DEV.to Analytics Pro

Local CLI analytics for DEV.to authors: content performance, audience signals, publishing patterns, tag intelligence, and exportable reports.

DEV.to Analytics Pro turns the authenticated DEV.to API into a terminal-native research and publishing dashboard. It is built for authors who want more than aggregate view counts: it correlates article performance with tags, publish timing, follower activity, repeat commenters, reading-list behavior, content characteristics, and cross-platform signals.

The tool can analyze:

• article views, reactions, comments, engagement rate, and growth over time
• top and underperforming articles over selectable date windows
• tag performance and missing-tag opportunities
• multi-part series performance and reader drop-off
• day/hour publishing performance
• follower growth around publication events
• repeat commenters and follower/commenter overlap
• commenter profile enrichment through DEV.to profile data
• article-body characteristics, vocabulary, tooling mentions, and link density
• reading-list and followed-tag behavior
• competitive activity in selected DEV.to tags
• JSON and CSV exports for downstream analysis

No hosted dashboard or database is required. Analysis runs from the local Python process and output stays on the machine unless you explicitly export or share it.

git clone https://github.com/GnomeMan4201/devto-analytics-pro.git
cd devto-analytics-pro
python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
pip install -r requirements.txt

python dev.py --help
pip install pytest
python -m pytest -q

The current suite primarily validates importability and basic project structure. API-backed analytics depend on live DEV.to responses and a valid API key and should be treated separately from the offline smoke tests.

Create an API key in your DEV.to account settings and export it into the current shell:

export DEVTO_API_KEY='your-key-here'

Use the environment-backed entrypoint for normal operation:

python dev_env.py --overview
python dev_env.py --full-report

dev_env.py injects the credential into the existing CLI in memory, so the API key does not appear in the operating-system process argument vector. The original dev.py --api-key ... interface remains available for compatibility, but the environment-backed entrypoint is preferred.

Do not commit API keys, exported private account data, or shell history containing credentials.

python dev_env.py --overview

python dev_env.py --top 20 --sort engagement

python dev_env.py --tags --days 90

python dev_env.py --follower-correlation
python dev_env.py --commenters
python dev_env.py --loyal-readers
python dev_env.py --commenter-enrichment --enrich-top 15

python dev_env.py --publish-heatmap
python dev_env.py --series
python dev_env.py --content-analysis
python dev_env.py --tag-fix
python dev_env.py --insights

python dev_env.py --full-report

--full-report intentionally performs many API-backed analyses and can take longer than a single focused command.

python dev_env.py --export-json analytics.json
python dev_env.py --export-csv analytics.csv
python dev_env.py --days 90 --export-csv last-90-days.csv

The underlying argument reference remains authoritative:

python dev.py --help

Major analysis flags include:

• Python 3
• a DEV.to account
• a DEV.to API key for authenticated analytics
• network access when running API-backed commands

The runtime Python dependency surface is intentionally minimal and defined in requirements.txt:

• requests

pytest is only needed to run the repository test suite. No plotting or numerical-computing package is required by the current implementation.

The tool requests account and content data from DEV.to only when a command needs it. Some analyses may retrieve follower, commenter, reading-list, profile, or article-body information. Treat exported JSON/CSV as potentially sensitive research data even when much of the underlying DEV.to activity is public.

Recommended practice:

• keep the API key in DEVTO_API_KEY and use dev_env.py
• keep exports out of version control
• review generated datasets before sharing
• use the smallest analysis command that answers the question when you do not need the full report

There are two different validation layers:

1. Offline smoke validation — importing the module, checking syntax/project structure, exercising the CLI help path, and verifying the environment-backed entrypoint fails closed when no key is present.
2. Live integration behavior — commands that depend on DEV.to API availability, authentication, pagination, and the shape of returned account data.

A green local smoke test confirms that the repository is runnable; it does not prove that every external DEV.to endpoint is currently available or unchanged.

devto-analytics-pro/
├── dev.py                  # CLI and analytics engine
├── dev_env.py              # preferred environment-backed credential entrypoint
├── requirements.txt        # runtime dependencies
├── tests/                  # pytest smoke/basic tests
├── .github/workflows/      # CI automation
└── assets/                 # repository artwork

This project is for first-party analytics, publishing research, and audience analysis around accounts and content you are authorized to access. It is particularly useful for researchers and technical writers who want reproducible, local analysis instead of relying exclusively on platform dashboards.

GnomeMan4201 — independent security researcher / badBANANA research.