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tonhowtf/omniget
Udemy & Hotmart course downloader, YouTube downloader (yt-dlp GUI, 1,800+ sites) + desktop app for AI agents: Claude Code, Codex, Gemini CLI, Ollama. Permissions, undo, jobs, loops until tests pass, MCP server, 156 tools, course player. Free and open source for Windows, macOS and Linux. No terminal. Your files stay on your computer.
Current momentum
Stars
14.3K
+149 since sync
Commits
80
in period
Active contributors
6
+0%
Issues closed
17
260.9h avg. resolution
Recorded trajectory
Movement you can verify over time.
Loading daily momentum history.
Behind the number
A score you can inspect.
Each signal contributes points to the total. These are capped GitHub samples, not complete activity counts or proof of adoption.
Development activity40.0 / 40 pts
80 commits · 13 opened PRs · 17 closed issues in 7 days
24 + 22 × ln(1 + weekly weighted activity). Activity = commits + 2 × opened PRs + closed issues. Signal bounded to 0–100, then weighted 40%.
Contributor breadth18.2 / 25 pts
6 sampled contributors in 7 days · 0% contributor-growth input
24 + 25 × ln(1 + contributors × √(7 / days)) + 0.15 × growth. Growth uses the 30-day windows; capped samples use a neutral 0 input. Signal bounded to 0–100, then weighted 25%.
Issue resolution6.5 / 20 pts
260.9 hours resolution input
105 − 9 × log₂(1 + hours). Uses sampled closed issues, not first-response time. With no resolved issues, the model uses 168 hours. Signal bounded to 0–100, then weighted 20%.
Release cadence11.3 / 15 pts
6 days cadence input
105 − 5 × cadence days. With fewer than two published releases, the model uses time since the last push as a proxy. Signal bounded to 0–100, then weighted 15%.
What changed in the recorded sample?
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1.3K forks
Downstream adoption
2 open issues
Community demand
6 day cadence
Shipping consistency