Stats Spark ⚡

Automated GitHub profile statistics generator with beautiful SVG visualizations and AI-powered repository analysis

Live Site: https://github-stats.makeboldspark.com

📊 View Sample Analysis Report - See real-world output with AI-powered insights 🎨 View Interactive Dashboard - Explore repositories with live visualizations

About

GitHubStatsSpark is a comprehensive GitHub analytics suite that transforms GitHub activity into actionable insights and stunning visualizations, combining automated SVG profile statistics with AI-powered repository analysis and an interactive mobile-first dashboard.

Live Site: https://github-stats.makeboldspark.com

Built by Mark Hazleton — Mark Hazleton, Solutions Architect GitHubStatsSpark is part of the Make Bold Spark portfolio of technical demonstrations.

🎯 What is Stats Spark?

Stats Spark is a comprehensive GitHub analytics suite that transforms your GitHub activity into actionable insights and stunning visualizations. It combines automated SVG generation for profile statistics with AI-powered repository analysis to give you a complete picture of your development work.

Perfect for:

👨‍💻 Developers wanting to showcase their GitHub activity professionally
📊 Teams analyzing repository health and contribution patterns
🎯 Technical leaders reviewing developer productivity and technology usage
🚀 Open source maintainers tracking project momentum and community engagement

🌟 Why Stats Spark?

Beautiful Profile Statistics

Automated Weekly Updates: GitHub Actions workflow runs at midnight UTC on Sundays
6 Visual Categories: Overview, heatmap, languages, streaks, fun stats, and release cadence
Unique Spark Score: 0-100 metric combining consistency, volume, and collaboration
Theme Customization: Dark, light, and custom themes with WCAG AA accessibility
Zero Maintenance: Set it once, updates automatically every week

AI-Powered Analysis

Intelligent Repository Ranking: Composite algorithm weighing popularity, activity, and health
AI-Generated Summaries: Claude Haiku creates technical summaries with 97%+ success rate
Developer Profiling: Technology diversity, activity patterns, contribution classification
Comprehensive Reports: GitHub-flavored markdown with embedded visualizations
Performance Optimized: Under 5 minutes for up to 500 repositories with smart caching

Interactive Dashboard

Mobile-First Design: Touch-optimized interface with 44x44px touch targets and responsive layouts (320px-768px viewports)
Bottom Sheet Navigation: Native mobile patterns for filters, sort controls, and detailed views
Swipe Gestures: Touch-friendly interactions including swipe-to-delete and horizontal navigation
Needs Attention View: Ranks repositories by combined security alerts, PR backlog, dependency drift, and staleness
Visual Analytics: Interactive Chart.js + react-chartjs-2 visualizations optimized for mobile with touch tooltips
Drill-Down Details: Comprehensive repository analysis with commit history, enriched dependency coverage, and rendered markdown summaries
Export Functionality: Download filtered data as CSV or JSON
Performance Optimized: Lighthouse CI configured targeting <2s First Contentful Paint with 0.9+ performance score
Offline Support: IndexedDB caching with Dexie for offline access and 7-day retention
Accessibility: WCAG 2.1 AA compliant with screen reader support and keyboard navigation
GitHub Pages Deployment: Automatically updates with your latest statistics

Enterprise-Ready

Smart Caching: Intelligent API request optimization
Rate Limit Handling: Automatic retry with exponential backoff
Flexible Configuration: YAML-based configuration for all options
Local Development: Full CLI for testing before deployment
Extensible Architecture: Modular design for easy customization

Stats Spark automatically analyzes your GitHub activity and generates stunning SVG visualizations that you can embed in your profile README. Get insights into your coding patterns, track your streaks, and showcase your Spark Score!

✨ Features

📊 SVG Profile Statistics

Generate beautiful, embeddable SVG visualizations that update automatically:

Overview Dashboard

⚡ Spark Score: Unique 0-100 metric (40% consistency, 35% volume, 25% collaboration)
📈 Key Metrics: Total commits, repositories, languages, active days
⏰ Activity Patterns: Identify your peak coding hours (night owl, early bird, daytime coder)
⚡ Lightning Rating: 1-5 bolts based on your overall activity level

Commit Heatmap

📅 Calendar View: GitHub-style contribution calendar
🔥 Intensity Visualization: Color-coded commit frequency
📊 Pattern Recognition: Identify consistency and work rhythms

