ayush-that

ayush-that / jiang-clips

Public

Automated pipeline that converts long-form YouTube videos into viral-ready vertical clips

content-automation ffmpeg reels remotion shorts
151
37
100% credibility
Found Mar 19, 2026 at 151 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

An automated tool that turns long YouTube videos into engaging short-form clips by identifying key moments with AI, transcribing speech, removing pauses, adding animated captions, and composing split-screen reels.

How It Works

1
📺 Spot a great video

You find a long YouTube video packed with interesting stories or facts that would make awesome short clips for social media.

2
🛠️ Get ready to go

You grab the easy starter tools and make sure video editing helpers are installed on your computer.

3
🤖 Link up smart AI

You connect a helpful AI service so it can spot the best moments and add clever captions.

4
Pick your style
🎥
One video

Paste a single video link to quickly get a handful of polished shorts.

📺
Whole channel

Give a channel link to automatically farm clips from the latest videos.

5
✨ Hit start and relax

You launch the process and it automatically finds viral moments, trims silence, speeds things up, adds animated captions, and builds eye-catching reels.

6
⏳ Peek at progress

Check the status anytime to see how your clips are coming along, or pick up right where it left off if needed.

🎉 Reels ready to share

Open the output folder to find your new short videos, complete with hooks, captions, and fun effects – perfect for posting anywhere.

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AI-Generated Review

What is jiang-clips?

jiang-clips is an automated pipeline that grabs long YouTube videos, slices them into viral-ready vertical clips for TikTok or Reels, and spits out polished 1080x1920 MP4s. It handles download, transcription via YouTube or Whisper, AI-powered segment detection with Google Gemini, silence trimming, animated captions, and optional split-screen compositing with background footage. Built in TypeScript on Bun, it runs via simple CLI commands like `bun run src/index.ts pipeline ` or `batch ` for github automated pipelines churning through channels.

Why is it gaining traction?

It stands out with resumable processing via checkpoints, parallel clip generation up to 10 at once, and smart Gemini prompts tuned for educational/history content that nail hook-heavy 30-90s segments. Developers dig the zero-config defaults, status checks, and cleanup tools—no babysitting long runs. Compared to manual editors or basic rippers, this delivers production-ready reels with captions and speedups in one automated github release package managers-friendly flow.

Who should use this?

YouTube creators repurposing hour-long lectures into shorts, podcasters automating clip farms for LinkedIn or TikTok, or social media teams batch-processing channels without Premiere Pro drudgery. Ideal for anyone running automated pipelines ci cd-style on video content, skipping tedious trimming.

Verdict

Grab it if you need a battle-tested automated pipeline for jiang cheng clips-style workflows—151 stars and solid docs/tests make it reliable for side projects, despite the 1.0% credibility score signaling early maturity. Scale with your own Gemini key, but monitor costs on big batches.

(198 words)

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