--- name: video-content-extractor description: "Extract key frames from MP4 videos at configurable intervals, run Tesseract OCR, and generate structured Markdown reports with video metadata and timestamped text transcripts." category: media-processing risk: safe source: community source_repo: 274326424/video-content-extractor source_type: community date_added: "2026-06-06" author: 274326424 tags: [video, ocr, ffmpeg, tesseract, frame-extraction, media] tools: [codex] --- ## Supporting material availability This catalog copy contains the inline guidance below, but does not include the following supporting paths. They are external prerequisites, not bundled files: - `scripts/extract_video.py` Obtain the required material from the [declared upstream source](https://github.com/274326424/video-content-extractor), or from a user-provided installation of that source. Resolve each path relative to that installation, inspect its contents, and verify any applicable license and execution prerequisites. Do not run a command or claim a referenced procedure was followed until its required file exists. Do not fabricate the missing scripts or treat an unrelated file with the same name as equivalent. Independent steps fully documented here can proceed within the user’s authorized scope; report any dependent step as unavailable. # 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. --- Source attribution: FrancoStino Original project: https://github.com/FrancoStino/opencode-skills-collection/blob/9a4d25d6b2b25441e049bb2ccb479d0f9fa67c70/bundled-skills/video-content-extractor/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2026 Davide Ladisa Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Dave AI resource metadata Creator / publisher: FrancoStino Original project: https://github.com/FrancoStino/opencode-skills-collection/blob/9a4d25d6b2b25441e049bb2ccb479d0f9fa67c70/bundled-skills/video-content-extractor/SKILL.md Attribution basis: Source repository record Fields of work: Content & Media, Marketing Roles: Content Marketer, Copywriter, Video Producer Specialties: API Tools: Ffmpeg, Tesseract Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 91458b9be126b11cf95f68c3dcdedbaec60c52b4499642d397a9286e94b1cfd2