Language Statistics

🌐 Technology Stack: Comprehensive language breakdown with percentages
📊 Visual Distribution: Clean bar charts showing language usage
🎯 Diversity Metrics: Track your polyglot programming journey

Streaks & Consistency

🔥 Current Streak: Active coding streak counter
🏆 Longest Streak: Your personal best
📈 Consistency Tracking: Visualize regular contribution patterns

Fun Stats ⚡ ENHANCED

8 personality-driven achievements with emoji flair:

🦉 Coding Time Personality: Night Owl, Early Bird, or Daytime Coder
🚀 Commit Velocity: From "Quality over Quantity" to "Commit Machine"
📚 Repository Collection: Achievement tiers from Focused to Collector
🌐 Language Diversity: Specialist to Polyglot Programmer
⭐ Community Recognition: Stars earned across all repositories
🏛️ Account Longevity: Experience badges from newcomer to veteran
💥 Commit Milestones: Total commits with achievement levels
🌙 Pattern Personality: Custom messages based on coding style

Release Cadence

📊 Sparklines: Weekly and monthly repository diversity
🚀 Activity Breadth: Highlights how many unique repositories were touched in each period, not raw commit volume
📈 Trend Visualization: Track activity patterns over time

🤖 AI-Powered Repository Analysis

Generate comprehensive markdown reports with intelligent insights:

Intelligent Repository Ranking

30% Popularity Weight: Stars and forks from community engagement
45% Activity Weight: Recent commits with time-decay (90d/180d/365d windows)
25% Health Weight: Documentation, licensing, and maintenance signals
Smart Algorithm: Balances established projects with active development

Attention Ranking and Dependency Insights

Schema 2.3.0: Unified repository records now include attention_score, attention_rank, attention_metrics, diagnostics_summary, and screenshot_audit
Dependency Coverage: tech_stack now tracks known versions, registry resolution coverage, and unknown-version gaps
Maintenance Signals: Attention scoring blends PR pressure, security findings, staleness, and dependency health
Markdown-Aware UI: AI summaries are rendered as GitHub-flavored markdown in both desktop and mobile drill-down views

AI-Generated Technical Summaries

Claude Haiku Integration: Enterprise-grade AI summaries for each repository
Three-Tier Fallback: Claude → README extraction → Basic metadata
97%+ Success Rate: Consistent high-quality summaries
Technical Focus: Architecture, tech stack, use cases, and unique features

Developer Profile Analysis

Technology Diversity: Language usage patterns and specialization metrics
Activity Patterns: Coding time preferences and consistency analysis
Contribution Classification: Creator, contributor, maintainer patterns
Observable Trends: Long-term patterns and development focus areas

Comprehensive Reports

GitHub-Flavored Markdown: Perfect formatting for GitHub rendering
Embedded Visualizations: Includes all SVG statistics inline
Rich Metadata: Stars, forks, commits, languages, file sizes
Quality Indicators: License and documentation status badges
Navigation: Quick links to jump between sections

Performance & Reliability

⚡ Fast: Under 5 minutes for up to 500 repositories
🔄 Smart Caching: Reduces API calls by 80%+ through content-addressed cache invalidation (keyed by pushed_at timestamp)
🧠 Intelligent Refresh: Only updates repositories with new commits
🛡️ Rate Limit Safe: Automatic handling and retry logic
📊 Progress Tracking: Real-time feedback during generation
♿ Accessible: WCAG AA compliant visualizations

🔧 Developer Features

🎯 Selective Output: Choose which statistics and reports to generate
🖥️ Local CLI: Full command-line interface for testing and development
📝 YAML Configuration: Centralized configuration for themes, options, and behavior
🚀 GitHub Actions: Pre-configured workflow for automated weekly updates
🎨 Custom Themes: Define your own color schemes and styles
📦 Modular Architecture: Clean separation of concerns for easy extension
🧪 Comprehensive Tests: 19+ Python test modules (target: 80%+ core module coverage)
📚 Full Documentation: Detailed guides, API reference, and examples

🚀 Quick Start

⚡ Unified Pipeline Script (Recommended)

The easiest way to run the complete 4-phase pipeline:

# Windows PowerShell
.\run-spark.ps1 -User YOUR_USERNAME -IncludeAI -Verbose

# Preserve outputs for multiple users side by side
.\run-spark.ps1 -User YOUR_USERNAME -MultiUser

# Check environment first
.\run-spark.ps1 -CheckOnly

Script handles:

