How does this skill differ from simply asking a general-purpose AI to write an FFmpeg command?

This skill generates optimized FFmpeg commands specifically for high-precision tasks like dataset creation and contact sheet generation, avoiding the common black-frame and slow-seeking errors found in generic AI outputs.

Does this skill require any additional software to be installed on my computer?

You will need the FFmpeg command-line utility installed on your local machine or server, as this skill generates the precise scripts and commands to run within that environment.

Can I use the generated commands on both Windows and Mac systems?

Yes, the skill is designed to generate syntax compatible with Bash (Linux/macOS) and PowerShell (Windows), ensuring commands work regardless of your operating system.

Is it possible to process multiple video files at once using this skill?

Absolutely; the skill can generate shell or Python wrappers designed to loop through entire directories, making it ideal for preparing large-scale machine learning datasets.

How does the skill handle frame accuracy for videos with complex codecs?

The skill generates commands that utilize 'fast seeking' (input seeking) or 'accurate seeking' (output seeking) based on your specific needs for speed versus frame-perfect precision.

Video Frame Extractor skill

Example session with this skill installed

I need to create a 4x4 contact sheet from 'demo.mp4'. Extract a frame every 30 seconds, scale them to 320px wide, and save as 'summary.jpg'.

Read your context and instructions
Compiled the video frame extractor
Generated the image

I will generate an FFmpeg command to create a 4x4 contact sheet from your video by sampling one frame every 30 seconds and tiling them into a single JPEG.

Video Frame Extraction Plan

1. Task Summary

Create a 16-frame contact sheet (4x4 grid) from demo.mp4 with a sampling interval of 30 seconds and a thumbnail width of 320px.

2. Ready-to-Run Command

ffmpeg -i "demo.mp4" -vf "fps=1/30,scale=320:-1,tile=4x4" -frames:v 1 "summary.jpg"

3. Output Details

Filename: summary.jpg
Format: JPEG Image
Layout: 4 columns by 4 rows
Thumbnail Size: 320px width (aspect ratio preserved)

4. Important Options

fps=1/30: Extracts one frame every 30 seconds.
scale=320:-1: Resizes each frame to 320px wide; -1 keeps the height proportional.
tile=4x4: Arranges the extracted frames into a grid.
-frames:v 1: Tells FFmpeg to output exactly one image file (the grid).

5. Validation

Run ls -lh summary.jpg to verify the file exists. Open the image to ensure all 16 slots in the grid are populated. If the video is shorter than 8 minutes (16 * 30s), some grid spots will be empty.

6. Troubleshooting

Empty Output: Ensure the video duration is at least 30 seconds.
Command Not Found: Verify FFmpeg is installed by running ffmpeg -version.
Scaling Error: If the input resolution is odd, change scale to scale=320:240 or similar even numbers.

Next steps

Run the provided command in your terminal.
Adjust the fps=1/30 value if you need more or less frequent samples.
Change tile=4x4 to a different layout (e.g., tile=8x2) if the grid aspect ratio looks off.

summary.jpg

PNG · 1536×1024

Generated

Example file from a real run - the skill writes it into your workspace.

Connects securely to your tools. The creator never sees your data.

About this skill

Manually processing video files for machine learning datasets, UI mockups, or content summaries is tedious and error-prone. This skill automates the generation of precise FFmpeg commands to handle frame extraction and clip cutting without damaging source files.

What it does

Interval extraction captures frames at specific time steps or frame rates for dataset generation.
Timestamp capture grabs exact stills from specific video coordinates with high precision.
Contact sheets generates tiled thumbnail grids for quick visual indexing of long videos.
Precision clipping cuts segments using either duration-based or start/end point methods.
Script generation builds lightweight shell or Python wrappers for batch processing tasks.

How it works

Identify task by specifying if you need intervals, specific timestamps, or a tiled contact sheet.
Configure parameters like output format (PNG/JPG), scale, quality, and grid dimensions.
Generate command to receive a ready-to-run FFmpeg string tailored for your OS and shell.
Test and scale using the provided validation steps before running large batch jobs.

Frameworks & tools

This tool targets the FFmpeg command-line utility. It generates commands and scripts compatible with Bash, Zsh, and Windows PowerShell environments.

Why this beats prompting it yourself

Generic AI prompts often produce incorrect FFmpeg flag ordering or omit critical seeking parameters, leading to slow processing or black frames. This skill implements specific patterns for fast seeking and accurate tile filtering that account for codec complexities.

Use cases

Generating image sequences for training computer vision models.
Creating high-quality preview thumbnails for web video players.
Extracting specific frames for technical documentation and bug reports.
Creating visual summaries of long-form security or research footage.

Known limitations

Requires FFmpeg to be pre-installed on the host system. Exact frame accuracy depends on the source codec and seeking method used.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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Video Frame Extractor skill