ffmpeg-hwaccel

ffmpeg-hwaccel is a skill for Claude Code from damionrashford/media-os. It costs 121 tokens per session (4,687 once invoked), scanned A, original, MIT.

A guide to using a computer's graphics hardware to decode, process, and encode video with FFmpeg. It covers NVIDIA, Intel, AMD, Apple, Vulkan, and other acceleration systems.

In plain words
What is it for?
Use it to detect available acceleration, configure GPU video encoding or decoding, and build accelerated FFmpeg pipelines for transcoding.
Why use it?
CPU-based video conversion can take too long for large files, live streams, or batches of footage. Hardware acceleration can move parts of the work to a supported GPU or media engine.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter.

Part of the media-os plugin — 121 skills, 11 agents, 5 hooks, 1 plugin shipped together

Good fit Use it to detect available acceleration, configure GPU video encoding or decoding, and build accelerated FFmpeg pipelines for transcoding.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/damionrashford/media-os/ffmpeg-hwaccel
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add damionrashford/media-os --skill ffmpeg-hwaccel
Clone the repo
git clone --depth 1 https://github.com/damionrashford/media-os

Made for: Claude Code.

Or install media-os, the plugin that ships this one along with the rest of its 121 skills, 11 agents, 5 hooks, 1 plugin.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for ffmpeg-hwaccel

README.md
[![agentmods](https://agentmods.dev/badge/skills/damionrashford/media-os/ffmpeg-hwaccel/github.svg)](https://agentmods.dev/skills/damionrashford/media-os/ffmpeg-hwaccel)
Your own site
<a href="https://agentmods.dev/skills/damionrashford/media-os/ffmpeg-hwaccel"><img src="https://agentmods.dev/badge/skills/damionrashford/media-os/ffmpeg-hwaccel/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for ffmpeg-hwaccel

Your own site · 80×15
<a href="https://agentmods.dev/skills/damionrashford/media-os/ffmpeg-hwaccel"><img src="https://agentmods.dev/badge/skills/damionrashford/media-os/ffmpeg-hwaccel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 121 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,687 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00121 $0.04687
Opus 5 $0.00060 $0.02344
Sonnet 5 $0.00024 $0.00937
Haiku 4.5 $0.00012 $0.00469

Measured 8d ago against content hash 5d77dc0c3cb6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

ffmpeg-hwaccel scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/hwaccel.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/ffmpeg-hwaccel/SKILL.md · 298 lines

How it starts

The opening of the file, as written. The whole thing — 298 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Ffmpeg Hwaccel

Context: $ARGUMENTS

Quick start

  • I don't know what my GPU supports: → Step 1 (detection)
  • Just encode fast on whatever GPU I have:scripts/hwaccel.py transcode --accel auto
  • Zero-copy NVIDIA pipeline (decode+scale+encode on GPU): → Step 2, recipe A
  • Intel Quick Sync on Windows/Linux: → Step 2, recipe B
  • VA-API on Linux desktop: → Step 2, recipe C
  • macOS (Intel or Apple Silicon): → Step 2, recipe D
  • AMD on Windows: → Step 2, recipe E

When to use

  • You need to transcode many/long files and CPU encode is too slow.
  • You want real-time or faster-than-realtime encoding (live streaming, bulk archive conversion).
  • You explicitly want NVENC, QSV, VAAPI, VideoToolbox, AMF or Vulkan.
  • Hardware decoder (NVDEC/QSV-dec/VAAPI-dec) to offload decoding while still using CPU for something else.

If you just want best quality at the smallest size, prefer ffmpeg-transcode with libx264/libx265/libaom-av1 — hardware encoders trade quality for speed. See the gotcha about quality-per-bit below.

Step 1 — Detect what's available on this machine

# 1. Which hwaccel APIs does this ffmpeg build expose?
ffmpeg -hide_banner -hwaccels

# 2. Which HW encoders are compiled in?
ffmpeg -hide_banner -encoders | grep -E 'nvenc|qsv|vaapi|videotoolbox|amf|vulkan'

# 3. Which HW decoders / *_cuvid variants?
ffmpeg -hide_banner -decoders | grep -E 'cuvid|qsv|vaapi|videotoolbox'

# 4. Probe the NVENC / QSV / VAAPI backends
ffmpeg -hide_banner -f lavfi -i nullsrc -c:v h264_nvenc -f null -     2>&1 | head -20   # NVENC sanity
ffmpeg -hide_banner -init_hw_device qsv=hw -f lavfi -i nullsrc -c:v h264_qsv -f null - 2>&1 | head -20
vainfo                                                                                 # Linux VAAPI

Or just: uv run ${CLAUDE_SKILL_DIR}/scripts/hwaccel.py detect.

Platform cheat sheet (what to pick when auto):

OS / GPU First choice Fallback
macOS (any Mac) videotoolbox libx264
Linux + NVIDIA nvenc vaapi
Linux + Intel iGPU qsv (iHD driver) vaapi
Linux + AMD vaapi (Mesa) libx264
Windows + NVIDIA nvenc libx264
Windows + Intel iGPU qsv d3d11va decode
Windows + AMD amf libx264

Read the full file on GitHub · 298 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 298 lines · 121 tokens per session scan A 5d77dc0c3cb6

Subscribe to this mod's changes

ffmpeg-hwaccel is a skill published in the GitHub repository damionrashford/media-os (18 stars, last pushed 3mo ago), licensed MIT. It adds 121 tokens to every session and 4,687 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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