/alibabacloud-video-editor

name: alibabacloud-video-editor
description: >
  Edit videos with Alibaba Cloud ICE using URL or OSS inputs and cloud-rendered outputs. Use for Timeline editing, normal-template creation and rendering, multi-clip composition, titles, subtitles, transitions, audio mixing, translation or localization, and single-media intelligent-production jobs such as smart covers, erasure, caption extraction, matting, beauty, reframing, and audio processing. Also inspect templates and verify output URLs. Content analysis such as labels, highlight candidates, speaker attribution, and transcripts must be supplied upstream. Never use local ffmpeg to generate, convert, or extract media; it is limited to streamed audio analysis.

Video Editor Skill

Edit video in the cloud (Alibaba Cloud ICE) — no local ffmpeg. Three modes:

• **Timeline editing** (`SubmitMediaProducingJob`) — assemble several clips: you write a Timeline JSON, the script submits a producing job, polls it, and returns the output video URL.
• **Normal templates** (`AddTemplate`, `Type=Timeline`) — store a parameterized Timeline Config, inspect its `ClipsParam` contract, and repeatedly render it with replacement text/media → `references/22-normal-templates.md`.
• **Intelligent production** (`SubmitIProductionJob`) — run **one algorithm over one media file** (smart cover, logo/subtitle erasure, caption extraction, matting, beauty, H→V, audio denoise/mixing/demix/analysis). No Timeline involved → §7.1 and `references/13-intelligent-production.md`.

**This skill is the last link of the chain.** Content understanding — what happens where, who speaks, which moments matter — is produced upstream by other capabilities and handed to this skill as data (§2.4). Everything here turns that data plus the material into a rendered, verified video.

Hard Rules

Six rules where deviation is not a taste call. Everything else in this document — every font size, colour, duration, preset and length ratio — is a *worked example* from a job that shipped, not a mandate. Read those for what is possible, then make your own call from the material and the user's ask.

1. **Nothing lands on the user's local storage.** Media is never downloaded — read it in place (`ffmpeg -i "<https URL>"` streams over HTTP; a snapshot job returns signed frame URLs). Intermediates live in OSS: an algorithm's output, a dub take, a proxy, a converted input — all of them are OSS objects an ICE job wrote, never files on the user's disk. The deliverable is handed over as a **playback URL** (§8), not a downloaded file; produce a local file only when the user explicitly asks for one. The few things a local tool must write to be read at all (a waveform PNG, a `silencedetect` transcript, frames you inspect, `edl.json`, `project.md`) go into one scratch dir outside the user's project — `${TMPDIR:-/tmp}/video-editor/<session>` — and are disposable; never create them in the user's workspace. A local file the user hands over is uploaded to OSS first (§4) and operated on there. 2. **All generation goes through ICE; ffmpeg only analyzes audio.** Cutting, concatenating, trimming, overlaying, transitions, subtitle burn-in, mixing, speed change, synthesized speech/dubbing, format conversion: an ICE job renders it (`SubmitMediaProducingJob` for a Timeline, `SubmitIProductionJob` for a single-media algorithm, a template render for a Normal Timeline). ffmpeg's whole remit is **reading the audio signal** — waveform (`showwavespic`), silence/speech tails (`silencedetect`), loudness (`volumedetect`), duration (`ffprobe`) — and it writes nothing but those analysis numbers (§1.4). It never generates or transforms **media**: no `concat`, no `-filter_complex` over a media file, no `-c copy`, no re-encode, no `atempo`, no transcode. Frames come from `SubmitSnapshotJob`, not from ffmpeg. The one exception is a chart, not media: `timeline_view.py` crops tiles out of a **cloud** snapshot sprite and stacks them over the waveform, because no ICE API returns either (§1.4). Not a Python media library either, and not a different cloud product. If no ICE job can express what the user needs, say so and stop — a locally rendered deliverable is a wrong answer, not a workaround. 3. **Confirm region first, then the output bucket before any render** (§2.1). Template Config generation and `AddTemplate` do not need a bucket; rendering one does. There is no default region, not even `cn-shanghai`. 4. **Confirm the plan in plain language before submitting anything** (§2.2). A producing job costs money and minutes; a paragraph of prose costs neither. 5. **Verify every deliverable before reporting it** (§9). `Success` means a file was written, nothing more. Never report an unverified output as verified. 6. **Compile every multi-clip Timeline; never write it directly.** Even when the user asks only for `timeline.json` and forbids cloud calls, first write `edl.json`, then execute `python "$SKILL_DIR/scripts/video_editor.py" compile --edl edl.json --output timeline.json` — `compile` is offline and makes no cloud call (§2.1, `18-edl-and-compile.md`). The generated file is the deliverable. Fix every blocking checklist violation; both `compile` and `submit` enforce the same checklist (§5). **Division of labor**: `references/` = knowledge base you read on demand; `scripts/video_editor.py` = pure executor (submit / poll / fetch URL, plus `iproduction` / `iproduction-status`); `scripts/frame_qa.py` = model review of **cloud** material — the whole video (`--mode full`) or signed snapshot frames (`--frames`), never local sampling (§9). All editing logic lives in the Timeline you generate.

1. Setup

`$SKILL_DIR` below = the directory containing this SKILL.md. **Always invoke the scripts with an absolute path** (`python "$SKILL_DIR/scripts/video_editor.py" ...`); the working directory is usually the user's project, not the skill directory.

pip install -r "$SKILL_DIR/scripts/requirements.txt"

AK/SK (§1.1) and an OSS output bucket