Auto Clipper

---
name: auto-clipper
description: Use when automatically clip long videos into short, engaging highlights
  for TikTok, Reels, and YouTube Shorts using FFmpeg and AI scene detection.
domain: content
author: oyi77
license: Apache-2.0
subdomain: content-creation
tags:
- auto
- clipper
- video
version: 1.0.0
category: content
---

description: Convert long videos into viral Shorts, TikToks, and Reels automatically. AI-powered scene detection, highlight extraction, and smart clipping. Use when repurposing long-form content into short-form, clipping highlights, or creating shorts from existing videos.
domain: content
tags:
- auto
- clipper
- content-creation
- digital-content
- media
- video
dependencies: "- faster-whisper\n  - textblob\n  - vadersentiment\n  - moviepy\n  - opencv-python\n  - ffmpeg-python\n  -\
  \ customtkinter\n"
---
# Auto Clipper

## When to Use

**Trigger phrases:**
- "clip this video" · "make shorts from this" · "turn this into TikTok"
- "auto clip" · "extract highlights" · "repurpose long video"
- "create Reels from" · "viral clips from" · "short-form from long-form"

**Use cases:**
- Convert a podcast/webinar into multiple short clips
- Extract highlights from long YouTube videos
- Create TikTok/Reels from existing content
- Auto-detect engaging moments via sentiment analysis
- Batch process multiple long videos into shorts

**When NOT to use:**
- For tasks outside this skill's scope


**Production-ready** AI-powered video clipper untuk content creator Indonesia.


## When NOT to Use

- When the content requires deep domain expertise you do not have
- For legal, medical, or financial advice content
- When real-time data is required (use live data feeds)


## Overview

Auto Clipper enables content production with professional quality and consistency.

## Workflow

```python
# Example: Content generation pipeline
def generate_content(topic: str, format: str = "article"):
    outline = create_outline(topic)
    draft = write_draft(outline, format)
    edited = edit_for_quality(draft)
    optimized = optimize_for_seo(edited)
    return publish(optimized)
```

1. **Define brief** — Set objectives, audience, and style guidelines
2. **Research and gather** — Collect source material and reference content
3. **Create draft** — Generate initial content following the brief
4. **Refine and edit** — Polish for quality, accuracy, and engagement
5. **Publish and distribute** — Deploy to target platforms
6. **Track performance** — Monitor engagement and iterate

## Quality Checklist

- [ ] Content matches the defined brief and audience
- [ ] All facts verified against authoritative sources
- [ ] Formatting consistent with style guidelines
- [ ] SEO/distribution optimization applied
- [ ] Call-to-action clear and compelling

## Tools

- Content management system for publishing
- Analytics platform for performance tracking
- Design tools for visual assets
- Collaboration tools for review cycles

## Anti-Rationalization Table

| Rationalization | Reality |
|---|---|
| "Good enough content works" | Quality content drives engagement. Mediocre content gets ignored. |
| "I will optimize later" | SEO and distribution need optimization from the start. |
| "Templates are good enough" | Templates are a starting point. Custom content outperforms generic. |


## Process

1. **Prepare** — Gather requirements, verify prerequisites, set up environment
1. **Execute** — Run auto clipper workflow with configured parameters
1. **Verify** — Validate output meets requirements, document results

## Verification

- [ ] Content meets quality standards and brief requirements
- [ ] Output is properly formatted for target platform
- [ ] All facts and references verified
- [ ] SEO/distribution optimization applied where applicable