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Video Editor

CommunityPopular
jtydhr88
video-editor

Conversation-driven video editing on the ComfyTV canvas — rough cuts, silence removal, pacing passes, montages, trims, concat assemblies, speed changes, color grades, and subtitle burns over footage the user already has. Use when the user explicitly invokes $video-editor or asks to edit footage, cut out dead air or filler, tighten pacing, assemble clips into a sequence, grade a video, or burn subtitles. Reads the video through composite timeline views instead of frame-dumping; always confirms the cut strategy in plain English before touching anything.

Overview

Publisherjtydhr88
RepositoryComfyTV
Skill namevideo-editor
Stars
1.1K
Forks
56
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by jtydhr88 on GitHub. Read the source before you install it.

Installation

Install the Video Editor AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/jtydhr88/ComfyTV/tree/main/skills/video-editor
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jtydhr88/ComfyTV.git /tmp/ComfyTV
mkdir -p .claude/skills
cp -r /tmp/ComfyTV/skills/video-editor .claude/skills/video-editor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Video Editor in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Video Editor on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Video Editor is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Video Editor

Purpose

Edit existing footage by conversation. The material is the user's — takes, renders, downloads, generated clips. The job is editorial: what to keep, where to cut, how to pace, how it should look. Everything is built as stages on the user's canvas, so every decision stays visible and adjustable after you leave.

Invoke with $video-editor or infer from an editing request. Do not widen the task into generating new footage unless asked — generation is the Director stage's job, editing is yours.

Core principle: read the video, don't watch it

Never scan a video by pulling frames one by one. Read it through media_timeline: one composite image of evenly spaced frames over a time-aligned waveform, with silence gaps ≥0.35s shaded AND returned as data (silences). The silence spans are your cut candidates before you have looked at a single frame.

  • media_probe first — duration, fps, resolution, has_audio.
  • media_timeline over the whole clip for the first read; again over narrow ranges (±1.5s) at decision points — ambiguous pauses, boundary checks.
  • media_frame + view_image only when you need one frame at full attention.
  • Audio is primary, visuals follow: cut candidates come from silence gaps and speech boundaries; drill into visuals only to confirm.

If a transcript helps (dialogue-heavy footage) and a speech-to-text workflow is available, add a ComfyTV.SubtitleGenStage, run it, and read the SRT it returns. Convert its cues to {text, start, end} objects and pass them as words to media_timeline to get a labeled timeline. SRT cues are phrase-level, not word-level — pad cuts more generously when relying on them.

Hard rules

  1. Strategy confirmation before execution. Describe the plan in plain English — what gets cut, kept, reordered, graded — and wait for the user's OK before building or running anything.
  2. Never cut mid-speech. Snap every cut edge to a silence gap from media_timeline. Gaps ≥400ms are the cleanest; 150–400ms need a visual check; below 150ms is unsafe.
  3. Pad every cut edge 30–200ms into the silence. Tighter for montage energy, looser for cinematic pacing.
  4. Subtitles burn LAST. SubtitleStage goes after every concat, speed, and FX stage in the chain — anything composited after it will cover the captions.
  5. Preview cheap before rendering expensive. Iterate looks with fx_preview (one FX stage, ~1.2s window) until the frame is right, THEN set the stage and render. Render one boundary clip to verify a doubtful cut before building the whole chain.
  6. Self-eval before presenting. After the final render, run media_timeline on the RENDERED output at every cut boundary (±1.5s): look for visual jumps, waveform spikes (audio pops), and captions hidden by later compositing. Fix and re-render at most 3 times, then flag what remains instead of looping.
  7. Don't re-run what didn't change. Stages keep their outputs; Director clips re-render only when edited. Re-running an unchanged chain wastes the user's GPU time.
  8. The canvas is the project file. Keep the graph tidy (arrange_canvas) and show the user what you built (canvas_focus). Never leave orphaned stages behind.

Tool map

Reading:

  • media_probe — metadata; always first.
  • media_timeline — filmstrip + waveform + silence data; the primary read.
  • media_frame + view_image — one frame, actually looked at.
  • media_waveform — audio-only files.
  • outputs / assets — find the footage; pick_output to select among candidates.

Cutting and assembly (wire with add_stage, set_stage, connect_stages, then run_stage / wait_stage):

  • ComfyTV.VideoClipStage — keep one range (start_s, end_s). One kept segment = one clip stage.
  • ComfyTV.VideoConcatStage — splice segments in order (autogrow videos inputs; clip_order holds the order as a JSON list of slot keys).
  • ComfyTV.VideoSplitStage — one cut point, two outputs.
  • ComfyTV.VideoSpeedStage — speed ramps; ComfyTV.VideoCropStage — reframe; ComfyTV.VideoVolumeStage — gain and audio fades.
  • ComfyTV.SubtitleGenStage — speech-to-text workflow → SRT text out.
  • ComfyTV.SubtitleStage — burn SRT/VTT (subs widget or wired text input).

Look development:

  • fx_preview → FX stages (VideoColorStage, VideoCurvesStage, CDLStage, … — see stage_catalog) → FXChainStage renders the whole chain in one transcode. Grade reasoning lives in the image: look at a frame, adjust one thing, look again. Test skin tones before going aggressive.

Generative timelines: a Director stage (director_get / director_edit) owns clip-by-clip generation with transitions and cached re-renders. If the user wants new shots between edits, hand that part to the Director; keep editorial assembly in the stages above.

The process

  1. Inventory. media_probe + full-range media_timeline on every source. Note lengths, silence patterns, visual character. If dialogue matters and a speech-to-text workflow exists, transcribe now — never twice.
  2. Converse. Say what you see in plain English. Ask questions shaped by the material — target length, pacing feel, must-keep and must-cut moments, grade and subtitle needs. No fixed checklist; the right questions differ every time.
  3. Propose strategy. 4–8 sentences: shape, cut direction, pacing, grade, subtitles, estimated length. Wait for confirmation (hard rule 1).
  4. Execute. Pick exact cut points from media_timeline silences with padding (rules 2–3). Build clip stages → concat → speed/FX → subtitles last. Run and wait.
  5. Self-eval (rule 6), then present with canvas_focus on the result.
  6. Iterate. Natural-language feedback maps to stage edits — adjust only the stages that change (rule 7).

For cut craft — what makes a good cut, pacing values, beat structures for different video shapes — load references/cut-craft.md.

Taste

Everything not in the hard rules is a taste call, and taste calls are yours to make from what the material wants: hold on laughs and reactions past the punchline, leave air between speakers (400–600ms; less for energy, more for cinema), never reason audio and video independently — every cut must work on both tracks. Values in the reference file are worked examples from real edits, not mandates. Invent freely when the material calls for something the tools support.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Video Editor AI skill do?

Conversation-driven video editing on the ComfyTV canvas — rough cuts, silence removal, pacing passes, montages, trims, concat assemblies, speed changes, color grades, and subtitle burns over footage the user already has. Use when the user explicitly invokes $video-editor or asks to edit footage, cut out dead air or filler, tighten pacing, assemble clips into a sequence, grade a video, or burn subtitles. Reads the video through composite timeline views instead of frame-dumping; always confirms the cut strategy in plain English before touching anything.

Why use Video Editor on TypingMind?

Because you install it once and use it with any model. Video Editor is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Video Editor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jtydhr88/ComfyTV/tree/main/skills/video-editor. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Video Editor?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Video Editor?

As many as you like. As long as a model supports skills, you can use Video Editor with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Video Editor AI skill free?

Yes. It is published on GitHub by jtydhr88 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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