✅ Environment validation (virtual env, tokens, config)
✅ Python package installation
✅ Cache management
✅ Complete 4-phase pipeline execution
✅ Output verification and summary

Options:

-User - GitHub username (default: markhazleton)
-IncludeAI - Generate AI summaries
-MultiUser - Store outputs under per-user folders instead of overwriting shared files
-ClearCache - Clear all caches before running
-ForceRefresh - Force refresh all data
-Screenshots - Capture repository website screenshots (requires Playwright)
-MissingOnly - Only capture screenshots where PNG doesn't exist yet
-Verbose - Enable detailed logging
-CheckOnly - Validate environment only

📦 Manual Setup & CLI

For direct Python CLI usage:

# 1. Install dependencies
python -m venv .venv
source .venv/bin/activate  # Unix/Mac
# .\.venv\Scripts\Activate.ps1  # Windows
pip install -r requirements.txt
pip install -e .

# 2. Set environment variables
export GITHUB_TOKEN=your_github_token_here
export ANTHROPIC_API_KEY=your_anthropic_key_here  # Optional

# 3. Run unified command
spark unified --user YOUR_GITHUB_USERNAME --include-ai-summaries

This single command generates:

✅ /data/repositories.json - Complete unified dataset for frontend
✅ /output/users/{username}/*.svg - All 6 visual analytics (overview, heatmap, languages, streaks, fun, release)
✅ /output/users/{username}/reports/*.md - Comprehensive markdown analysis report
✅ AI summaries for each repository (if API key provided)

Notable unified data additions in schema 2.3.0:

✅ Attention ranking fields for maintenance triage
✅ Pull request and security summary payloads with explicit availability semantics
✅ Enriched dependency version coverage and latest-version resolution metrics
✅ Diagnostics summary and screenshot audit payloads for public-repo reputation checks

Benefits:

🚀 ~60% faster than separate commands
💾 Single API pass (fewer rate limit issues)
🎯 Consistent data snapshot across all outputs
⚡ Optimized data gathering and caching

Testing/Debugging Options:

# Test with only 2 repositories (fast cache validation)
spark unified --user YOUR_USERNAME --max-repos 2

# Force refresh all data (bypass cache)
spark unified --user YOUR_USERNAME --force-refresh

# Verbose logging for debugging
spark unified --user YOUR_USERNAME --verbose

See QUICKSTART_UNIFIED.md for detailed instructions.

GitHub Actions Automation

Or set up automatic weekly updates:

1. Fork This Repository

Click the "Fork" button in the top right to create your own copy.

2. Enable GitHub Actions

Go to Settings → Actions → General
Select "Allow all actions and reusable workflows"
Click Save

3. Run the Workflow

Navigate to Actions tab
Select "Generate GitHub Statistics"
Click "Run workflow" → "Run workflow"
Wait 2-5 minutes for completion

4. Embed in Your Profile

Add to your profile README (username/username/README.md):

![GitHub Stats](https://raw.githubusercontent.com/YOUR_USERNAME/github-stats-spark/main/output/users/YOUR_USERNAME/overview.svg)

Replace YOUR_USERNAME with your GitHub username!

Full instructions: Getting Started Guide

📊 Statistics Categories

Stats Spark generates 6 SVG categories for your GitHub profile:

Category	Description	Output File
Overview	Spark Score, commits, languages, time pattern	`overview.svg`
Heatmap	Commit frequency calendar	`heatmap.svg`
Languages	Programming language breakdown	`languages.svg`
Streaks	Current and longest coding streaks	`streaks.svg`
Fun Stats ⚡	8 personality-driven achievements	`fun.svg`
Release Cadence	Weekly + monthly repo diversity sparklines	`release.svg`

⚡ Enhanced Fun Stats (New!)

