D'AhmadTolong buatkan aku aplikasi seperti miqla.app yang berfungsi sebagai berikut : Auto-Clipping: Mengubah video panjang (podcast, webinar, YouTube) menjadi beberapa video pendek vertikal secara otomatis. Fitur Utama: Virality Scoring: Memilih dan memberi skor pada momen video yang paling berpotensi viral. Auto Captions & Formatting: Menambahkan teks otomatis, menyesuaikan posisi wajah pembicara (reframing), dan memotong bagian jeda hening. AI MCP Integration: Memungkinkan pembuatan klip langsung lewat perintah instruksi (prompt) di asisten AI (seperti Claude atau ChatGPT). Dan berikan nama yang baru jangan miqla.app lagi
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Drop a YouTube, podcast or stream link. The full episode becomes the source — no timeline scrubbing required.
The moments with the highest viral potential are scored and ranked, so the strongest cuts surface first.
Captions, reframed speaker framing and silence-trimmed pacing are baked in before you export.
A long recording hides dozens of clips worth posting. auto-clipping reads the whole thing, scores each moment, and hands back the ones most likely to travel.
Every stretch of the long video is scored, then the moments with the highest viral potential surface first with an editorial score. You review a ranked shortlist instead of scrubbing the whole file.
Captions and text are set automatically. The frame follows the speaker’s face so the vertical cut never loses them, and silent pauses are cut out.
Connect ClaudeChatGPT through MCP and create clips with a prompt from inside the assistant. Prompt-made clips land in the same auto-clipping account and projects you already work in.
“…the part everyone remembers is four seconds long.”
“…the part everyone remembers is four seconds long.”
Comparison
Six capabilities, one column. auto-clipping is checked against the cutting-room approach most creators still use between uploads.
| Feature | auto-clipping | Other tools |
|---|---|---|
| Auto-clipping from a long video | Yes — several vertical clips from one upload | Manual cuts, one at a time |
| Virality scoring and ranking | Yes — moments ranked by score | Guesswork |
| Auto captions | Yes — added automatically | Typed by hand |
| Speaker reframing | Yes — framing follows the speaker | Fixed framing |
| Silent-pause removal | Yes — pauses trimmed | Left in the cut |
| AI MCP integration with Claude and ChatGPT | Yes — clips by prompt on your account | Not available |
Auto-clipping from a long video
Virality scoring and ranking
Auto captions
Speaker reframing
Silent-pause removal
AI MCP integration with Claude and ChatGPT
The ranked list did the thinking. I opened a forty-minute webinar and the clips were already ordered by the moment people would actually rewatch.
Captions land on the right word and the frame stays on the speaker’s face. The silent stretches were already gone before I touched it.
I asked for the three sharpest moments in plain language. Getting clips out of a prompt beats opening another tab and fighting a timeline.
Under the hood
A technical spread on how auto-clipping reads a long video, scores each moment and renders the clips that are worth posting — hooks in, silence out.
auto-clipping watches the full podcast, webinar or YouTube upload — not just a highlight reel — and maps every moment in the timeline before a single clip is cut.
Each moment is judged on hooks, pacing, emotion and payoff, then given a virality score. The strongest moments become ranked vertical clips, ordered by what is most likely to travel.
Captions are written automatically and the frame is reframed so the speaker stays centred. Silent pauses are trimmed out, so pacing stays tight from the first second.
Each kept clip is rendered as a vertical short, ready to download and post. Keep the clips you want, drop the rest — you only spend credits on clips that exist.
Scored on hooks, pacing, emotion and payoff, then ranked before captions, reframing and silence-trimming are applied.
Not signed in yet? We carry the link through Sign Up so it waits for you in New Project.
Drop a YouTube, podcast or stream link. The full episode becomes the source — no timeline scrubbing required.
The moments with the highest viral potential are scored and ranked, so the strongest cuts surface first.
Captions, reframed speaker framing and silence-trimmed pacing are baked in before you export.
A long recording hides dozens of clips worth posting. auto-clipping reads the whole thing, scores each moment, and hands back the ones most likely to travel.
Every stretch of the long video is scored, then the moments with the highest viral potential surface first with an editorial score. You review a ranked shortlist instead of scrubbing the whole file.
Captions and text are set automatically. The frame follows the speaker’s face so the vertical cut never loses them, and silent pauses are cut out.
Connect ClaudeChatGPT through MCP and create clips with a prompt from inside the assistant. Prompt-made clips land in the same auto-clipping account and projects you already work in.
“…the part everyone remembers is four seconds long.”
“…the part everyone remembers is four seconds long.”
Comparison
Six capabilities, one column. auto-clipping is checked against the cutting-room approach most creators still use between uploads.
| Feature | auto-clipping | Other tools |
|---|---|---|
| Auto-clipping from a long video | Yes — several vertical clips from one upload | Manual cuts, one at a time |
| Virality scoring and ranking | Yes — moments ranked by score | Guesswork |
| Auto captions | Yes — added automatically | Typed by hand |
| Speaker reframing | Yes — framing follows the speaker | Fixed framing |
| Silent-pause removal | Yes — pauses trimmed | Left in the cut |
| AI MCP integration with Claude and ChatGPT | Yes — clips by prompt on your account | Not available |
Auto-clipping from a long video
Virality scoring and ranking
Auto captions
Speaker reframing
Silent-pause removal
AI MCP integration with Claude and ChatGPT
The ranked list did the thinking. I opened a forty-minute webinar and the clips were already ordered by the moment people would actually rewatch.
Captions land on the right word and the frame stays on the speaker’s face. The silent stretches were already gone before I touched it.
I asked for the three sharpest moments in plain language. Getting clips out of a prompt beats opening another tab and fighting a timeline.
Under the hood
A technical spread on how auto-clipping reads a long video, scores each moment and renders the clips that are worth posting — hooks in, silence out.
auto-clipping watches the full podcast, webinar or YouTube upload — not just a highlight reel — and maps every moment in the timeline before a single clip is cut.
Each moment is judged on hooks, pacing, emotion and payoff, then given a virality score. The strongest moments become ranked vertical clips, ordered by what is most likely to travel.
Captions are written automatically and the frame is reframed so the speaker stays centred. Silent pauses are trimmed out, so pacing stays tight from the first second.
Each kept clip is rendered as a vertical short, ready to download and post. Keep the clips you want, drop the rest — you only spend credits on clips that exist.
Scored on hooks, pacing, emotion and payoff, then ranked before captions, reframing and silence-trimming are applied.
Not signed in yet? We carry the link through Sign Up so it waits for you in New Project.
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