Video To Gif logo

Video To Gif

Organization
cognyai
video-to-gif

Convert an MP4 (e.g. a HyperFrames render or a screen recording) to a high-quality GIF using FFmpeg's two-pass palette method. Picks the right preset for the target surface — Slack/DMs/Twitter/email/Reddit/GitHub — so you don't ship a 40MB GIF to a 5MB inbox.

Overview

Publishercognyai
Repositoryclaude-code-marketing-skills
Skill namevideo-to-gif
Stars
111
Forks
15
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 cognyai on GitHub. Read the source before you install it.

Installation

Install the Video To Gif 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/cognyai/claude-code-marketing-skills/tree/main/skills/video-to-gif
  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/cognyai/claude-code-marketing-skills.git /tmp/claude-code-marketing-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-marketing-skills/skills/video-to-gif .claude/skills/video-to-gif
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Video To Gif 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 To Gif 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 To Gif 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 to GIF

Convert a video (MP4, MOV, WebM) to a GIF that's actually small and actually looks good on the surface you're posting to. Pairs naturally with /tiktok-launch-video, /reddit-launch-video, and /linkedin-launch-video for sharing renders in places where MP4 isn't supported (Slack threads, X cards, GitHub READMEs, transactional emails).

GIF is a terrible video format — but it's everywhere, so we use FFmpeg's two-pass palettegen / paletteuse flow to make it look as good as the format allows.

Works with

Runs locally on FFmpeg with zero accounts. One optional hand-off:

JobPrimaryAlso answers itWhat differs
Make the GIFFFmpeg (local)—Always available
Post it to X (enhanced when connected)X Organic tool_upload_media + tool_create_post—Asks first; needs the GIF at a public URL and ≤ 15 MB

Usage

/video-to-gif launch.mp4 — interactive, asks the target /video-to-gif launch.mp4 slack — Slack-tuned /video-to-gif launch.mp4 reddit — Reddit-comment-tuned /video-to-gif launch.mp4 github — GitHub README–tuned /video-to-gif launch.mp4 twitter — X / Twitter–tuned /video-to-gif launch.mp4 email — transactional / newsletter–tuned

Prerequisites

bash
ffmpeg -version | head -1   # must exist
ffprobe -version | head -1

If FFmpeg isn't installed:

bash
# macOS
brew install ffmpeg
# Debian/Ubuntu
sudo apt install ffmpeg

Steps

1. Inspect the source

Run ffprobe to get the source's duration, dimensions, and frame rate. You need these to pick the right preset and to warn the user before producing something that won't fit.

bash
ffprobe -v error -show_entries stream=width,height,r_frame_rate -show_entries format=duration -of default=noprint_wrappers=1 <input>

Capture: source width, source height, source duration (seconds), source fps.

2. Pick the preset

If the user passed a target, use the matching preset. Otherwise ask. Each preset is tuned to the surface's actual size limits and aesthetic norms:

PresetWidthFPSLoopWhy
slack48012yes50MB upload cap; threads scroll fast; readability > smoothness
twitter48015yes15MB GIF cap; auto-converted to MP4 above the cap, kills the loop
reddit60015yes100MB cap but image-host sidebar dies past 20MB
github72015yes10MB README cap; over the cap GitHub silently drops the file
email48010yes5MB Gmail clip threshold; many ESPs strip animation past 1MB
web80018yesGeneric fallback for landing pages and product blogs

Rules of thumb:

  • If source is < 6 seconds, you can usually bump fps by ~3 without breaking the size budget.
  • If source is > 12 seconds, drop fps by 2–3 and consider trimming.
  • Never upscale — if source width is below the preset width, keep source width.
  • Strip audio (GIF can't carry it) — saves a step and removes accidental noise from screen captures.

3. Two-pass palette render (this is the only correct way)

A single-pass GIF render produces banded, dithered, oversized garbage. The right approach is two passes:

  1. Generate an optimal 256-color palette from the source.
  2. Apply that palette with Floyd-Steinberg dithering.

The pipeline is FFmpeg's palettegen filter feeding paletteuse. Do it as a single command using filter splits:

bash
ffmpeg -y -i "<input>" \
  -vf "fps=<FPS>,scale=<WIDTH>:-1:flags=lanczos,split[s0][s1];[s0]palettegen=stats_mode=diff[p];[s1][p]paletteuse=dither=floyd_steinberg" \
  -loop 0 \
  "<output.gif>"

Notes on the filter chain:

  • fps=<FPS> first — so palette is generated from the actual frames you'll keep.
  • scale=<WIDTH>:-1 keeps aspect; -1 rounds the height. Use -2 for codecs that need even dimensions (not needed for GIF, but harmless).
  • flags=lanczos is the best general-purpose downscaler.
  • stats_mode=diff weights moving regions higher when picking palette colors — sharper text, cleaner UI, less banding.
  • dither=floyd_steinberg is the best default. For UI / sharp edges with no gradients, try dither=bayer:bayer_scale=3 instead — slightly larger files but no dither noise on flat colors.
  • -loop 0 = infinite loop. Use -loop 1 to play once. (For Slack and Twitter, infinite loop is what users expect.)

