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Ffmpeg Best Practice

Community
linyqh
ffmpeg-best-practice

Lean FFmpeg playbook for reliable video compression, WeChat-compatible MP4 export, clip stitching, audio mixing, subtitle handling, and quick fallback decisions. Use for requests like “压缩视频”, “微信上传”, “体积太大”, “导出更小但别糊”, “多段素材拼接”, “字幕烧录”, and “ffmpeg 报错”.

Overview

Publisherlinyqh
Repositoryspeclip-skills
Skill nameffmpeg-best-practice
Stars
110
Forks
14
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 linyqh on GitHub. Read the source before you install it.

Installation

Install the Ffmpeg Best Practice 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/linyqh/speclip-skills/tree/main/ffmpeg-best-practice
  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/linyqh/speclip-skills.git /tmp/speclip-skills
mkdir -p .claude/skills
cp -r /tmp/speclip-skills/ffmpeg-best-practice .claude/skills/ffmpeg-best-practice
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ffmpeg Best Practice 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 Best Practice 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 Best Practice 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 Best Practice

Use this skill when the task requires direct FFmpeg work and the answer should be stable, compatible, and easy to adapt.

Scope

  • Compression for smaller files without obvious quality loss
  • WeChat/mobile/web-safe MP4 output
  • Concat, trim, normalize, subtitle burn-in or muxing
  • Audio mixing and missing-audio handling
  • Hardware encode with software fallback

Core Workflow

  1. Probe input files with ffprobe before writing commands.
  2. Check encoder availability once per machine/session.
  3. Decide whether the job is stream-copy or re-encode.
  4. For compression, default to MP4 + H.264 + AAC + yuv420p + +faststart.
  5. Prefer capped CRF first; use 2-pass only for a hard file-size target.
  6. Use -map 0:a? for optional audio and do not write [0:a?] inside filter_complex.
  7. If hardware encode fails or produces oversized output, fall back to libx264.
  8. For subtitle burn-in, first collect subtitle style/settings parameters, then convert subtitles to .ass, and only then run burn-in.
  9. Return the command, output path, chosen profile, and key flag rationale.

Probe First

Run for each input:

bash
ffprobe -v error -hide_banner -show_streams -show_format -of json "INPUT"

Check once per machine/session:

bash
ffmpeg -hide_banner -encoders
ffmpeg -hide_banner -hwaccels

Minimum checks:

  • Video/audio presence
  • Resolution, fps, duration, rotation
  • Whether target is compatibility-first or strict-size-first
  • Available H.264 hardware encoders on this machine

Compression Rules

When the goal is “尽量小,但别太糊”:

  1. Do not copy streams. Re-encode.
  2. Keep MP4 + H.264 + AAC + yuv420p + +faststart as the default delivery target.
  3. Prefer CRF + maxrate + bufsize over pure fixed bitrate.
  4. Downscale before crushing bitrate.
  5. Clamp fps to 24/25/30 unless higher fps is genuinely needed.
  6. Use AAC 64k~96k for speech-heavy output, 96k~128k for general content.
  7. Never upscale just to match a target profile.

WeChat-Compatible Core Method

Treat “微信压缩” as a compatibility-first export target, not a claim of exact parity with WeChat’s private pipeline.

  • Container: mp4
  • Video: H.264, yuv420p
  • Audio: AAC-LC, stereo, 44.1kHz or 48kHz
  • Delivery: -movflags +faststart
  • Frame rate: 24, 25, or 30
  • Avoid 10-bit, unusual pixel formats, and fragile containers

Use this ladder as the default starting point:

text
540p:  CRF 23, maxrate 1000k, bufsize 2000k, AAC 64k~96k
720p:  CRF 22, maxrate 1800k, bufsize 3600k, AAC 96k
1080p: CRF 21, maxrate 2800k, bufsize 5600k, AAC 96k~128k

Selection:

  • Short side <= 540: keep at most 540p
  • Short side <= 720: keep at most 720p
  • Short side > 720: default to 720p; keep 1080p only if detail matters
  • Hard upload limit: switch to 2-pass bitrate budgeting

Command Patterns

Replace placeholders like IN, OUT, W, H, FPS, CRF, and bitrate values before running.

