muapi-ai-clipping
Turn a long video into N viral-ready short clips with a single managed API call. Wraps muapi.ai's `/ai-clipping` endpoint, which handles transcription, highlight ranking through a virality framework (hook / emotional peak / opinion bomb / revelation / conflict / quotable / story peak / practical value), overlap dedupe, and vertical face-tracking auto-crop server-side. No local Whisper, no local LLM, no GPU.
npx skills add SamurAIGPT/Generative-Media-Skills --skill ai-clipping --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# AI Clipping **One API call: long video in → ranked vertical short clips out.** Each clip ships with a viral score (0–100), an opening hook line, a one-sentence "why it works" reason, and a hosted mp4 URL. Underlying API: https://muapi.ai/playground/ai-clipping Reference implementation (open source): https://github.com/SamurAIGPT/AI-Youtube-Shorts-Generator --- ## When to Use - Auto-clip a podcast, interview, lecture, vlog, or stream into TikTok / Reels / Shorts. - Extract the best 30–75s moments from any hosted video URL. - Get face-tracked vertical (9:16), square (1:1), or portrait (4:5) crops without running ffmpeg locally. If you only need raw timestamps for your own renderer, set `--coords-only` to skip cropping and just get the highlight ranges. --- ## Agent Execution Protocol ### Step 1 — Collect Inputs | Input | Required | Default | Notes | |:---|:---|:---|:---| | `--video` | yes | — | Hosted mp4 URL, or local file path (auto-uploaded), or YouTube URL (if backend supports it) | | `--num-clips` | no | `3` | Number of highlights to extract | | `--aspect-ratio` | no | `9:16` | `9:16` \| `1:1` \| `4:5` | | `--coords-only` | no | off | Return just the highlight time ranges, ski
- When to Use
- Agent Execution Protocol
- Step 1 — Collect Inputs
- Step 2 — Verify Prerequisites
- Step 3 — Run the Skill
- What Happens Server-Side
- Quick Invocation Patterns
- Aspect Ratio Picker
- Output Schema
- Common Mistakes to Avoid
- Failure Modes
- Done Criteria
bash library/edit/ai-clipping/scripts/run-ai-clipping.sh \
bash run-ai-clipping.sh --video "https://example.com/long.mp4"
bash run-ai-clipping.sh --video "<URL>" --num-clips 8 --view
bash run-ai-clipping.sh --video "<URL>" --aspect-ratio 1:1 --num-clips 3
bash run-ai-clipping.sh --video "<URL>" --coords-only --output-json result.json
muapi predict wait "$REQUEST_ID" --download ./outputs
bash run-ai-clipping.sh --video ./recording.mp4 --num-clips 5 --view
xargs -a urls.txt -I{} bash run-ai-clipping.sh --video "{}"What does the muapi-ai-clipping skill do?
Turn a long video into N viral-ready short clips with a single managed API call. Wraps muapi.ai's `/ai-clipping` endpoint, which handles transcription, highlight ranking through a virality framework (hook / emotional peak / opinion bomb / revelation / conflict / quotable / story peak / practical value), overlap dedupe, and vertical face-tracking auto-crop server-side. No local Whisper, no local LLM, no GPU.
How do I install it?
Run `npx skills add SamurAIGPT/Generative-Media-Skills --skill ai-clipping --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From SamurAIGPT/Generative-Media-Skills, a repository with 3,976 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.