Communitygithub.com

비디오 편집(everything-claude-code)

원본부터 최종 컷까지: FFmpeg, Remotion, ElevenLabs, fal.ai, Descript/CapCut 폴리시.

비디오 편집(everything-claude-code)란 무엇인가요?

데모 후 컷과 페이스 정리 — 출시 가능한 런치 컷.

지원 대상Claude CodeCodex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/video-editing

즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

미리보기

스킬 README에서

문서

Video Editing

AI-assisted editing for real footage. Not generation from prompts. Editing existing video fast.

When to Activate

  • User wants to edit, cut, or structure video footage
  • Turning long recordings into short-form content
  • Building vlogs, tutorials, or demo videos from raw capture
  • Adding overlays, subtitles, music, or voiceover to existing video
  • Reframing video for different platforms (YouTube, TikTok, Instagram)
  • User says "edit video", "cut this footage", "make a vlog", or "video workflow"

Core Thesis

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.

The Pipeline

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.

Layer 1: Capture (Screen Studio / Raw Footage)

Collect the source material:

  • Screen Studio: polished screen recordings for app demos, coding sessions, browser workflows
  • Raw camera footage: vlog footage, interviews, event recordings
  • Desktop capture via VideoDB: session recording with real-time context (see videodb skill)

Output: raw files ready for organization.

Layer 2: Organization (Claude / Codex)

Use Claude Code or Codex to:

  • Transcribe and label: generate transcript, identify topics and themes
  • Plan structure: decide what stays, what gets cut, what order works
  • Identify dead sections: find pauses, tangents, repeated takes
  • Generate edit decision list: timestamps for cuts, segments to keep
  • Scaffold FFmpeg and Remotion code: generate the commands and compositions
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.

Layer 3: Deterministic Cuts (FFmpeg)

FFmpeg handles the boring but critical work: splitting, trimming, concatenating, and preprocessing.

Extract segment by timestamp

ffmpeg -i raw.mp4 -ss 00:12:30 -to 00:15:45 -c copy segment_01.mp4

Batch cut from edit decision list

#!/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

Concatenate segments

# 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

Create proxy for faster editing

ffmpeg -i raw.mp4 -vf "scale=960:-2" -c:v libx264 -preset ultrafast -crf 28 proxy.mp4

Extract audio for transcription

ffmpeg -i raw.mp4 -vn -acodec pcm_s16le -ar 16000 audio.wav

Normalize audio levels

ffmpeg -i segment.mp4 -af loudnorm=I=-16:TP=-1.5:LRA=11 -c:v copy normalized.mp4

Layer 4: Programmable Composition (Remotion)

Remotion turns editing problems into composable code. Use it for things that traditional editors make painful:

When to use Remotion

  • Overlays: text, images, branding, lower thirds
  • Data visualizations: charts, stats, animated numbers
  • Motion graphics: transitions, explainer animations
  • Composable scenes: reusable templates across videos
  • Product demos: annotated screenshots, UI highlights

Basic Remotion composition

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>
  );
};

Render output

npx remotion render src/index.ts VlogComposition output.mp4

See the Remotion docs for detailed patterns and API reference.

Layer 5: Generated Assets (ElevenLabs / fal.ai)

Generate only what you need. Do not generate the whole video.

Voiceover with ElevenLabs

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)

Music and SFX with fal.ai

Use the fal-ai-media skill for:

  • Background music generation
  • Sound effects (ThinkSound model for video-to-audio)
  • Transition sounds

Generated visuals with fal.ai

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"
})

VideoDB generative audio

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")

Layer 6: Final Polish (Descript / CapCut)

The last layer is human. Use a traditional editor for:

  • Pacing: adjust cuts that feel too fast or slow
  • Captions: auto-generated, then manually cleaned
  • Color grading: basic correction and mood
  • Final audio mix: balance voice, music, and SFX levels
  • Export: platform-specific formats and quality settings

This is where taste lives. AI clears the repetitive work. You make the final calls.

Social Media Reframing

Different platforms need different aspect ratios:

PlatformAspect RatioResolution
YouTube16:91920x1080
TikTok / Reels9:161080x1920
Instagram Feed1:11080x1080
X / Twitter16:9 or 1:11280x720 or 720x720

Reframe with FFmpeg

# 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

Reframe with VideoDB

from videodb import ReframeMode

# Smart reframe (AI-guided subject tracking)
reframed = video.reframe(start=0, end=60, target="vertical", mode=ReframeMode.smart)

Scene Detection and Auto-Cut

FFmpeg scene detection

# 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

Silence detection for auto-cut

# Find silent segments (useful for cutting dead air)
ffmpeg -i input.mp4 -af silencedetect=noise=-30dB:d=2 -f null - 2>&1 | grep silence

Highlight extraction

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."

What Each Tool Does Best

ToolStrengthWeakness
Claude / CodexOrganization, planning, code generationNot the creative taste layer
FFmpegDeterministic cuts, batch processing, format conversionNo visual editing UI
RemotionProgrammable overlays, composable scenes, reusable templatesLearning curve for non-devs
Screen StudioPolished screen recordings immediatelyOnly screen capture
ElevenLabsVoice, narration, music, SFXNot the center of the workflow
Descript / CapCutFinal pacing, captions, polishManual, not automatable

Key Principles

  1. Edit, don't generate. This workflow is for cutting real footage, not creating from prompts.
  2. Structure before style. Get the story right in Layer 2 before touching anything visual.
  3. FFmpeg is the backbone. Boring but critical. Where long footage becomes manageable.
  4. Remotion for repeatability. If you'll do it more than once, make it a Remotion component.
  5. Generate selectively. Only use AI generation for assets that don't exist, not for everything.
  6. Taste is the last layer. AI clears repetitive work. You make the final creative calls.

Native Fusion Presets

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.

Related Skills

  • fal-ai-media — AI image, video, and audio generation
  • videodb — Server-side video processing, indexing, and streaming
  • content-engine — Platform-native content distribution

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

관련 스킬