--- title: "youtube-shorts-generator by anil-matcha · skilld" canonical_url: "https://skilld.dev/gh/anil-matcha/ai-youtube-shorts-generator" meta: description: "Generate viral 9:16 YouTube Shorts (or TikTok/Reels clips) from a long-form YouTube URL or local video. Triggers on requests like \"make shorts from this… From anil-matcha/ai-youtube-shorts-generator." "og:description": "Generate viral 9:16 YouTube Shorts (or TikTok/Reels clips) from a long-form YouTube URL or local video. Triggers on requests like \"make shorts from this… From anil-matcha/ai-youtube-shorts-generator." "og:title": "youtube-shorts-generator by anil-matcha" "twitter:description": "Generate viral 9:16 YouTube Shorts (or TikTok/Reels clips) from a long-form YouTube URL or local video. Triggers on requests like \"make shorts from this… From anil-matcha/ai-youtube-shorts-generator." "twitter:title": "youtube-shorts-generator by anil-matcha" --- ` [All skills](https://skilld.dev/skills) [![anil-matcha avatar](https://skilld.dev/_img/avatar?url=https%3A%2F%2Fgithub.com%2Fanil-matcha.png%3Fsize%3D96)](https://skilld.dev/gh/anil-matcha) # **/youtube-shorts-generator** [@57e261e](https://github.com/Anil-matcha/AI-Youtube-Shorts-Generator/commit/57e261e6d44e91f8d3ad5172e8f39d950a8426e4 "Your agent reads SKILL.md at commit 57e261e") by [Anil Chandra Naidu Matcha](https://skilld.dev/gh/anil-matcha)· [anil-matcha](https://skilld.dev/gh/anil-matcha)/ [ai-youtube-shorts-generator](https://skilld.dev/gh/anil-matcha/ai-youtube-shorts-generator)·5.2k stars 948 Generate viral 9:16 YouTube Shorts (or TikTok/Reels clips) from a long-form YouTube URL or local video. Triggers on requests like "make shorts from this video", "extract viral clips from this YouTube link", "auto-clip this podcast", "find the best moments and crop vertical". Pipeline downloads the source, transcribes via MuAPI /openai-whisper, ranks highlights through a virality framework (hook / emotional peak / opinion bomb / revelation / conflict / quotable / story peak / practical value), dedupes overlapping candidates, and vertically auto-crops the top N as mp4s. - 1 file - 7.1 KB - Updated 5 months ago - [GitHub](https://github.com/Anil-matcha/AI-Youtube-Shorts-Generator/blob/main/.claude/skills/youtube-shorts-generator/SKILL.md "View SKILL.md on GitHub") - [3 warnings](#third-party-checks "Third-party checks: 3 warnings · 3 checks · Risk MEDIUM") ## SKILL.md 7.1 KB **≈150** tokens always: the name and description. **≈1.7k** when used: this file. ## YouTube Shorts Generator End-to-end pipeline that turns one long video into N viral-ready vertical clips. Each clip ships with a viral score (0–100), an opening hook line, and a one-sentence reason it should perform. Reference implementation: https://github.com/SamurAIGPT/AI-Youtube-Shorts-Generator ### When to use this skill - "Generate shorts from this YouTube video" - "Find the most viral 60-second clips in this podcast" - "Auto-crop this interview to 9:16" - "Give me TikTok clips from this lecture" If the user only wants transcription, summarization, or thumbnails — this is the wrong skill. ### Inputs to collect before running Ask once, then proceed: 1. **Source** — YouTube URL (preferred) or path/URL to an mp4 2. **`num_clips`** — default 3 3. **`aspect_ratio`** — default `9:16` (also: `1:1`, `4:5`) 4. **`language`** — default auto-detect (forwarded to MuAPI Whisper as ISO-639-1) 5. **Output JSON path** — optional; if set, dump full result there If the user gave a URL and nothing else, use defaults and don't block on questions. ### Prerequisites (verify before first run) - Python 3.10+ - A MuAPI key — set `MUAPI_API_KEY` in `.env`. Powers download, transcription, highlight ranking, and clipping. If missing, stop and ask the user for it; do not invent one. - `pip install -r requirements.txt` inside a venv If the repo isn't cloned yet, clone `https://github.com/SamurAIGPT/AI-Youtube-Shorts-Generator.git` into the working directory. ### Pipeline (what to execute) Run the eight stages in order. Each maps to a module in `shorts_generator/`. 1. **Download** ( `downloader.py`) — pull the source video at the requested resolution ( `360`/ `480`/ `720`/ `1080`, default `720`). 2. **Transcribe** ( `transcriber.py`) — MuAPI `/openai-whisper` runs Whisper server-side and returns timestamped `verbose_json` segments. Billed per minute of audio. 3. **Classify content type** — LLM tags the video (podcast / interview / tutorial / vlog / lecture / monologue) and density. Tune the highlight prompt per type. 4. **Chunk if long** ( `highlights.py`) — videos > `LONG_VIDEO_THRESHOLD` (1800s default) are split into `CHUNK_SIZE_SECONDS` (1200s default) windows with `CHUNK_OVERLAP_SECONDS` (60s default) overlap so cross-boundary highlights aren't missed. 