The Fun Stats visualization now showcases 8 creative measurements with personality:

🦉 Coding Time Personality - Night Owl, Early Bird, or Daytime Coder
🚀 Commit Velocity - From "Quality over Quantity" to "Commit Machine"
📚 Repository Collection - Achievement tiers from Focused to Collector
🌐 Language Diversity - Specialist to Polyglot Programmer
⭐ Community Recognition - Stars earned across all repositories
🏛️ Account Longevity - Experience badges from newcomer to veteran
💥 Commit Milestones - Total commits with achievement levels
🌙 Pattern Personality - Custom messages based on your coding style

🤖 Repository Analysis Reports

Stats Spark's AI-powered analysis feature generates comprehensive markdown reports that showcase your complete GitHub profile:

Report Structure

Profile Overview Section
- Embedded SVG visualizations (all 5 categories)
- Quick navigation links to major sections
- Generation metadata and statistics
Top Repositories Listing (default: top 50)
- Ranked by composite algorithm (popularity + activity + health)
- AI-generated technical summaries for each repository
- Rich metadata: stars, forks, languages, commit activity
- Quality indicators: license and documentation badges
- Repository statistics: contributors, file size, commit velocity
Developer Profile Insights
- Overall technology diversity and language specialization
- Activity patterns and coding time preferences
- Contribution classification (creator vs. contributor)
- Observable trends and development focus
Report Metadata
- Generation timestamp and version information
- AI summary success rate and coverage statistics
- Tool attribution and data sources

Sample Output

📊 View Full Sample Report

The sample report demonstrates:

✅ 37 repositories analyzed with AI-powered summaries
✅ Detailed technical summaries for each major project
✅ Complete activity visualizations and metrics
✅ Professional GitHub-flavored markdown formatting
✅ Easy navigation and comprehensive insights

⚡ Spark Score

The Spark Score is a 0-100 metric reflecting your GitHub activity:

Formula: 40% Consistency + 35% Volume + 25% Collaboration

Lightning Rating: 1-5 bolts based on your score

⚡⚡⚡⚡⚡ (80-100): Exceptional
⚡⚡⚡⚡ (60-79): Strong
⚡⚡⚡ (40-59): Good
⚡⚡ (20-39): Growing
⚡ (0-19): Starting

🎨 Themes

spark-dark (default): Dark theme with electric blue and gold
spark-light: Light theme with WCAG AA colors
custom: Define your own in config/themes.yml

See Configuration Guide for theme customization.

💻 Local CLI

Stats Spark provides a comprehensive command-line interface for local development and testing.

Installation

# Clone the repository
git clone https://github.com/YOUR_USERNAME/github-stats-spark.git
cd github-stats-spark

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
export GITHUB_TOKEN=your_github_token
export ANTHROPIC_API_KEY=your_anthropic_key  # Optional for AI summaries

Generate SVG Statistics

Create beautiful visualizations for your GitHub profile:

# Generate all statistics
spark generate --user YOUR_USERNAME

# Use custom theme (via config file)
spark generate --user YOUR_USERNAME --output-dir ./my-stats

# Force refresh all data (bypass cache)
spark generate --user YOUR_USERNAME --force-refresh

# Generate dashboard JSON data
spark generate --user YOUR_USERNAME --dashboard

# Preview theme without generating
spark preview --theme spark-dark

# Validate configuration
spark config --validate

🆕 Generate AI-Powered Analysis Reports

Create comprehensive markdown reports with repository analysis:

# Analyze top 50 repositories and generate full report
spark analyze --user YOUR_USERNAME

# List top repositories without generating report (dry-run)
spark analyze --user YOUR_USERNAME --list-only

# Customize analysis
spark analyze --user YOUR_USERNAME --top-n 25 --output output/reports

# Generate unified report (SVGs + analysis in one pass)
spark analyze --user YOUR_USERNAME --unified

# Also keep a dated copy alongside the unified report
spark analyze --user YOUR_USERNAME --unified --keep-dated

# Store outputs under per-user folders
spark analyze --user YOUR_USERNAME --unified

# Verbose output for debugging
spark analyze --user YOUR_USERNAME --verbose

Analysis Command Features:

📊 Intelligent repository ranking with composite scoring
🤖 AI-powered technical summaries (requires ANTHROPIC_API_KEY)
📈 Multi-window activity analysis (90d/180d/365d)
👤 Developer profile generation with observable patterns
📝 GitHub-flavored markdown output with embedded visualizations
⚡ High performance with smart caching to minimize API calls

Options:

--user USERNAME: GitHub username to analyze (required)
--top-n N: Number of top repositories to include (default: 50)
--output DIR: Output directory for reports (default: output/reports)
--list-only: List top repositories without generating report
--unified: Generate unified report (SVGs + analysis) instead of dated report
--keep-dated: Also generate dated report when using --unified mode
--verbose: Enable detailed logging

See Analyze Command Guide for detailed documentation.

