Web page content extraction with automatic title, HTML, and metadata retrieval.
Works with
Fetches and parses web pages via the page_reader function, returning structured data including title, HTML content, plain text, and publication time
Supports both CLI usage for quick tasks and SDK integration for programmatic access in backend code only
Includes caching, rate limiting, parallel processing, and error handling patterns for production applications
Handles multiple URLs, content aggregatio
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionweb-readerExecute the skills CLI command in your project's root directory to begin installation:
Fetches web-reader from answerzhao/agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate web-reader. Access via /web-reader in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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This skill guides the implementation of web page reading and content extraction functionality using the z-ai-web-dev-sdk package, enabling applications to fetch and process web page content programmatically.
Skill Location: {project_path}/skills/web-reader
This skill is located at the above path in your project.
Reference Scripts: Example test scripts are available in the {Skill Location}/scripts/ directory for quick testing and reference. See {Skill Location}/scripts/web-reader.ts for a working example.
Web Reader allows you to build applications that can extract content from web pages, retrieve article metadata, and process HTML content. The API automatically handles content extraction, providing clean, structured data from any web URL.
IMPORTANT: z-ai-web-dev-sdk MUST be used in backend code only. Never use it in client-side code.
The z-ai-web-dev-sdk package is already installed. Import it as shown in the examples below.
For simple web page content extraction, you can use the z-ai CLI instead of writing code. This is ideal for quick content scraping, testing URLs, or simple automation tasks.
# Extract content from a web page
z-ai function --name "page_reader" --args '{"url": "https://example.com"}'
# Using short options
z-ai function -n page_reader -a '{"url": "https://www.example.com/article"}'
# Save extracted content to JSON file
z-ai function \
-n page_reader \
-a '{"url": "https://news.example.com/article"}' \
-o page_content.json
# Extract and save blog post
z-ai function \
-n page_reader \
-a '{"url": "https://blog.example.com/post/123"}' \
-o blog_post.json
# Extract news article
z-ai function \
-n page_reader \
-a '{"url": "https://news.site.com/breaking-news"}' \
-o news.json
# Read documentation page
z-ai function \
-n page_reader \
-a '{"url": "https://docs.example.com/getting-started"}' \
-o docs.json
# Scrape blog content
z-ai function \
-n page_reader \
-a '{"url": "https://techblog.com/ai-trends-2024"}' \
-o blog.json
# Extract research article
z-ai function \
-n page_reader \
-a '{"url": "https://research.org/papers/quantum-computing"}' \
-o research.json
--name, -n: Required - Function name (use "page_reader")--args, -a: Required - JSON arguments object with:
url (string, required): The URL of the web page to read--output, -o <path>: Optional - Output file path (JSON format)The CLI returns a JSON object containing:
title: Page titlehtml: Main content HTMLtext: Plain text contentpublish_time: Publication timestamp (if available)url: Original URLmetadata: Additional page metadata{
"title": "Introduction to Machine Learning",
"html": "<article><h1>Introduction to Machine Learning</h1><p>Machine learning is...</p></article>",
"text": "Introduction to Machine Learning\n\nMachine learning is...",
"publish_time": "2024-01-15T10:30:00Z",
"url": "https://example.com/ml-intro",
"metadata": {
"author": "John Doe",
"description": "A comprehensive guide to ML"
}
}
# Create a simple script to process multiple URLs
for url in \
"https://site1.com/article1" \
"https://site2.com/article2" \
"https://site3.com/article3"
do
filename=$(echo $url | md5sum | cut -d' ' -f1)
z-ai function -n page_reader -a "{\"url\": \"$url\"}" -o "${filename}.json"
done
Use CLI for:
Use SDK for:
The Web Reader uses the page_reader function to:
import ZAI from 'z-ai-web-dev-sdk';
async function readWebPage(url) {
try {
const zai = await ZAI.create();
const result = await zai.functions.invoke('page_reader', {
url: url
});
console.log('Title:', result.data.title);
console.log('URL:', result.data.url);
console.log('Published:', result.data.publishedTime);
console.log('HTML Content:', result.data.html);
console.log('Tokens Used:', result.data.usage.tokens);
return result.data;
} catch (error) {
console.error('Page reading failed:', error.message);
throw error;
}
}
// Usage
const pageData = await readWebPage('https://example.com/article');
console.log('Page title:', pageData.title);
import ZAI from 'z-ai-web-dev-sdk';
async function extractArticleText(url) {
const zai = await ZAI.create();
const result = await zai.functions.invoke('page_reader', {
url: url
});
// Convert HTML to plain text (basic approach)
const plainText = result.data.html
.replace(/<[^>]*>/g, ' ')
.replace(/\s+/g, ' ')
.trim();
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
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web-reader fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend web-reader for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in web-reader — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
web-reader reduced setup friction for our internal harness; good balance of opinion and flexibility.
web-reader reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for web-reader matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend web-reader for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
I recommend web-reader for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: web-reader is focused, and the summary matches what you get after install.
web-reader has been reliable in day-to-day use. Documentation quality is above average for community skills.
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