DEV.to Metrics Tracker 📊

Automatic collection and historical analysis of your DEV.to metrics

"Without historical data, you only see snapshots. With historical data, you see trends."

🎯 Goals

1. Automatically collect all your DEV.to metrics
2. Store historical snapshots for long-term analysis
3. Deep-dive into engagement (comments, followers, etc.)
4. Never lose data again — the core idea behind the project

📦 Files

devto-metrics-tracker/
├── devto_tracker.py          # Main collection script
├── comment_analyzer.py       # Deep comment analysis
├── setup_automation.sh       # Automatic cron setup
├── advanced_analytics.py     # Advanced metrics analysis
├── anrety.py                 # Article analysis tool
├── checkcoverage.py          # Coverage verification
├── checkincremental.py       # Incremental data checks
├── cleanup_articles.py       # Cleanup of removed articles
├── cli_to_svg.py             # CLI-to-SVG converter
├── dashboard.py              # Metrics dashboard
├── diagnose.py               # Metrics diagnostics
├── fix.py                    # Error correction script
├── list_articles.py          # List collected articles
├── nlp_analyzer.py           # NLP analysis on comments
├── quality_analytics.py      # Article quality analysis
├── quick_check.py            # Quick metrics validation
├── traffic_analytics.py      # Traffic insights

🚀 Quick Install

1. Requirements

pip install requests python-dotenv

2. Configuration

Create a .env file in the project root:

# Copy the example file
cp .env.example .env

# Edit .env and add your API key
DEVTO_API_KEY=your_actual_api_key_here
GEMINI_API_KEY=your_gemini_key_here  # Optional

Get your DEV.to API key from: https://dev.to/settings/extensions

3. Initialization

# Initialize database
python3 devto_tracker.py --init

# Collect your first snapshot
python3 devto_tracker.py --collect

Note: API keys are now loaded from .env file automatically. No need to pass them via command line!

3. Automation (recommended)

export DEVTO_API_KEY='your-api-key'
chmod +x setup_automation.sh
./setup_automation.sh

This will:

• Initialize the database
• Perform a test snapshot
• Create a cron wrapper
• Suggest different collection frequencies

📊 Database Structure

Table: snapshots

Daily overview of all metrics.

Column	Description
collected_at	Timestamp
total_articles	Total number of articles
total_views	Total views
total_reactions	Total reactions
total_comments	Total comments
follower_count	Total followers

Table: article_metrics

Per-article metrics at each collection.

Column	Description
collected_at	Timestamp
article_id	Article ID
title	Title
views	Views
reactions	Reactions
comments	Comments
tags	JSON array

Table: follower_events

Column	Description
collected_at	Timestamp
follower_count	Count
new_followers_since_last	Increment

Table: comments

Column	Description
comment_id	Comment ID
article_id	Article
created_at	Timestamp
author_username	Author
body_html	HTML content
body_length	Length

🔍 Usage

Manual collection

python3 devto_tracker.py --api-key YOUR_KEY --collect
python3 devto_tracker.py --api-key YOUR_KEY --analyze-growth 30
python3 devto_tracker.py --api-key YOUR_KEY --analyze-article 123456

Comment analysis

python3 comment_analyzer.py --article 123456
python3 comment_analyzer.py --compare
python3 comment_analyzer.py --engaged-readers
python3 comment_analyzer.py --timing
python3 comment_analyzer.py --full-report

📈 Examples of Questions Answered

Growth Analysis

• How many views did I gain per day this week?
• Which article has the best velocity (views/day)?
• When did I gain the most followers?

Comment Deep-Dive

• Who are my most loyal readers?
• How fast do comments arrive after publication?
• Which articles spark the most conversation?
• What's the average comment length?

Correlation Analysis

• Which article brought me the most followers?
• Is there a link between comment count and follower growth?
• Do long articles generate more engagement?

⏰ Recommended Collection Frequency

• Early Stage (0–1000 followers): 2×/day
• Growth (1000–5000 followers): 4×/day
• Established (5000+ followers): 6×/day

💡 Real-World Use Cases

Case 1: Viral article

• Hour-by-hour growth curve
• Spike detection
• Correlating DEV.to staff comments
• Long-tail behavior

Case 2: Follower growth

Daily follower increments and stabilization patterns.

Case 3: Comment engagement

Deep qualitative and quantitative insights.

🔧 Maintenance

Backup, DB size checks, cleanup, VACUUM, and more.

📊 Useful SQL Queries

Includes:

• 7-day article growth
• Best days of the week
• Comment velocity

🚨 Troubleshooting

Database lock issues, rate-limit handling, missing data logs, etc.

🎯 Roadmap

• Web dashboard
• CSV export
• Email alerts
• Multi-API integration
• Sentiment analysis
• Performance prediction

📝 Important Notes

• DEV.to does not provide historical metrics
• Respect API limits
• Keep your DB private

🤝 Contributing

Fork → Branch → Commit → PR.

MIT License.

---

🧭 Inspiration & Related Tools

This project was inspired in part by the work of the DEV.to community, especially:

devto-analytics-pro (by GnomeMan4201) — a solid foundation for visualizing DEV.to metrics and understanding overall activity.

devto-bot-audit (by GnomeMan4201) — a clever tool for identifying suspicious follower patterns and highlighting potential bot activity.

These tools helped shape the early direction of this tracker. However, this project goes further by focusing on historical data, automated collection, deep analytics, and a design closer to an ETL pipeline mixed with a lightweight CRM.

It aims to complement the existing ecosystem rather than replace it.

---

🚧 Status

Still in progress. Check TODO.md for the roadmap toward the Community version.