🔄 Cache & Refresh Commands

Manage cached data and perform incremental updates:

# Show cache information
spark cache --info

# Show per-repository cache status
spark cache --status --user YOUR_USERNAME

# List repositories needing cache refresh
spark cache --list-refresh-needed --user YOUR_USERNAME

# Clear all cached data
spark cache --clear

# Prune old cache entries (keep last 2 weeks)
spark cache --prune

# Smart incremental refresh (updates only changed repos)
spark refresh --user YOUR_USERNAME

# Refresh and regenerate AI summaries for changed repos
spark refresh --user YOUR_USERNAME --include-ai-summaries

# Clear AI summaries to force regeneration
spark refresh --user YOUR_USERNAME --clear-summaries

📚 Documentation

Comprehensive guides and references for all features:

Getting Started

Getting Started Guide - Complete setup instructions for GitHub Actions
Configuration Guide - All configuration options and customization
Embedding Guide - How to embed SVGs in your profile README

Feature Documentation

Analyze Command Guide - AI-powered repository analysis deep dive
API Reference - Developer documentation for core modules
Changelog - Version history and release notes

Examples

Sample Analysis Report - Real-world output with AI-powered insights
Theme Gallery - Available themes and customization options

Support

Issues - Report bugs or request features
Discussions - Ask questions and share ideas

🔧 Troubleshooting

Common Issues

GitHub Actions Workflow Fails

Problem: Workflow runs but doesn't complete successfully

Solutions:

Check Actions logs in the Actions tab
Verify GitHub Actions is enabled: Settings → Actions → General
Ensure GITHUB_TOKEN permissions are correct
Check if rate limits were hit (workflow handles automatically)

SVGs Don't Display in Profile

Problem: Embedded images show broken or don't load

Solutions:

Verify URLs use your correct username
Check files exist in output/users/{username}/ directory
Ensure branch name is correct (usually main)
Try accessing the raw image URL directly
Clear browser cache and refresh

Example correct URL:

![GitHub Stats](https://raw.githubusercontent.com/YOUR_USERNAME/github-stats-spark/main/output/users/YOUR_USERNAME/overview.svg)

Rate Limiting Issues

Problem: Getting rate limit errors from GitHub API

Solutions:

Workflow automatically handles with caching and retries
For local development, wait for rate limit reset
Use authenticated requests (GITHUB_TOKEN is recommended)
Enable caching in configuration

AI Summaries Not Generating

Problem: Repository analysis runs but summaries are missing

Solutions:

Verify ANTHROPIC_API_KEY is set correctly
Check API key has sufficient credits/quota
Review logs for API errors
Try --no-ai flag to use README extraction fallback

Local CLI Issues

Problem: Commands fail or produce errors

Solutions:

Verify Python 3.11+ is installed: python --version
Install dependencies: pip install -r requirements.txt
Set environment variables correctly
Run with --verbose flag for detailed output
Check configuration with spark config --validate

Getting Help

Still stuck? We're here to help:

📖 Check Getting Started Guide for detailed setup
🔍 Search existing issues
💬 Start a discussion
🐛 Open a new issue with details

🤝 Contributing

We welcome contributions of all kinds! Stats Spark is an open-source project that thrives on community involvement.

Ways to Contribute

🐛 Report Bugs

Found an issue? Open a bug report with:

Clear description of the problem
Steps to reproduce
Expected vs. actual behavior
Environment details (Python version, OS, etc.)

💡 Suggest Features

Have an idea? Start a discussion or open a feature request describing:

The problem you're trying to solve
Proposed solution or feature
Use cases and benefits
Any relevant examples or mockups

🔧 Submit Pull Requests

Ready to code? We'd love your contributions:

Fork the repository and create a feature branch
Make your changes following our code style
Add tests for new functionality
Update documentation as needed
Submit a PR with a clear description

Good First Issues: Look for issues labeled good first issue for beginner-friendly tasks.

