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Video content extractor

Skill sickn33/agentic-awesome-skills/skills/video-content-extractor

Extract key frames from MP4 videos at configurable intervals, run Tesseract OCR, and generate structured Markdown reports with video metadata and timestamped text transcripts.From its SKILL.md

Install
npx -y skills add sickn33/agentic-awesome-skills --skill video-content-extractor

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

4.2 KB, 765 tokens by cl100k_base, as published. Nobody here has run it

Video Content Extractor

Overview

Automatically extracts key frames from MP4 video files at configurable time intervals, performs OCR text recognition on each frame, and generates a structured Markdown report. The report includes video metadata (duration, resolution, codecs) and frame-by-frame OCR transcripts with timestamp references.

This skill is designed for Codex CLI and requires FFmpeg and Tesseract OCR installed on the local machine.

When to Use This Skill

  • Use when you need to extract text content from video presentations, lectures, or screencasts.
  • Use when you want to create searchable transcripts from video files without embedded subtitles.
  • Use when you need to analyze video content programmatically and generate structured summaries.
  • Use when the user asks to "read what is on screen" or "extract the content from this video."

How It Works

Step 1: Analyze Video Metadata

The skill uses ffprobe to extract video metadata: duration, resolution, frame rate, codec information, and file size.

Step 2: Extract Key Frames

Using FFmpeg, the skill captures frames at the configured interval (default: every 30 seconds). Each frame is saved as a timestamped JPEG image.

Step 3: OCR Text Recognition

Each extracted frame is processed by Tesseract OCR. If the default PSM mode returns no meaningful text, it falls back to fully automatic page segmentation.

Step 4: Generate Markdown Report

All extracted data is assembled into a structured Markdown document.

Examples

Example 1: Basic Extraction

Agent prompt: Use the video-content-extractor skill to extract content from lecture.mp4

Output generates lecture.md and lecture_frames/ directory.

Example 2: Custom Interval

Parameters: video_path, output_dir, interval(seconds), lang Extract every 60 seconds with English-only OCR: python scripts/extract_video.py recording.mp4 ./output 60 eng

Example 3: Bilingual Content

Extract with default Chinese + English OCR: python scripts/extract_video.py lecture.mp4 . 15 chi_sim+eng

Best Practices

  • Use shorter intervals (10-15s) for fast-paced content with frequent text changes.
  • Use longer intervals (30-60s) for presentation slides or slow lectures to reduce duplicate frames.
  • For Chinese content, ensure Tesseract Chinese language pack is installed (chi_sim).

Limitations

  • Requires FFmpeg and Tesseract OCR to be installed and accessible via PATH.
  • Tesseract OCR accuracy depends on video quality, text size, and font clarity.
  • Does not extract audio or perform speech-to-text transcription.
  • Frame extraction is time-based (not scene-change-based), which may produce near-duplicate frames.
  • Large videos with short intervals can generate many frames - ensure sufficient disk space.

Security and Safety Notes

  • This skill only reads video files and writes extracted frames and Markdown reports.
  • It does NOT send any data over the network - all processing is local.
  • FFmpeg and Tesseract are invoked with fixed, pre-vetted arguments.
  • The skill does not modify or delete the original video file.

Common Pitfalls

  • Problem: Tesseract returns garbled text Solution: Ensure the correct language pack is installed. Run tesseract --list-langs to verify.

  • Problem: FFmpeg fails with "not found" Solution: Make sure FFmpeg is on PATH. Run ffmpeg -version to verify.

  • Problem: OCR is slow on large videos Solution: Increase the interval parameter to reduce frames processed.

Related Skills

  • @media-summarizer - For summarizing video content using visual and audio cues.
  • @document-ocr - For OCR on static images or scanned documents without video processing.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most video audio skills give in 765 tokens

Counted across 619 of the 725 authors here whose files we hold, read 2026-09-06

  • Read product marketing context firstin 13 of 619, across 7 files
  • Define the core visual thesis in one sentencein 11 of 619, across 3 files
  • Break the concept into 3 to 6 scenesin 11 of 619, across 3 files
  • Render the smallest working version firstin 11 of 619, across 3 files
  • Start with a low-quality smoke test renderin 11 of 619, across 3 files
  • Add captions for accessibility and engagementin 11 of 619, across 5 files
  • Write the scene outline before writing codein 11 of 619, across 3 files
  • Specify subject, action, camera, style, and moodin 11 of 619, across 5 files
  • Decide what each scene provesin 10 of 619, across 2 files
  • Export one clean thumbnail framein 10 of 619, across 2 files
  • Pick the right tool for the jobin 10 of 619, across 4 files
  • Run the test suite before proposing a fixin 8 of 619, across 7 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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