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, skip cropping |
If the user gave only a video URL, run with defaults — don't block on questions.
Step 2 — Verify Prerequisites
muapi-cliinstalled and authed (muapi auth configure)MUAPI_API_KEYavailable (env var ormuapi auth statuspasses)
That's it. No ffmpeg, no Python, no Whisper install, no LLM keys. Everything runs server-side.
Step 3 — Run the Skill
bash library/edit/ai-clipping/scripts/run-ai-clipping.sh \
--video "https://example.com/podcast.mp4" \
--num-clips 5 \
--aspect-ratio 9:16 \
--view
The script:
- Resolves
--videoto a hosted URL (uploads local files viamuapi upload fileif needed). - Calls
muapi edit clippingwith the supported parameters. - Polls until the job is done (or returns the
request_idimmediately under--async). - Prints a ranked summary and, if
--output-jsonis set, writes the full result.
What Happens Server-Side
The /ai-clipping endpoint internally runs the full pipeline so the agent doesn't have to:
- Transcribe with Whisper.
- Classify content type (podcast / interview / tutorial / vlog / lecture / monologue).
- Rank highlights through the virality framework:
- 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
- Dedupe overlapping candidates by score.
- Top-N select and face-track auto-crop to the requested aspect ratio.
This is why the skill is small: the heavy lifting is on the API.
Quick Invocation Patterns
Defaults — three 9:16 clips:
bash run-ai-clipping.sh --video "https://example.com/long.mp4"
Podcast — more clips, view in player:
bash run-ai-clipping.sh --video "<URL>" --num-clips 8 --view
Square clips for Instagram feed:
bash run-ai-clipping.sh --video "<URL>" --aspect-ratio 1:1 --num-clips 3
Just the timestamps (build your own renderer):
bash run-ai-clipping.sh --video "<URL>" --coords-only --output-json result.json
Async submit (returns request_id, poll later):
REQUEST_ID=$(bash run-ai-clipping.sh --video "<URL>" --async --output-json - | jq -r '.request_id')
muapi predict wait "$REQUEST_ID" --download ./outputs
Local file:
bash run-ai-clipping.sh --video ./recording.mp4 --num-clips 5 --view
Batch — urls.txt with one URL per line:
xargs -a urls.txt -I{} bash run-ai-clipping.sh --video "{}"
Aspect Ratio Picker
| Platform | Ratio | Sweet-spot duration |
|---|---|---|
| TikTok / Reels / YouTube Shorts | 9:16 |
30–75s |
| Instagram Feed | 1:1 |
15–45s |
| Pinterest / portrait | 4:5 |
30–60s |
Default to 9:16 unless the platform is specified.
Output Schema
{
"source_video_url": "...",
"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 --coords-only is set, each entry has start_time/end_time but no clip_url — render locally with ffmpeg.
When reporting back to the user, surface for each clip: rank, score, time range, title, hook, and clip URL.
Common Mistakes to Avoid
- Wrong aspect ratio for the platform — Shorts / TikTok / Reels are
9:16. Default to that. - Padding to hit
num_clips— if the API returns fewer survivors than requested, return what you have. Don't pretend. - Re-running on a 404'd clip URL — the same
request_idcan be re-fetched withmuapi predict wait <id>rather than re-clipping. - Trying to tune Whisper / chunk size / LLM prompts — those knobs aren't exposed; the endpoint handles them.
Failure Modes
- API key missing or rejected — surface the exact error; never fabricate a key.
- Job timed out — bump poll timeout (
--poll-timeout) and retry. - Source URL not reachable from the backend — upload locally with
muapi upload file <path>first, then pass the returned URL. - Fewer clips returned than requested — the source had fewer rankable highlights. Return what came back with a note.
Done Criteria
The skill is done when:
result.shortshas up tonum_clipsentries, each with a workingclip_url(orstart_time/end_timeunder--coords-only).- The user has been shown the ranked list (score, time range, title, hook, URL).
- If
--output-jsonwas set, the file exists and parses.