依据WCAG 2.2设计和检查Web、iOS及Android界面,核查读屏标签、键盘操作、配色对比度和操作目标尺寸,使界面支持辅助技术。
- 为纯图标按钮添加说明
- 检查键盘操作与模态框焦点
- 检查对比度与操作目标尺寸
从已有视频和屏幕录制中选择精彩片段,完成剪切、结构整理、字幕与音频添加,涵盖长视频精简、社交平台画幅调整及编辑软件中的最终修整。
AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.
此技能帮助整理拍摄素材和屏幕录制,挑选精彩片段并合成为一个视频。它根据转写文本查找不必要的停顿和重复内容,确定保留区间与顺序。使用FFmpeg剪切、拼接和调整音量,使用Remotion叠加文字与图表。还可按需添加旁白、音乐和补充图片,并将画幅调整为适合社交平台的竖屏或正方形。
适合将长时间录制精简为短篇精彩集锦,或用已有素材制作Vlog、教程和产品演示。在多个视频中重复使用同一种呈现效果时,也可以使用此技能。
主要用于编辑实拍素材,不用于通过提示词生成整段视频。不同环节会使用FFmpeg、Remotion等外部工具,调用ElevenLabs的API需要API密钥。最终节奏、字幕修正、色调和音频平衡需要由人在Descript或CapCut中检查和调整。
本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。
AI 仅根据 SKILL.md 的内容,从 4 个方面进行了评估。星标数和热度不计入评估。
总分按每项 A=25 分、B=15 分、C=5 分相加。 由 AI(OpenAI Codex)于 2026年10月19日 评估。仅供参考,安装前请同时查看原文。
安装前可以查看交给 AI 代理的指令内容。
AI-assisted editing for real footage. Not generation from prompts. Editing existing video fast.
AI video editing is useful when you stop asking it to create the whole video and start using it to compress, structure, and augment real footage. The value is not generation. The value is compression.
For measured reference-driven work, chain taste-distillation into
taste-application, then return here for the editor and final-output review.
The standalone taste skills can use existing footage; generation is optional.
Before live editor or DAW changes, save a versioned project checkpoint and verify the file exists. Save and verify another checkpoint after the changes. An API readback proves the current in-memory state, not that it was saved. Keep rendered media, editable projects, and creative approval as separate states in the handoff.
For MIDI-driven audio, check pitches against the receiving rack's note mapping and audition the result; successful clip creation can still produce silence. For reconstructed projects, validate through native load and save, sort events in timeline order, verify sample links and mute states, then check and audition the exact exported audio for unintended silence. XML parsing alone does not prove that the DAW accepted every clip or produced audible output. Check a bridge's capability handshake before invoking newer commands. Do not enable upload or training-data telemetry as a side effect of a creative task; use a supported local control path when consent or capability is absent.
Screen Studio / raw footage
→ Claude / Codex
→ FFmpeg
→ Remotion
→ ElevenLabs / fal.ai
→ Descript or CapCut
Each layer has a specific job. Do not skip layers. Do not try to make one tool do everything.
Collect the source material:
videodb skill)Output: raw files ready for organization.
Use Claude Code or Codex to:
Example prompt:
"Here's the transcript of a 4-hour recording. Identify the 8 strongest segments
for a 24-minute vlog. Give me FFmpeg cut commands for each segment."
This layer is about structure, not final creative taste.
FFmpeg handles the boring but critical work: splitting, trimming, concatenating, and preprocessing.
ffmpeg -i raw.mp4 -ss 00:12:30 -to 00:15:45 -c copy segment_01.mp4
#!/bin/bash
# cuts.txt: start,end,label
while IFS=, read -r start end label; do
ffmpeg -i raw.mp4 -ss "$start" -to "$end" -c copy "segments/${label}.mp4"
done < cuts.txt
# Create file list
for f in segments/*.mp4; do echo "file '$f'"; done > concat.txt
ffmpeg -f concat -safe 0 -i concat.txt -c copy assembled.mp4
ffmpeg -i raw.mp4 -vf "scale=960:-2" -c:v libx264 -preset ultrafast -crf 28 proxy.mp4
ffmpeg -i raw.mp4 -vn -acodec pcm_s16le -ar 16000 audio.wav
ffmpeg -i segment.mp4 -af loudnorm=I=-16:TP=-1.5:LRA=11 -c:v copy normalized.mp4
Remotion turns editing problems into composable code. Use it for things that traditional editors make painful:
import { AbsoluteFill, Sequence, Video, useCurrentFrame } from "remotion";
export const VlogComposition: React.FC = () => {
const frame = useCurrentFrame();
return (
<AbsoluteFill>
{/* Main footage */}
<Sequence from={0} durationInFrames={300}>
<Video src="/segments/intro.mp4" />
</Sequence>
{/* Title overlay */}
<Sequence from={30} durationInFrames={90}>
<AbsoluteFill style={{
justifyContent: "center",
alignItems: "center",
}}>
<h1 style={{
fontSize: 72,
color: "white",
textShadow: "2px 2px 8px rgba(0,0,0,0.8)",
}}>
The AI Editing Stack
</h1>
</AbsoluteFill>
</Sequence>
{/* Next segment */}
<Sequence from={300} durationInFrames={450}>
<Video src="/segments/demo.mp4" />
</Sequence>
</AbsoluteFill>
);
};
npx remotion render src/index.ts VlogComposition output.mp4
See the Remotion docs for detailed patterns and API reference.