1) Fast remux, no compression

bash
ffmpeg -y -i "IN" -map 0:v:0 -map 0:a? -c copy -movflags +faststart "OUT"

2) Web-safe baseline export

bash
ffmpeg -y -i "IN" \
  -c:v libx264 -preset medium -crf 21 -pix_fmt yuv420p \
  -c:a aac -b:a 128k -ar 48000 -ac 2 \
  -map 0:v:0 -map 0:a? -movflags +faststart "OUT"

3) Recommended WeChat-style compression

Default to 720p / 25fps / CRF 22 / maxrate 1800k / bufsize 3600k / AAC 96k unless the source or budget says otherwise.

bash
ffmpeg -y -i "IN" \
  -vf "scale='min(W,iw)':'min(H,ih)':force_original_aspect_ratio=decrease:force_divisible_by=2,pad=W:H:(ow-iw)/2:(oh-ih)/2,setsar=1,fps=FPS" \
  -c:v libx264 -preset slow -crf CRF -maxrate VMAXRATE -bufsize VBUFSIZE \
  -pix_fmt yuv420p -c:a aac -b:a ABITRATE -ar 48000 -ac 2 \
  -map 0:v:0 -map 0:a? -movflags +faststart "OUT"

4) Hard file-size target with 2-pass H.264

text
target_total_kbps = TARGET_MB * 8192 / DURATION
target_video_kbps = target_total_kbps - audio_kbps - 32
bash
ffmpeg -y -i "IN" \
  -vf "scale='min(W,iw)':'min(H,ih)':force_original_aspect_ratio=decrease:force_divisible_by=2,pad=W:H:(ow-iw)/2:(oh-ih)/2,setsar=1,fps=FPS" \
  -c:v libx264 -preset slow -b:v VBITRATE -maxrate VMAXRATE -bufsize VBUFSIZE \
  -pix_fmt yuv420p -pass 1 -an -f mp4 /dev/null && \
ffmpeg -y -i "IN" \
  -vf "scale='min(W,iw)':'min(H,ih)':force_original_aspect_ratio=decrease:force_divisible_by=2,pad=W:H:(ow-iw)/2:(oh-ih)/2,setsar=1,fps=FPS" \
  -c:v libx264 -preset slow -b:v VBITRATE -maxrate VMAXRATE -bufsize VBUFSIZE \
  -pix_fmt yuv420p -pass 2 -c:a aac -b:a ABITRATE -ar 48000 -ac 2 \
  -map 0:v:0 -map 0:a? -movflags +faststart "OUT"

On Windows, replace /dev/null with NUL.

5) Concat and normalize mixed clips

  • Matching streams: use concat demuxer + -c copy
  • Mixed sources: re-encode with concat filter after normalizing resolution/fps/audio
  • If any clip lacks audio, generate silence first
bash
ffmpeg -y -i "IN" -f lavfi -i anullsrc=channel_layout=stereo:sample_rate=48000 \
  -shortest -map 0:v:0 -map 1:a:0 -c:v copy -c:a aac -b:a 128k "OUT"

6) Subtitle handling

Burn in (required order: get subtitle settings -> convert to ASS -> burn in):

Before any burn-in command, you must first obtain subtitle settings parameters such as:

  • font family
  • font size
  • primary color
  • outline/border color and width
  • shadow
  • alignment/position
  • margins / safe area

If subtitle masking is enabled in the current workspace, you must also obtain:

  • the exact subtitle mask filter snippet
  • the intended visual style of the mask (translucent glass-like blur, not a black rectangle)

Do not burn subtitles directly from raw .srt or other plain subtitle sources. Always convert to .ass first so style and layout are explicit and reproducible.