Alternative for HyperFrames sources: if the input is still a HyperFrames project (not just its MP4), you can render the GIF straight from the composition, skipping the MP4 step: npx hyperframes render --format gif --fps 15 --gif-loop 0 --output out.gif from the project directory (--fps 15 is HyperFrames' recommended GIF rate; it still needs FFmpeg). It outputs at the composition's own size (its --resolution only scales up), so when the preset in step 2 is narrower than the composition, use the MP4 + palette pipeline below instead. Either way, check the cap as in step 4.

4. Render and verify

Run the command. Then verify:

bash
# Check the file size
ls -lh "<output.gif>"

# Check the GIF's actual dimensions and frame count
ffprobe -v error -count_frames -select_streams v:0 \
  -show_entries stream=width,height,nb_read_frames \
  -of default=noprint_wrappers=1 "<output.gif>"

If the file is larger than the preset's surface cap (see table above), do one of:

  1. Drop FPS by 2–3 and re-render (best quality preserved).
  2. Drop width by ~80px and re-render (text may get harder to read).
  3. Trim the source first (-ss <start> -t <duration>) and re-render.

Don't mix all three — change one variable at a time so you know what helped.

5. (Optional) Trim before converting

If the source is long, trim before the palette pass — the palette will be more accurate and the file smaller.

bash
# Take 8 seconds starting at 2.5s in
ffmpeg -y -ss 2.5 -t 8 -i "<input>" -c copy "<input>.trimmed.mp4"
# then run the two-pass palette command on .trimmed.mp4

-ss before -i is keyframe-fast but slightly imprecise. For frame-accurate trims, put -ss after -i (slower).

6. Hand the file back

Print:

  1. Path to the output GIF
  2. Final size on disk
  3. Final dimensions and frame count
  4. Surface cap status: "fits Slack (50MB) ✓" / "exceeds GitHub README cap (10MB), retry with fps=12 ✗"

Enhanced when connected (twitter preset only): if the workspace has X Organic connected and the user wants it posted, get a public URL for the GIF (on Cloud, mcp__cogny__mcp__cogny-image-gen__tool_request_upload_url plus the returned curl; otherwise ask for a hosted link), then call mcp__cogny__mcp__X_Organic__tool_upload_media(file_url, alt_text) and mcp__cogny__mcp__X_Organic__tool_create_post(text, media_ids). Both ask first, and the post goes live immediately, so show the exact text before posting.

One-liners (for when the user knows what they want)

bash
# Slack-tuned (480px / 12fps)
ffmpeg -y -i in.mp4 -vf "fps=12,scale=480:-1:flags=lanczos,split[s0][s1];[s0]palettegen=stats_mode=diff[p];[s1][p]paletteuse=dither=floyd_steinberg" -loop 0 out.gif

# Twitter-tuned (480px / 15fps)
ffmpeg -y -i in.mp4 -vf "fps=15,scale=480:-1:flags=lanczos,split[s0][s1];[s0]palettegen=stats_mode=diff[p];[s1][p]paletteuse=dither=floyd_steinberg" -loop 0 out.gif

# GitHub README-tuned (720px / 15fps)
ffmpeg -y -i in.mp4 -vf "fps=15,scale=720:-1:flags=lanczos,split[s0][s1];[s0]palettegen=stats_mode=diff[p];[s1][p]paletteuse=dither=floyd_steinberg" -loop 0 out.gif

# Email-tuned (480px / 10fps, single loop)
ffmpeg -y -i in.mp4 -vf "fps=10,scale=480:-1:flags=lanczos,split[s0][s1];[s0]palettegen=stats_mode=diff[p];[s1][p]paletteuse=dither=floyd_steinberg" -loop 0 out.gif

Common mistakes

  1. Single-pass GIF render — ffmpeg -i in.mp4 out.gif produces ugly, oversized output. Always use the palette pipeline.
  2. Upscaling — never scale GIFs up; they go from "bad" to "extremely bad". Match or downscale only.
  3. High FPS for talking-head content — anything above 18fps is wasted on a GIF; drop to 12–15fps and put the bytes into resolution instead.
  4. Including audio — GIF can't carry it. If you need audio, the surface supports MP4 and you should ship MP4 instead.
  5. Long GIFs (> 15s) — if you need 30+ seconds, you probably need MP4 or WebP, not GIF. Ask the user if a different format works.
  6. Sending a 40MB GIF to email — most ESPs strip the animation past 1MB, leaving the recipient with a static first frame. Use the email preset or fall back to a static PNG hero with a link.

Related

  • /tiktok-launch-video, /reddit-launch-video, /linkedin-launch-video — produce the source MP4 this skill converts
  • /welcome-series, /winback-engine — places where a small embedded GIF lifts engagement

Frequently asked questions

What does the Video To Gif AI skill do?

Convert an MP4 (e.g. a HyperFrames render or a screen recording) to a high-quality GIF using FFmpeg's two-pass palette method. Picks the right preset for the target surface — Slack/DMs/Twitter/email/Reddit/GitHub — so you don't ship a 40MB GIF to a 5MB inbox.

Why use Video To Gif on TypingMind?

Because you install it once and use it with any model. Video To Gif 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 To Gif in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cognyai/claude-code-marketing-skills/tree/main/skills/video-to-gif. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Video To Gif?

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 To Gif?

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

Is the Video To Gif AI skill free?

It is published on GitHub by cognyai. 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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