Generate only what you need. Do not generate the whole video.
import os
import requests
resp = requests.post(
f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}",
headers={
"xi-api-key": os.environ["ELEVENLABS_API_KEY"],
"Content-Type": "application/json"
},
json={
"text": "Your narration text here",
"model_id": "eleven_turbo_v2_5",
"voice_settings": {"stability": 0.5, "similarity_boost": 0.75}
}
)
with open("voiceover.mp3", "wb") as f:
f.write(resp.content)
Use the fal-ai-media skill for:
Use for insert shots, thumbnails, or b-roll that doesn't exist:
generate(app_id: "fal-ai/nano-banana-pro", input_data: {
"prompt": "professional thumbnail for tech vlog, dark background, code on screen",
"image_size": "landscape_16_9"
})
If VideoDB is configured:
voiceover = coll.generate_voice(text="Narration here", voice="alloy")
music = coll.generate_music(prompt="lo-fi background for coding vlog", duration=120)
sfx = coll.generate_sound_effect(prompt="subtle whoosh transition")
The last layer is human. Use a traditional editor for:
This is where taste lives. AI clears the repetitive work. You make the final calls.
Different platforms need different aspect ratios:
| Platform | Aspect Ratio | Resolution |
|---|---|---|
| YouTube | 16:9 | 1920x1080 |
| TikTok / Reels | 9:16 | 1080x1920 |
| Instagram Feed | 1:1 | 1080x1080 |
| X / Twitter | 16:9 or 1:1 | 1280x720 or 720x720 |
# 16:9 to 9:16 (center crop)
ffmpeg -i input.mp4 -vf "crop=ih*9/16:ih,scale=1080:1920" vertical.mp4
# 16:9 to 1:1 (center crop)
ffmpeg -i input.mp4 -vf "crop=ih:ih,scale=1080:1080" square.mp4
from videodb import ReframeMode
# Smart reframe (AI-guided subject tracking)
reframed = video.reframe(start=0, end=60, target="vertical", mode=ReframeMode.smart)
# Detect scene changes (threshold 0.3 = moderate sensitivity)
ffmpeg -i input.mp4 -vf "select='gt(scene,0.3)',showinfo" -vsync vfr -f null - 2>&1 | grep showinfo
# Find silent segments (useful for cutting dead air)
ffmpeg -i input.mp4 -af silencedetect=noise=-30dB:d=2 -f null - 2>&1 | grep silence
Use Claude to analyze transcript + scene timestamps:
"Given this transcript with timestamps and these scene change points,
identify the 5 most engaging 30-second clips for social media."
| Tool | Strength | Weakness |
|---|---|---|
| Claude / Codex | Organization, planning, code generation | Not the creative taste layer |
| FFmpeg | Deterministic cuts, batch processing, format conversion | No visual editing UI |
| Remotion | Programmable overlays, composable scenes, reusable templates | Learning curve for non-devs |
| Screen Studio | Polished screen recordings immediately | Only screen capture |
| ElevenLabs | Voice, narration, music, SFX | Not the center of the workflow |
| Descript / CapCut | Final pacing, captions, polish | Manual, not automatable |
ITO Production v1 provides restrained highlight bloom, opposing RGB spatial offsets and a luminance/edge halo. The exact files passed prior native import, save/reopen and short motion-render checks after two-source visual review. These are starting values requiring shot-specific review; the halo does not detect or track subjects.
ITO V28 contains preserved, native-verified Fusion graph snippets and an idempotent Lua installer. These are technical compatibility examples, not recommended production defaults: their documented visual limitations require tuning and taste review before use. See the bundle provenance for the scope of prior import and render checks.
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依据WCAG 2.2设计和检查Web、iOS及Android界面,核查读屏标签、键盘操作、配色对比度和操作目标尺寸,使界面支持辅助技术。
从指令、记忆、工具执行和界面显示等十二个层面排查人工智能代理故障,依据代码和日志梳理原因,按严重程度列出问题并提出修复方案。
用实际开发任务比较多个编程智能体,记录成功率、可获取的API费用、耗时和重复运行的稳定性,为工具选型及更新后的性能评估提供数据。
设计和调整AI智能体使用的工具、输入与返回格式,明确出错后的恢复、重试和停止条件,管理上下文信息量,并以完成率、重试次数和成功任务成本评估改进。
根据错误信息和执行环境,分析AI代理反复失败或重复操作的原因。通过小范围、可撤销的恢复操作检验假设,并将结果与依据整理成可供交接的报告。
在AI代理反复失败或重复操作时,记录错误与环境前提、排查原因,采取影响有限的恢复操作,并汇总结果和证据,为有依据的重试与交接提供支持。