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Vlog Pacing

Community
appariciojunior
vlog-pacing

The numbers and rules that make a transcript-based cut feel natural instead of "popping" - silence detection, word pads, pause shortening, room at scene edges, jump-cut zoom alternation and punch-ins. Use when building the cut planner, tuning pauses, or when the creator says the edit is too choppy or too slow.

Overview

Publisherappariciojunior
Repositoryvideo-editor-skills
Skill namevlog-pacing
Stars
360
Forks
50
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Vlog Pacing 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/appariciojunior/video-editor-skills/tree/main/skills/vlog-pacing
  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/appariciojunior/video-editor-skills.git /tmp/video-editor-skills
mkdir -p .claude/skills
cp -r /tmp/video-editor-skills/skills/vlog-pacing .claude/skills/vlog-pacing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vlog Pacing 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 Vlog Pacing 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 Vlog Pacing 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.

Pacing: how much air to leave

A transcript cut is precise; the danger is that it is too precise. Every cut becomes a jump cut and the video "pops". These values came from real feedback.

Cut from the transcript, never from volume

A volume-only silence cut (ffmpeg silencedetect on the whole file) gave ~800 choppy pieces on a 34-minute class. Cutting from the word timestamps gave 120 pieces the creator loved. Use the waveform only to refine edges.

Planner algorithm (in words)

  1. Silence map per clip: below -43 dB for at least 0.25 s.
  2. Pause candidates inside kept speech: gap between two words ≥ 0.25 s and ≥ 85% of it is silence on the waveform (an untranscribed sound is not a pause).
  3. Shorten only pauses longer than max_pause, down to pause_keep. Split the leftover air 40% before / 60% after.
  4. Word edges: start = word start − 0.08 s, end = word end + 0.12 s, refined to the nearest silence edge within 0.25 / 0.3 s so soft consonants aren't clipped; never reach into a word that was cut.
  5. Scene edges: the first range of a clip gets 0.5 s before the first word, the last range 0.6 s after the last word (only spoken words limit this; a lighter click or ambience may stay). Override per range in the keep-list (lead_in, tail). Look at frames first: stop before the creator reaches for the camera.
  6. Snap to frames (start floors, end ceils, end exclusive), remove overlaps, merge touching pieces.
  7. Write a review file: the transcript with cut words struck through + a timeline list with times.

Settings that worked

Contentmax_pausepause_keepFeel
Class / tutorial0.5 s0.3 stight, loved
Vlog1.0 s0.6 sbreathing room; 0.5/0.3 felt "popping"

Rough check with a pause report ("what if max_pause = …: N cuts, saves X s") before choosing. On a dramatic slow opening, 0.5 s gave 7 jump cuts in 20 seconds; 1.0 s gave 3 in 53 seconds.

Framing (zoom)

  • Every jump cut inside one scene alternates 100% / 107.5%, so it reads as a camera change.
  • A new scene (new clip, or after B-roll) always opens at 100%. Toggling zoom on scene changes adds pops.
  • Punch-in 115% only for the punchline itself: split the piece at the line's first word and again right after its last word. Without the second split one punch-in held 83 seconds.
  • B-roll is always 100%.

When they say "it's choppy"

Measure first (how many cuts come from pause shortening vs. scene edges vs. zoom), then fix the actual cause: usually loosen pauses, add scene room, and stop zooming on scene changes. Don't guess.

Frequently asked questions

What does the Vlog Pacing AI skill do?

The numbers and rules that make a transcript-based cut feel natural instead of "popping" - silence detection, word pads, pause shortening, room at scene edges, jump-cut zoom alternation and punch-ins. Use when building the cut planner, tuning pauses, or when the creator says the edit is too choppy or too slow.

Why use Vlog Pacing on TypingMind?

Because you install it once and use it with any model. Vlog Pacing 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 Vlog Pacing in TypingMind?

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

Which AI models can use Vlog Pacing?

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 Vlog Pacing?

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

Is the Vlog Pacing AI skill free?

It is published on GitHub by appariciojunior. Check the repository for licensing terms. 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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