muapi-ai-clipping

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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.

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Sourcesamuraigpt/generative-media-skills
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NPX Install

npx skill4agent add samuraigpt/generative-media-skills muapi-ai-clipping

Tags

Translated version includes tags in frontmatter
ai-clippingviral-shortsapi-integrationvideo-processingmuapi-ai

SKILL.md Content

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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

InputRequiredDefaultNotes
--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
nooffReturn 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
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:
  1. Resolves 
    --video
    to a hosted URL (uploads local files via 
    muapi upload file
    if needed).
  2. Calls 
    muapi edit clipping
    with the supported parameters.
  3. Polls until the job is done (or returns the 
    request_id
    immediately under 
    --async
    ).
  4. 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
bash run-ai-clipping.sh --video "https://example.com/long.mp4"
Podcast — more clips, view in player:
bash
bash run-ai-clipping.sh --video "<URL>" --num-clips 8 --view
Square clips for Instagram feed:
bash
bash run-ai-clipping.sh --video "<URL>" --aspect-ratio 1:1 --num-clips 3
Just the timestamps (build your own renderer):
bash
bash run-ai-clipping.sh --video "<URL>" --coords-only --output-json result.json
Async submit (returns request_id, poll later):
bash
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
bash run-ai-clipping.sh --video ./recording.mp4 --num-clips 5 --view
Batch — 
urls.txt
with one URL per line:
bash
xargs -a urls.txt -I{} bash run-ai-clipping.sh --video "{}"

Aspect Ratio Picker

PlatformRatioSweet-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

json
{
  "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

  1. Wrong aspect ratio for the platform — Shorts / TikTok / Reels are 
    9:16
    . Default to that.
  2. Padding to hit 
    num_clips
     — if the API returns fewer survivors than requested, return what you have. Don't pretend.
  3. 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.
  4. 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:
  1. result.shorts
    has up to 
    num_clips
    entries, each with a working 
    clip_url
    (or 
    start_time
    /
    end_time
    under 
    --coords-only
    ).
  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.