5. **Rank highlights** — LLM scans each chunk through `VIRALITY_CRITERIA`: - **Hook moments** — strong opening line that stops the scroll - **Emotional peaks** — laughter, anger, vulnerability, awe - **Opinion bombs** — spicy, contrarian, debate-bait takes - **Revelation moments** — "wait, what?" reframes - **Conflict** — disagreement, tension, callouts - **Quotable lines** — tight, screenshot-worthy phrasing - **Story peaks** — climax of a narrative arc - **Practical value** — actionable insight a viewer will save Each candidate gets `start_time`, `end_time`, `score` 0–100, `title`, `hook_sentence`, `virality_reason`. Aim for 30–75s clips unless content dictates otherwise. 6. **Dedupe** — collapse overlaps. Rule: if two candidates overlap > 50%, keep the higher score, drop the other. 7. **Top-N selection** — sort surviving candidates by score, take `num_clips`. 8. **Vertical auto-crop** ( `clipper.py`) — render each highlight at `aspect_ratio`. Auto-handles face tracking and screen recordings; no Haar cascades. ### Invocation CLI (the standard path): ``` python main.py "" \ --num-clips 5 \ --aspect-ratio 9:16 \ --output-json result.json ``` Python API (when embedding in another pipeline): ``` from shorts_generator import generate_shorts result = generate_shorts( "", num_clips=5, aspect_ratio="9:16", ) for short in result["shorts"]: print(short["score"], short["title"], short["clip_url"]) ``` Batch mode — `urls.txt` with one URL per line: ``` xargs -a urls.txt -I{} python main.py "{}" ``` ### CLI flags reference | Flag | Default | Notes | | --- | --- | --- | | `--num-clips` | `3` | How many shorts to render | | `--aspect-ratio` | `9:16` | `9:16` for TikTok/Reels, `1:1` square, anything else by flag | | `--format` | `720` | Source download resolution | | `--language` | auto | Whisper language code (e.g. `en`) | | `--output-json` | — | Dump full result (transcript + all candidates + clip URLs) | ### Output schema ``` { "source_video_url": "...", "transcript": { "duration": 1873.4, "segments": [...] }, "highlights": [ /* every candidate, before top-N cut */ ], "shorts": [ { "title": "The one mistake that cost me $50K", "start_time": 124.3, "end_time": 187.6, "score": 92, "hook_sentence": "Nobody talks about this, but it killed my first startup...", "virality_reason": "Opens with a number + regret, peaks on a contrarian lesson", "clip_url": "https://.../short_1.mp4" } ] } ``` When reporting back to the user, surface for each clip: rank, score, time range, title, hook, and clip URL. Skip the raw transcript unless asked. ### Tunable knobs - `shorts_generator/highlights.py` - `VIRALITY_CRITERIA` — reorder or extend signals - `HIGHLIGHT_SYSTEM_PROMPT` — duration sweet spot, hook rules, JSON schema - `CHUNK_SIZE_SECONDS` — 1200s default - `LONG_VIDEO_THRESHOLD` — 1800s default - `CHUNK_OVERLAP_SECONDS` — 60s default - `shorts_generator/config.py` (or env vars) - `MUAPI_POLL_INTERVAL` — 5s - `MUAPI_POLL_TIMEOUT` — 1800s ### Whisper transcription Audio is transcribed by MuAPI's `/openai-whisper` endpoint (server-side `whisper-1`, billed per minute). The CLI passes `--language` straight through; leave it empty for auto-detection, or pass an ISO-639-1 code (e.g. `en`) to lock it. ### Failure modes — handle, don't paper over - **Whisper produced no segments** — likely no detectable speech or a hard language. Retry with `--language ` (correct ISO-639-1) before declaring failure. - **API key missing or rejected** — surface the exact error; never fabricate a key. - **Job timed out** — bump `MUAPI_POLL_TIMEOUT` and retry; don't silently truncate. - **Highlight ranker returned < `num_clips`** — return what survived dedupe with a note; don't pad with low-score filler. ### Done criteria The skill is done when: 1. `result["shorts"]` has up to `num_clips` entries, each with a working `clip_url`. 2. The user has been shown the ranked list (score, time range, title, hook, URL). 3. If `--output-json` was set, the file exists and parses. If any clip URL 404s on a HEAD check, re-run just the crop stage for that highlight rather than re-running the whole pipeline. Source: [SKILL.md on GitHub](https://github.com/Anil-matcha/AI-Youtube-Shorts-Generator/blob/main/.claude/skills/youtube-shorts-generator/SKILL.md) ## Third-party checks
3 warnings1mo3 checks · Risk MEDIUM - Gen Agent Trust Hub1mo The skill downloads and executes code from an external third-party repository and processes untrusted video transcripts, which presents a risk of indirect prompt injection. - Socket1mo 1 alert: gptAnomaly - Snyk1mo Risk: MEDIUM · 2 issues
## Provenance [Signed by skilld at 57e261e.](https://github.com/Anil-matcha/AI-Youtube-Shorts-Generator/commit/57e261e6d44e91f8d3ad5172e8f39d950a8426e4 "57e261e6d44e91f8d3ad5172e8f39d950a8426e4") This ties the file your Agent reads to that commit on GitHub. It does not review the instructions. Last checked against GitHub yesterday. Activeupdated 5 months ago ## README badge ![README badge for anil-matcha/ai-youtube-shorts-generator](https://skilld.dev/b/anil-matcha/ai-youtube-shorts-generator?theme=light&label=0) ## Related skills - - - - - -