📖 Improve Documentation

Fix typos or clarify existing docs
Add examples or tutorials
Improve code comments
Create guides for common use cases

🎨 Share Your Usage

Show how you're using Stats Spark
Share your custom themes
Write blog posts or tutorials
Spread the word on social media

Development Setup

# Clone your fork
git clone https://github.com/YOUR_USERNAME/github-stats-spark.git
cd github-stats-spark

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
pytest

# Run tests with coverage
pytest --cov=spark --cov-report=html

# View coverage report
start htmlcov/index.html  # Windows
open htmlcov/index.html   # macOS
xdg-open htmlcov/index.html  # Linux

Code Quality Standards

✅ Follow PEP 8 style guidelines
✅ Write descriptive commit messages
✅ Add docstrings to public functions/classes
✅ Include type hints where appropriate
✅ Maintain or improve test coverage (target: 80%+ core modules)
✅ Update relevant documentation

Architecture Overview

For contributors, see API Reference for detailed module documentation including:

Core modules: fetcher, calculator, visualizer, summarizer, config
Analysis modules: ranker, report_generator, unified_report_workflow, unified_data_generator
Cache modules: cache, cache_manager, cache_status, cache_refresh_strategy
Utilities: logger, time_utils, screenshot
CLI: cli, cli_argument_builders, cli_output_layout

🧪 Testing

Stats Spark maintains comprehensive test coverage to ensure reliability and quality.

Running Tests

# Run all tests
pytest

# Run with detailed output
pytest -v

# Run specific test file
pytest tests/unit/test_calculator.py

# Run tests matching pattern
pytest -k "test_spark_score"

# Run with coverage report
pytest --cov=spark --cov-report=html

# View coverage in browser
start htmlcov/index.html  # Windows
open htmlcov/index.html   # macOS

Coverage Statistics

Module	Coverage	Status
visualizer.py	91%	✅ Excellent
config.py	88%	✅ Excellent
calculator.py	87%	✅ Excellent
cache.py	63%	🔶 Improving
ranker.py	26%	⚠️ Needs work
summarizer.py	19%	⚠️ Needs work
fetcher.py	13%	⚠️ Needs work
Overall	34%	🔶 Improving

Target: 80%+ coverage for all core modules. A coverage remediation suite is in progress.

Test Organization

tests/
├── unit/              # Unit tests for individual modules
│   ├── test_cache.py
│   ├── test_cache_manager.py
│   ├── test_calculator.py
│   ├── test_cli.py
│   ├── test_config.py
│   ├── test_dashboard_generator.py
│   ├── test_dependency_parser.py
│   ├── test_fetcher.py
│   ├── test_fetcher_api_version.py
│   ├── test_profile_generator.py
│   ├── test_ranker.py
│   ├── test_report_generator.py
│   ├── test_summarizer.py
│   ├── test_unified_data_generator_enrichment.py
│   ├── test_unified_report_workflow.py
│   ├── test_visualizer.py
│   ├── test_wcag.py
│   └── ...
├── integration/       # Integration tests
│   ├── test_dependency_analysis.py
│   ├── test_unified_repository_enrichment.py
│   └── ...
└── fixtures/          # Test data and configurations
    ├── sample_config.yml
    ├── sample_repositories.json
    └── ...

Note: Frontend tests (Vitest) are configured in frontend/package.json but test files have not yet been written.

📄 License

MIT License - See LICENSE for details

This project is free and open-source. You can:

✅ Use commercially
✅ Modify and distribute
✅ Use privately
✅ Sublicense

Attribution appreciated but not required!

🙏 Acknowledgments

Stats Spark is built on the shoulders of giants:

Core Technologies

PyGithub - GitHub API wrapper for Python
svgwrite - SVG generation library
Anthropic Claude - AI-powered repository summaries
Python 3.11+ - Modern Python features and performance

Inspiration

GitHub's contribution graph and profile statistics
Open source community for continuous feedback and ideas

Contributors

Thank you to all contributors who have helped make Stats Spark better!

View all contributors →

🌟 Star History

If you find Stats Spark useful, please consider giving it a star! ⭐

It helps others discover the project and motivates continued development.

📊 Usage Examples

In Profile README

# Your Name

![GitHub Stats](https://raw.githubusercontent.com/YOUR_USERNAME/github-stats-spark/main/output/users/YOUR_USERNAME/overview.svg)

## Activity

![Commit Heatmap](https://raw.githubusercontent.com/YOUR_USERNAME/github-stats-spark/main/output/users/YOUR_USERNAME/heatmap.svg)

## Languages

![Language Distribution](https://raw.githubusercontent.com/YOUR_USERNAME/github-stats-spark/main/output/users/YOUR_USERNAME/languages.svg)

## Analysis

Check out my [detailed GitHub analysis](output/users/YOUR_USERNAME/reports/YOUR_USERNAME-analysis.md) with AI-powered insights!

