VidPipe: I Built an Agentic Video Editor with GitHub Copilot CLI

Source: Dev.to

This is a submission for the GitHub Copilot CLI Challenge

What I Built

VidPipe is an open‑source agentic video editor that automates the entire post‑production workflow. Drop a video file and a 15‑stage AI pipeline transforms it into:

• Short clips (15–60 s) with 6 variants per short (landscape, portrait, square, feed — all with burned‑in captions)
• Medium clips (1–3 min) with cross‑fade transitions
• Karaoke‑style captions (word‑by‑word highlighting, burned into video)
• AI silence removal (context‑aware, capped at 20 %)
• Chapter detection (JSON, Markdown, YouTube timestamps, FFmpeg metadata)
• Platform‑tailored social posts for TikTok, YouTube, Instagram, LinkedIn, and X
• Blog post with web‑sourced links
• Cost tracking across all LLM providers

The pipeline is orchestrated by 8 specialized AI agents built on the @github/copilot-sdk:

ShortsAgent, SummaryAgent, ProducerAgent, ChapterAgent, SocialMediaAgent, BlogAgent, MediumVideoAgent, SilenceRemovalAgent.
Each agent uses structured tool calls to make intelligent, contextual decisions about your content.

Why I Built This

I’m a full‑time employee, a dad (twins on the way — soon three kids), and an aspiring tech creator. I love talking about technology but I hate editing. Recording is the fun part; everything after is time I don’t have. VidPipe lets me record once and publish everywhere.

GitHub Repository | npm package | Documentation

Demo

Watch the full demo video →

The video walks through VidPipe processing a recording end‑to‑end — from ingestion through shorts generation, caption burning, and the Tinder‑style review interface for approving social‑media posts.

The Pipeline in Action

Ingest → Transcribe → Silence Removal → Captions → Caption Burn
    → Shorts → Medium Clips → Chapters → Summary
    → Social Posts → Short Posts → Medium Posts
    → Blog → Queue Build → Git Push

Output Structure

Every processed video gets its own organized folder:

recordings/my-demo/
  my-demo-captioned.mp4          # With burned‑in captions
  transcript.json                # Word‑level timestamps
  README.md                     # AI‑generated summary
  shorts/
    catchy-title-portrait.mp4    # 9:16 for TikTok/Reels
    catchy-title-square.mp4      # 1:1 for Instagram
    catchy-title/posts/          # Per‑short social posts
  chapters/
    chapters-youtube.txt         # Ready to paste into YouTube
  social-posts/
    tiktok.md, youtube.md, instagram.md, linkedin.md, x.md
    devto.md                     # Full blog post

Review UI

VidPipe includes a built‑in web app where you review and approve content before it gets scheduled:

My Experience with GitHub Copilot CLI

GitHub Copilot CLI wasn’t just a tool I used to write this project — it became an integral part of the development workflow. Here’s how:

1. Custom Copilot Hooks — Enforcing Quality at Every Step

I built 5 custom Copilot hooks in .github/hooks/ that run automatically during development:

These hooks keep Copilot CLI within strict guardrails — no shortcuts, no skipped tests, no bypassed reviews.

2. Custom Agents — Specialized AI Workers

I created 3 custom Copilot agents in .github/agents/:

3. Custom Skills — Reusable Workflows

3 custom skills live in .github/skills/:

4. The 37 KB copilot-instructions.md

My .github/copilot-instructions.md is a 37 KB file containing the full project context — every pipeline stage, every agent behavior, every FFmpeg pattern, every testing convention. This gives Copilot CLI a deep understanding of VidPipe’s architecture whenever it works on the codebase.

knows that the adjusted transcript goes to captions while the original transcript goes to shorts. It knows that `execFile` (not `exec`) is required for FFmpeg. It knows the provider abstraction layer and cost tracking patterns.

5. The Push Workflow — CI/CD Powered by Copilot

The npm run push command (which Copilot hooks enforce) runs this entire pipeline:

typecheck → tests (51 test files) → coverage check → build → git push
    → poll CodeQL alerts → poll Copilot PR review
    → auto-dispatch security‑fixer agent for CodeQL alerts
    → auto-dispatch review‑triage agent for review comments

If anything fails, Copilot CLI fixes it and re‑runs. The cycle continues until all gates pass.

How Copilot CLI Impacted My Development

The numbers tell the story:

• 131 TypeScript files, ~12,000 lines of source code
• 51 test files, ~10,500 lines of tests
• 8 AI agents built on @github/copilot-sdk
• 15‑stage pipeline processing videos end‑to‑end
• Published on npm as a global CLI tool

I built this entire project in 3 weeks — as a side project, after work, while parenting. That would have been impossible without Copilot CLI. It wrote tests, fixed security alerts, triaged its own code reviews, and kept the codebase consistent through custom hooks and instructions.

The most powerful aspect? Copilot CLI doesn’t just help you write code — it becomes part of your engineering system. The hooks enforce standards. The agents handle reviews. The skills automate releases. It’s not an assistant sitting next to you — it’s a teammate embedded in your workflow.

Built with love by a busy dad who’d rather record than edit. If you’re a creator struggling with the post‑production grind, give VidPipe a try.