3 stable releases

1.0.2	Oct 14, 2025
1.0.1	Oct 10, 2025
1.0.0	Oct 8, 2025

#431 in Web programming

MIT/Apache

1MB
25K SLoC

Webpage Quality Analyzer

High-performance webpage quality analyzer with 115 comprehensive metrics. Analyze web pages for SEO, content quality, technical standards, accessibility, and more - all in milliseconds.

🚀 Features

115 Comprehensive Metrics (92 HTML-based + 23 network-based) across 7 major categories (Content, SEO, Technical, Accessibility, and more)
8 Built-in Profiles optimized for different page types (news, blog, product, portfolio, etc.)
Multi-Platform Support: Native Rust, WebAssembly (browser/Node.js), C++ FFI
High Performance: 100+ pages/second batch processing with parallel analysis
Advanced Customization: Metric weights, thresholds, penalties, bonuses, and field selectors
Profile-Aware Scoring: Phase 3-6 implementation with category-based weighted scoring
Output Optimization: Field selection with up to 98.8% size reduction
Production Ready: Battle-tested, 40+ test files, extensive documentation

📦 Installation

Add this to your Cargo.toml:

[dependencies]
webpage_quality_analyzer = "1.0"

Or use cargo:

cargo add webpage_quality_analyzer

🎯 Quick Start

Level 1: Simple Usage

use webpage_quality_analyzer::{analyze, analyze_with_profile};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Analyze with default settings
    let report = analyze("https://example.com", None).await?;
    
    println!("Score: {}/100", report.score);
    println!("Quality: {}", report.verdict);
    println!("Word Count: {}", report.metrics.content_metrics.word_count);
    
    // Analyze with specific profile
    let news_report = analyze_with_profile(
        "https://example.com",
        None,
        "news"
    ).await?;
    
    Ok(())
}

Level 2: Builder Pattern

use webpage_quality_analyzer::Analyzer;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Build custom analyzer
    let analyzer = Analyzer::builder()
        .with_profile_name("blog")?
        .with_metric_weight("word_count", 1.5)?
        .disable_metric("grammar_score")?
        .with_timeout_secs(30)?
        .build()?;
    
    let report = analyzer.run("https://example.com", None).await?;
    println!("Custom analysis score: {}", report.score);
    
    Ok(())
}

Level 3: Advanced Configuration

use webpage_quality_analyzer::{from_config_file, analyze_batch_high_performance};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Load from YAML/JSON/TOML config file
    let analyzer = from_config_file("config.yaml")?;
    let report = analyzer.run("https://example.com", None).await?;
    
    // High-performance batch processing
    let urls = vec![
        "https://site1.com",
        "https://site2.com",
        "https://site3.com",
    ];
    
    let json_results = analyze_batch_high_performance(
        &urls,
        None,        // HTML (None = fetch from URLs)
        50,          // Max concurrent requests
        Some("news") // Profile name
    ).await?;
    
    for report in reports {
        println!("{}: {}/100", report.url, report.score);
    }
    
    Ok(())
}

Analyzing HTML Directly

let html = r#"
    <!DOCTYPE html>
    <html lang="en">
    <head>
        <title>Sample Page</title>
        <meta name="description" content="A sample page">
    </head>
    <body>
        <h1>Welcome</h1>
        <p>This is a test page with some content.</p>
    </body>
    </html>
"#;

let report = analyze("https://example.com", Some(html.to_string())).await?;
println!("HTML analysis score: {}", report.score);

📊 Metrics Categories

All 115 metrics (92 HTML-based + 23 network-based):

Major Categories (7 total)