Required mask-aware burn-in workflow

When the workspace enables subtitle masking, the only correct workflow is:

  1. Call subtitle_settings with video_path
  2. Read the returned subtitleMaskFilter
  3. Convert subtitles to .ass
  4. Apply the returned mask filter before the .ass subtitle filter

Rules:

  • Use the subtitle_settings mask filter snippet verbatim
  • Do not replace it with drawbox, a black rectangle, or a custom gray overlay
  • The expected look is a translucent glass-like blur that keeps the background visible while making the source subtitle unreadable
  • If the user mentions the preview mask style, treat that preview style as the target appearance
  • If the burn-in result visually diverges from the preview, revise the FFmpeg parameters instead of falling back to a dark overlay

Step 1: convert subtitle source to ASS:

bash
ffmpeg -y -i "SUB_INPUT" "SUB.ass"

Step 2: burn in from the generated ASS file:

bash
ffmpeg -y -i "IN" -vf "subtitles='SUB.ass'" \
  -c:v libx264 -preset medium -crf 19 \
  -c:a aac -b:a 128k -map 0:v:0 -map 0:a? -movflags +faststart "OUT"

Mask-aware burn in (preferred when masking is enabled):

bash
ffmpeg -y -i "IN" \
  -filter_complex "SUBTITLE_MASK_FILTER;[masked]subtitles=filename='SUB.ass'[v]" \
  -map "[v]" -map 0:a? \
  -c:v libx264 -preset medium -crf 18 -pix_fmt yuv420p \
  -c:a copy -movflags +faststart "OUT"

Notes:

  • Replace SUBTITLE_MASK_FILTER with the exact filter string returned by subtitle_settings(video_path=...)
  • Do not hand-rewrite that filter unless the user is explicitly debugging the mask effect
  • Keep the mask filter and subtitle filter in a single -filter_complex pipeline
  • If audio exists and no audio edit is requested, prefer -c:a copy

Soft subtitle mux:

bash
ffmpeg -y -i "IN" -i "SUB.srt" \
  -map 0:v:0 -map 0:a? -map 1:0 \
  -c:v copy -c:a copy -c:s mov_text -movflags +faststart "OUT"

Quick Fallbacks

  • matches no streams: replace -map 0:a with -map 0:a?
  • Audio labels fail in filter_complex: do not use optional audio labels; pre-create silence
  • concat parameter mismatch: pre-normalize clips to the same resolution/fps/audio layout
  • Hardware encoder unavailable or output too large: fall back to libx264
  • Output still too large: reduce to 720p before increasing CRF
  • VFR sync drift: normalize to CFR before audio mixing or concat

Encoder Defaults

  • macOS: h264_videotoolbox, fallback libx264
  • Windows: h264_nvenc, then h264_qsv, then h264_amf, then libx264
  • Best compression efficiency: libx264 -preset slow
  • Best compatibility: -pix_fmt yuv420p -movflags +faststart

Output Contract

Always return:

  1. Exact command used
  2. Output path
  3. Chosen profile (copy, web, 540p, 720p, 1080p, or 2-pass)
  4. Why key flags were chosen
  5. Any fallback applied

Safety

  • Never overwrite source media in place
  • Prefer absolute output paths
  • Probe first when stream topology is uncertain
  • Do not claim “exactly WeChat internal compression”; call it compatibility-aligned

Frequently asked questions

What does the Ffmpeg Best Practice AI skill do?

Lean FFmpeg playbook for reliable video compression, WeChat-compatible MP4 export, clip stitching, audio mixing, subtitle handling, and quick fallback decisions. Use for requests like “压缩视频”, “微信上传”, “体积太大”, “导出更小但别糊”, “多段素材拼接”, “字幕烧录”, and “ffmpeg 报错”.

Why use Ffmpeg Best Practice on TypingMind?

Because you install it once and use it with any model. Ffmpeg Best Practice 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 Best Practice in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/linyqh/speclip-skills/tree/main/ffmpeg-best-practice. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ffmpeg Best Practice?

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 Best Practice?

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

Is the Ffmpeg Best Practice AI skill free?

It is published on GitHub by linyqh. 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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