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-cli installed and authed (muapi auth configure)
MUAPI_API_KEY available (env var or muapi auth status passes)

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 --video to a hosted URL (uploads local files via muapi upload file if needed).
Calls muapi edit clipping with the supported parameters.
Polls until the job is done (or returns the request_id immediately under --async).
Prints a ranked summary and, if --output-json is 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_id can be re-fetched with muapi 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.shorts has up to num_clips entries, each with a working clip_url (or start_time/end_time under --coords-only).
The user has been shown the ranked list (score, time range, title, hook, URL).
If --output-json was set, the file exists and parses.

muapi-ai-clipping