Content (11 metrics) - Word count, readability (Flesch-Kincaid), text quality, content density
SEO (9 metrics) - Meta tags, Open Graph, structured data, canonical URLs
Technical (6 metrics) - HTML size, scripts, styles, validation
Semantic (4 metrics) - Heading hierarchy, heading length, heading distribution
Accessibility (7 metrics) - WCAG compliance, ARIA labels, contrast, alt text
Network (23 metrics) - Performance (LCP, FCP), Security (HTTPS, CSP), Analytics
Miscellaneous (55 metrics) - Links (8), Media (8), Forms (6), Structure (5), UX (5), Mobile (4), Branding (4), Structured Data (4), Business (3), Authority (3), Error (3), Internationalization (2)

Metric Distribution

92 metrics (80%) - HTML-only, no network required (WASM-compatible)
23 metrics (20%) - Network-required (when fetching URLs, server-side only)

See: Complete metrics breakdown

🎨 Available Profiles

Choose the right profile for your page type (8 built-in profiles):

Profile	Best For	Content Weight	Key Focus
`content_article`	Long-form articles	80%	Word count, structure, comprehensiveness
`blog`	Blog posts	75%	Content quality, engagement, readability
`news`	News articles	40%	Content freshness, readability, SEO (30%)
`general`	Any webpage	35%	Balanced scoring across all categories
`homepage`	Landing pages	25%	Navigation, structure, balanced (25% each)
`product`	Product pages	20%	Media (35%), SEO (25%), product details
`portfolio`	Creative showcases	15%	Media (50%), visual content
`login_page`	Authentication	10%	Technical (50%), accessibility (20%), security

Profile Customization: Each profile includes:

Category weights (Content, SEO, Technical, Semantic, Accessibility)
Content expectations (word count, headings, images)
Metric overrides (custom weights and thresholds)
Penalties (severe, moderate, light)
Bonuses (excellence, achievement, synergy)

⚙️ Feature Flags

Control optional features via Cargo features:

[dependencies]
webpage_quality_analyzer = { version = "1.0", features = ["async", "linkcheck", "nlp"] }

Available features:

async (default) - Async runtime with tokio + reqwest
readability (default) - Mozilla Readability content extraction
linkcheck - External link validation
nlp - Language detection and Unicode segmentation
grammar - Grammar checking (via nlprule)
wasm - WebAssembly bindings (mutually exclusive with async)
ffi - C FFI for C++ integration
cli - Command-line tool binary

🌐 Multi-Platform Support

WebAssembly (Browser/Node.js)

# Build for npm
wasm-pack build --target bundler --no-default-features --features wasm

# Use in JavaScript/TypeScript
npm install @webpage-quality-analyzer/core

import { WasmAnalyzer } from '@webpage-quality-analyzer/core';

const analyzer = new WasmAnalyzer();
const report = await analyzer.analyze('<html>...</html>');
console.log(`Score: ${report.score}/100`);

C++ Integration

#include "webpage_quality_analyzer.hpp"

CAnalyzer* analyzer = wqa_analyzer_new();
CReport* report = wqa_analyze(analyzer, "https://example.com", nullptr);
double score = wqa_report_get_score(report);

Command-Line Tool

# Download binary from releases
wqa analyze https://example.com
wqa batch urls.txt --parallel 10
wqa profiles  # List available profiles

🔧 Customization

Custom Metric Weights

let analyzer = Analyzer::builder()
    .with_profile_name("blog")?
    .with_metric_weight("word_count", 1.5)?       // Increase importance
    .with_metric_weight("readability_score", 2.0)? // Double weight
    .build()?;

Custom Thresholds

let analyzer = Analyzer::builder()
    .with_profile_name("blog")?
    .set_metric_threshold(
        "word_count",
        100.0,   // min
        800.0,   // optimal_min
        2000.0,  // optimal_max
        5000.0   // max
    )?
    .build()?;

Custom Penalties & Bonuses

use webpage_quality_analyzer::{GlobalPenalty, PenaltyTrigger, PenaltyType};

let analyzer = Analyzer::builder()
    .with_profile_name("news")?
    .add_penalty(GlobalPenalty {
        trigger: PenaltyTrigger::MetricBelow {
            metric: "word_count".to_string(),
            threshold: 500.0,
        },
        penalty: PenaltyType::FixedPoints { points: 10.0 },
        description: "Content too short".to_string(),
    })?
    .add_bonus_above("readability_fk", 80.0, 5.0, "Highly readable")?
    .build()?;

