Do I need a powerful GPU to use this pack?

Yes. The pipeline is tuned for a CUDA GPU with at least 8 GB VRAM (validated on an RTX 3070 Ti). CPU-only inference is not supported.

Are there any API keys or subscriptions required?

No. All engines (SadTalker, LivePortrait, Wav2Lip, XTTS-v2, GFPGAN) run locally. There is no SaaS subscription, no cloud bill, and no API key to configure.

Will this work on Windows, macOS and Linux?

Yes. The scripts are cross-platform (Python 3.11+). The validated reference environment is Windows 11 with CUDA, but the FFmpeg and Python commands work on Linux and macOS (Intel/Silicon) with equivalent GPU support.

Works with the AI tools you already use

+22 more

+18 agents

Hermes Video Automation Pack

Turn a single GPU workstation into an AI video studio.

$19

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Hermes Video Automation Pack

Example session with this skill installed

I have a 1024x1024 portrait and a 25fps driving video. I need to generate a 16:9 YouTube video using LivePortrait, including a background loop and a logo at 30fps.

Read your context and instructions
Compiled the hermes video automation

The video pipeline has been initialized. I have stabilized the driving video, prepared the 16:9 blurred background portrait, and generated the FFmpeg assembly script to ensure the output reaches exactly 30fps without truncating the source duration.

AI Video Pipeline Output


# 1. Prepare 16:9 Portrait with blurred background
ffmpeg -y -i source_image.jpg \
  -filter_complex "[0:v]scale=1920:1920,boxblur=40:5,crop=1920:1080[bg]; [0:v]scale=900:900:flags=lanczos[fg]; [bg][fg]overlay=(W-w)/2:(H-h)/2[out]" \
  -map "[out]" portrait_16_9.png

# 2. LivePortrait Inference (Optimized for 8GB VRAM)
python inference.py --source_image portrait_1024_square.png \
  --driving_video driving_smoothed.mp4 \
  --flag_stitching --flag_do_crop --flag_relative_motion --source_max_dim 512 \
  --output_dir ./results/

# 3. Final YouTube Assembly
# Note: -r 30 is applied to output only to prevent duration drift
ffmpeg -y -i background_loop.png -i animated_face.mp4 -i logo.png -i audio.wav \
  -filter_complex \
  "[0:v]scale=1920:1080,loop=loop=-1:size=3262[bg]; \
   [1:v]scale=-1:1080[face]; \
   [bg][face]overlay=(W-w)/2:(H-h)/2[bgface]; \
   [2:v]scale=120:-1,colorchannelmixer=aa=0.85[logo]; \
   [bgface][logo]overlay=W-w-30:H-h-30[v]" \
  -map "[v]" -map 3:a -c:v libx264 -preset slow -crf 18 -r 30 -shortest final_youtube.mp4

Next steps

Run nvidia-smi to ensure at least 7GB of VRAM is available before starting Phase 2.
Verify the final final_youtube.mp4 duration matches the source audio using ffprobe.
If jitter persists in the face overlay, re-run the stabilization filter on the driving source.

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About this skill

Hermes Video Automation Pack

Turn a single GPU workstation into a full AI video studio — no camera, no studio, no cloud subscription.

What it does

Five production-ready Hermes Agent skills that clone a voice, animate a portrait, and assemble a delivery-ready video with FFmpeg. Every stage runs locally on a consumer GPU (8 GB VRAM profile included).

ai-video-pipeline — end-to-end inference and assembly, tuned for 8 GB VRAM
liveportrait-pipeline — realistic face animation, GPU tuning, blurred-background composite
talking-head-video — SadTalker + LivePortrait + Wav2Lip + face upscale (GFPGAN)
video-youtube-assembly — background + logo + title + audio mux in one FFmpeg pass
tts-voice-cloning — natural French voice cloning with XTTS-v2 (Coqui)

Why use this pack

Generic video tutorials assume unlimited VRAM, an internet connection, and a SaaS subscription. This pack assumes the opposite: 8 GB VRAM, fully local, no recurring cost. Every command has been validated on a real workstation, with the pitfalls, VRAM limits, and known fixes documented per skill.

The output is a repeatable pipeline — change the audio, re-run, get a new talking-head video. No camera, no reshoot, no re-editing.

How to use it

Install Hermes Agent and drop the pack into your skills folder.
Prepare a square portrait and a narration audio file.
Ask your agent to run the pipeline, or run it manually through the 5 phases documented in each SKILL.md.
Inspect the final file with ffprobe before delivery.

Requirements

Hermes Agent v0.21+
Python 3.11+
FFmpeg on PATH
A CUDA GPU (validated on RTX 3070 Ti, 8 GB VRAM)
No API keys required — everything runs locally

Security

Scanned and cleared against the Agensi 8-point security review:

No secrets, no telemetry, no obfuscation
Network access limited to the local gateway (127.0.0.1) and a user-initiated file upload (gofile.io) for files over the Telegram size cap

License

Changelog

v1.0.1 — Added the missing liveportrait-troubleshooting.md reference (ONNX/CUDA conflict on Windows), cleaned 3 dead file references, fixed .env.example consistency.

Contact

Thomas Leroyer — searching.murphy@gmail.com https://www.agensi.io/@thomas-leroyer

How to install

Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

~30 seconds

1
Download the ZIP
Free skills download straight away. Paid skills unlock right after purchase.
2
Unzip into your skills folder
Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.
3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.

Skills folder by agent

Click the path to copy it. Create the folder if it does not exist yet.

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Hermes Video Automation Pack