In Project README

## Developer Activity

![GitHub Stats](https://raw.githubusercontent.com/YOUR_USERNAME/github-stats-spark/main/output/overview.svg)

*Updated weekly via [Stats Spark](https://github.com/markhazleton/github-stats-spark)*

Custom Sections

Create themed sections in your profile:

<div align="center">

# ⚡ GitHub Activity Dashboard

![Overview](https://raw.githubusercontent.com/YOUR_USERNAME/github-stats-spark/main/output/overview.svg)

![Languages](https://raw.githubusercontent.com/YOUR_USERNAME/github-stats-spark/main/output/languages.svg)
![Streaks](https://raw.githubusercontent.com/YOUR_USERNAME/github-stats-spark/main/output/streaks.svg)

![Fun Stats](https://raw.githubusercontent.com/YOUR_USERNAME/github-stats-spark/main/output/fun.svg)

</div>

⚡ Powered by Stats Spark

Illuminate your GitHub activity with beautiful statistics and AI-powered insights

Quick Start • Features • Documentation • Report Issue • Contribute

Made with ❤️ by developers, for developers

Name		Name	Last commit message	Last commit date
Latest commit History 288 Commits
.archive		.archive
.claude		.claude
.devspark		.devspark
.documentation		.documentation
.github		.github
.vs		.vs
assets		assets
config		config
data/users/markhazleton		data/users/markhazleton
docs		docs
frontend		frontend
output		output
preview		preview
src		src
tests		tests
.gitignore		.gitignore
.markdownlint-cli2.yaml		.markdownlint-cli2.yaml
.markdownlint.yml		.markdownlint.yml
.markdownlintignore		.markdownlintignore
.pytest_summarizer_results.xml		.pytest_summarizer_results.xml
.replit		.replit
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
LICENSE		LICENSE
README.md		README.md
agents-registry.json		agents-registry.json
audit_output.json		audit_output.json
coverage-remediation.json		coverage-remediation.json
coverage.json		coverage.json
replit.md		replit.md
requirements-dev.txt		requirements-dev.txt
requirements.txt		requirements.txt
run-spark.ps1		run-spark.ps1
setup.py		setup.py
test_scrape.py		test_scrape.py

Uh oh!

Folders and files

Latest commit

History

Repository files navigation

Stats Spark ⚡

About

🎯 What is Stats Spark?

🌟 Why Stats Spark?

Beautiful Profile Statistics

AI-Powered Analysis

Interactive Dashboard

Enterprise-Ready

✨ Features

📊 SVG Profile Statistics

Overview Dashboard

Commit Heatmap

Language Statistics

Streaks & Consistency

Fun Stats ⚡ ENHANCED

Release Cadence

🤖 AI-Powered Repository Analysis

Intelligent Repository Ranking

Attention Ranking and Dependency Insights

AI-Generated Technical Summaries

Developer Profile Analysis

Comprehensive Reports

Performance & Reliability

🔧 Developer Features

🚀 Quick Start

⚡ Unified Pipeline Script (Recommended)

📦 Manual Setup & CLI

GitHub Actions Automation

1. Fork This Repository

2. Enable GitHub Actions

3. Run the Workflow

4. Embed in Your Profile

📊 Statistics Categories

⚡ Enhanced Fun Stats (New!)

🤖 Repository Analysis Reports

Report Structure

Sample Output

⚡ Spark Score

🎨 Themes

💻 Local CLI

Installation

Generate SVG Statistics

🆕 Generate AI-Powered Analysis Reports

🔄 Cache & Refresh Commands

📚 Documentation

Getting Started

Feature Documentation

Examples

Support

🔧 Troubleshooting

Common Issues

GitHub Actions Workflow Fails

SVGs Don't Display in Profile

Rate Limiting Issues

AI Summaries Not Generating

Local CLI Issues

Getting Help

🤝 Contributing

Ways to Contribute

🐛 Report Bugs

💡 Suggest Features

🔧 Submit Pull Requests

📖 Improve Documentation

🎨 Share Your Usage

Development Setup

Code Quality Standards

Architecture Overview

🧪 Testing

Running Tests

Coverage Statistics

Test Organization

📄 License

🙏 Acknowledgments

Core Technologies

Inspiration

Packages