Disable Metrics

let analyzer = Analyzer::builder()
    .with_profile_name("general")?
    .disable_metric("grammar_score")?
    .disable_metric("language_detection")?
    .build()?;

Output Customization (Phase 6)

// Full report (default)
let report = analyzer.run(url, html).await?;

// Compact JSON (20-30% size reduction)
let compact_json = analyzer.run_compact(url, html).await?;

// Minimal output (98.8% size reduction)
let minimal = analyzer.run_with_fields(
    url, 
    html,
    vec!["score", "verdict", "url"]
).await?;

// Advanced field selection
use webpage_quality_analyzer::FieldSelector;
let selector = FieldSelector::builder()
    .include_sections(vec!["metrics"])
    .exclude_section("processed_document")
    .build();
let custom = analyzer.run_with_selector(url, html, &selector).await?;

📈 Performance

Analysis Speed:

Single page (HTML-only): <100ms (typical), ~200ms (large docs)
Single page (with network): ~300-500ms
Batch processing: 180+ pages/second (HTML-only), 50+ pages/second (with network)
Memory: Linear scaling, stable across repeated analyses
Thread-safe: Fully concurrent with Arc<Semaphore> control

Output Optimization:

Full report: 30-50 KB (pretty), 12-18 KB (compact)
Minimal output: 500 bytes (98.8% reduction)
Custom fields: 300 bytes (3 fields)

// High-performance batch processing
use webpage_quality_analyzer::analyze_batch_high_performance;

let urls = vec![/* ... 100 URLs ... */];
let json_results = analyze_batch_high_performance(
    &urls,
    None,        // Fetch HTML from URLs
    50,          // Max 50 concurrent requests
    Some("news") // Profile
).await?;

📚 Documentation

🧪 Testing

cargo test                              # Run all tests
cargo test --features linkcheck         # With network features
cargo bench                             # Run benchmarks

📄 License

Dual licensed under MIT OR Apache-2.0. You can choose either license.

🤝 Contributing

Contributions are welcome! Please feel free to submit issues, feature requests, or pull requests.

NPM: @webpage-quality-analyzer/core - JavaScript/TypeScript (WASM)
CLI: Download binaries for Linux/Windows/macOS
C++: Pre-compiled libraries with headers
Python: Coming soon (PyO3 bindings)

🌟 Why Choose This Analyzer?

Comprehensive: 115 metrics across 20 categories covering all aspects of webpage quality
Fast: Rust-powered performance, 180+ pages/sec batch processing
Flexible: 8 profiles + full customization of weights, thresholds, penalties, bonuses
Multi-Platform: Works everywhere - Native Rust, WASM (browser/Node.js), C++ FFI
Production-Ready: 40+ test files, 279-line test README, extensive documentation
Modern: profile-aware scoring, output optimization, field selectors
Optimized: DOM caching, streaming serialization, 98.8% output size reduction

📊 Example Report

{
  "score": 7.5,
  "verdict": "Very Poor",
  "url": "https://example.com",
  "metrics": {
    "content_metrics": {
      "word_count": 10,
      "paragraph_count": 1,
      "avg_sentence_length": 7.5,
      "readability_flesch_kincaid": 68.2
    },
    "technical_metrics": {
      "title_length": 14,
      "has_meta_description": true,
      "html_size_bytes": 12320
    },
    "seo_metrics": {
      "has_og_tags": true,
      "has_schema_org": true,
      "canonical_url_present": true
    }
  },
  "phase3_scoring": {
    "category_scores": {
      "Content": 2.3,
      "SEO": 68.5,
      "Technical": 45.0
    }
  }
}

Made with ❤️ in Rust | Version 1.0.0 | October 2025

Dependencies

~11–31MB
~450K SLoC