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Ffmpeg Encoder Check

OrganizationPopular
HKUDS
ffmpeg-encoder-check

Check FFmpeg encoder availability before video encoding to avoid library mismatches

Overview

PublisherHKUDS
RepositoryOpenSpace
Skill nameffmpeg-encoder-check
Stars
7.7K
Forks
918
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Ffmpeg Encoder Check 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.
  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/HKUDS/OpenSpace.git /tmp/OpenSpace
mkdir -p .claude/skills
cp -r /tmp/OpenSpace/benchmarks/gdpval/skills/ffmpeg-encoder-check .claude/skills/ffmpeg-encoder-check
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ffmpeg Encoder Check 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 Ffmpeg Encoder Check 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 Ffmpeg Encoder Check 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.

FFmpeg Encoder Availability Check

Purpose

Before writing video encoding scripts, always verify which H.264 encoders are available in your FFmpeg installation. This prevents failures from library version mismatches, particularly with libopenh264.

Instructions

Step 1: Probe Available Encoders

Run the following command to check available H.264 encoders:

bash
ffmpeg -encoders 2>/dev/null | grep h264

This shows which H.264 encoders are compiled into your FFmpeg build.

Step 2: Interpret Results

Common encoder options you may see:

EncoderDescriptionRecommendation
libx264Software H.264 encoderPreferred - widely compatible
libopenh264OpenH264 software encoderUse with caution - often has library version mismatches
h264_nvencNVIDIA hardware encoderGood if NVIDIA GPU available
h264_videotoolboxmacOS hardware encoderGood on macOS
h264_vaapiIntel VAAPI hardware encoderGood on Linux with Intel GPU
h264_qsvIntel QuickSync encoderGood on Windows/Linux with Intel GPU

Step 3: Choose Encoding Strategy

For same-resolution sources (no re-encoding needed):

bash
# Best option - pass-through without quality loss
ffmpeg -i input.mp4 -c:v copy -c:a copy output.mp4

If libx264 is available:

bash
# Reliable software encoding
ffmpeg -i input.mp4 -c:v libx264 -preset medium -crf 23 -c:a aac output.mp4

If only libopenh264 is available:

bash
# Use with caution - may have library issues
ffmpeg -i input.mp4 -c:v libopenh264 -c:a aac output.mp4

Step 4: Validate Before Batch Processing

Always test your encoding command on a small sample file before processing multiple videos or long footage.

Best Practices

  1. Default to -c:v copy when source and target resolutions match - no quality loss, fastest processing
  2. Prefer libx264 over libopenh264 for software encoding - more stable, better compatibility
  3. Check encoder availability at script startup, not during execution - fail fast with clear error
  4. Cache encoder check results if running multiple encoding operations in the same session
  5. Provide fallback options in automated scripts - try copy first, then libx264, then fail gracefully

Example Script Template

bash
#!/bin/bash

# Check available encoders at startup
ENCODERS=$(ffmpeg -encoders 2>/dev/null | grep h264)

if echo "$ENCODERS" | grep -q "libx264"; then
    VIDEO_CODEC="libx264"
    echo "Using libx264 encoder"
elif echo "$ENCODERS" | grep -q "libopenh264"; then
    VIDEO_CODEC="libopenh264"
    echo "Warning: Using libopenh264 (may have compatibility issues)"
else
    echo "Error: No H.264 encoder available"
    echo "Available encoders:"
    echo "$ENCODERS"
    exit 1
fi

# For same-resolution sources, prefer copy
if [ "$SOURCE_RESOLUTION" = "$TARGET_RESOLUTION" ]; then
    VIDEO_CODEC="copy"
    echo "Same resolution detected - using stream copy"
fi

# Encode
ffmpeg -i "$INPUT" -c:v "$VIDEO_CODEC" -c:a aac "$OUTPUT"

Common Errors to Avoid

  • Do not assume libx264 is available - FFmpeg builds vary by system
  • Do not use libopenh264 without checking - frequent library version mismatch errors
  • Do not re-encode unnecessarily - use -c:v copy when resolution matches
  • Do not skip the encoder check - always probe before writing encoding logic

Frequently asked questions

What does the Ffmpeg Encoder Check AI skill do?

Check FFmpeg encoder availability before video encoding to avoid library mismatches

Why use Ffmpeg Encoder Check on TypingMind?

Because you install it once and use it with any model. Ffmpeg Encoder Check 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 Ffmpeg Encoder Check in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HKUDS/OpenSpace/tree/main/benchmarks/gdpval/skills/ffmpeg-encoder-check. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ffmpeg Encoder Check?

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 Ffmpeg Encoder Check?

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

Is the Ffmpeg Encoder Check AI skill free?

Yes. It is published on GitHub by HKUDS 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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