentions of specific brands or topics across WeChat articles.
- Media Research: Analyze full text of articles published by top WeChat accounts.
- Trend Tracking: Collect articles about rising industry trends (e.g., AI agents) for comprehensive reading.
- Knowledge Base Building: Extract deep-dive articles into an internal repository.
- Competitor Analysis: Review full-length posts released by competitor accounts.
More from browser-act/skills
browser-act
Browser automation CLI (browser-act) for AI agents. MUST trigger when: (1) user mentions 'browser-act' in any form, or user needs to: (2) open/visit/browse/check a URL or webpage, (3) scrape/extract/crawl/monitor web content, (4) fill forms, click buttons, type text, scroll, or interact with page elements, (5) take a screenshot of a webpage, (6) handle or solve a captcha, (7) use a stealth/anti-detection browser or proxy, (8) connect to or control Chrome, (9) inspect network requests or record HAR, (10) automate any browser or web interaction task. Covers: navigation, page state inspection, element interaction, data extraction, JavaScript evaluation, tab management, network inspection, dialog handling, captcha solving, parallel browser sessions, stealth browsing, and any browser automation tasks.
887amazon-competitor-analyzer
Scrapes Amazon product data from ASINs using browseract.com automation API and performs surgical competitive analysis. Compares specifications, pricing, review quality, and visual strategies to identify competitor moats and vulnerabilities.
113amazon-reviews-api-skill
This skill helps users automatically extract Amazon product reviews via the Amazon Reviews API. Agent should proactively apply this skill when users express needs like getting reviews for Amazon product with ASIN B07TS6R1SF, analyzing customer feedback for a specific Amazon item, getting ratings and comments for a competitive product, tracking sentiment of recent Amazon reviews, extracting verified purchase reviews for quality assessment, summarizing user experiences from Amazon product pages, monitoring product performance through customer reviews, collecting reviewer profiles and links for market research, gathering review titles and descriptions for content analysis, scraping Amazon reviews without requiring a login.
73amazon-product-api-skill
This skill helps users extract structured product listings from Amazon, including titles, ASINs, prices, ratings, and specifications. Use this skill when users want to search for products on Amazon, find the best selling brand products, track price changes for items, get a list of categories with high ratings, compare different brand products on Amazon, extract Amazon product data for market research, look for products in a specific language or marketplace, analyze competitor pricing for keywords, find featured products for search terms, get technical specifications like material or color for product lists.
70web-research-assistant
AI-powered web research assistant that leverages BrowserAct API to supplement restricted web access by searching the internet for additional information. Designed for OpenClaw and Claude Code.
59amazon-product-search-api-skill
This skill is designed to help users automatically extract product data from Amazon search results. The Agent should proactively apply this skill when users request searching for products related to keywords, finding best-selling items from specific brands, monitoring product prices and availability on Amazon, extracting product listings for market research, collecting product ratings and review counts for competitive analysis, finding specific products with a maximum count, searching Amazon in different languages for localized results, tracking monthly sales estimates for brand products, gathering product URLs and titles for a product catalog, scanning Amazon for Best Seller tags in a specific category, monitoring shipping and delivery information for brand items, building a structured dataset of Amazon search results.
48