claude-video

Product Introduction

1. Definition: Claude-video is an open-source AI video analysis tool and Claude AI plugin that enables the Claude language model to process, understand, and answer questions about video content. It functions as a multimodal data pipeline, technically categorized as a video-to-text and visual question-answering (VQA) orchestration layer.
2. Core Value Proposition: It solves the critical problem of AI models being unable to directly interpret visual and auditory media. By automating video download, intelligent frame extraction, and audio transcription, it packages video context for Claude AI, allowing users to query, summarize, and analyze video content programmatically without manual review.

Main Features

1. Intelligent Frame Extraction & Deduplication: The tool uses ffmpeg to extract frames based on configurable detail modes: efficient (fast keyframes), balanced (scene-change detection), and token-burner (uncapped scene detection). A built-in deduplication algorithm scales frames to 16x16 grayscale thumbnails and computes mean absolute difference, dropping near-identical frames (e.g., static slides) to optimize token usage and cost. The auto-FPS logic caps sampling at 2 fps and allocates frame budgets based on video duration.
2. Multi-Source Transcription Pipeline: It first attempts to extract free, native captions (manual or auto-generated) via yt-dlp. If unavailable, it falls back to Whisper AI transcription, prioritizing the Groq API (whisper-large-v3) for speed and cost, with OpenAI's whisper-1 as an alternative. The process extracts a mono 16 kHz, 64 kbps MP3 audio clip (~480 kB/min) for the API call, producing a timestamped transcript.
3. Universal Video Input & Focused Analysis: Supports input from any URL compatible with yt-dlp (YouTube, TikTok, Loom, Vimeo, Instagram, etc.) or local file paths (.mp4, .mov, .mkv). Users can specify --start and --end parameters for focused analysis, which increases frame density for a specific segment, providing higher accuracy for targeted moments like bug reproductions or key statements.

Problems Solved

1. Pain Point: The inability of text-based LLMs like Claude to access the visual and auditory information within videos, forcing reliance on often inaccurate or missing metadata, titles, or manually provided transcripts.
2. Target Audience: Developers debugging UI issues from screen recordings; Content Analysts & Marketers dissecting competitor videos, ad creative, and viral content structure; Researchers & Students summarizing educational content or lecture playlists into searchable notes; Product Managers extracting feature details from launch or update videos.
3. Use Cases: Analyzing the hook and structure of a competitor's viral YouTube video; diagnosing a software bug from a user's screen recording (bug-repro.mov); summarizing a long tutorial or podcast into concise notes; extracting the substantive feature list from a hype-heavy product launch video; querying specific events within a video (e.g., "what happens at the 2:30 mark?").

Unique Advantages

1. Differentiation: Unlike simple transcript fetchers, Claude-video provides multimodal context by delivering both visual frames and audio transcript. Compared to manual video review or generic screen capture tools, it automates the entire pipeline from URL to AI-ready context with intelligent optimization for token cost and speed.
2. Key Innovation: Its "smart frame budget" system and deduplication logic directly address the high cost of image tokens in multimodal AI. By dynamically selecting keyframes or scene changes and removing duplicates, it maximizes informational value per token. The tiered --detail system (transcript, efficient, balanced, token-burner) gives users precise control over the fidelity-cost trade-off.

Frequently Asked Questions (FAQ)

1. How much does Claude-video cost to use? The core tool is free and open-source (MIT license). Costs are incurred from AI usage: Claude API tokens for processing images/text and, if needed, Whisper API credits (preferably via low-cost Groq) for transcribing videos without native captions.
2. Does Claude-video work with local video files? Yes, Claude-video supports local file paths (e.g., ~/Videos/recording.mp4). For files without embedded subtitles, it will use the Whisper API fallback to generate a transcript.
3. What video platforms are supported by the Claude AI video plugin? It supports all platforms compatible with yt-dlp, including YouTube, Vimeo, TikTok, Twitter/X, Instagram, Loom, and hundreds more. It handles both public URLs and private links where yt-dlp has access.
4. How does Claude-video handle long videos to avoid high token costs? For videos over 10 minutes, the default balanced mode caps frames at 100, leading to a sparse scan. It warns the user and recommends using --start/--end for focused analysis or switching to --detail token-burner for uncapped, comprehensive coverage at a higher token cost.
5. Is an API key required to use the Claude video analysis tool? An API key is only required for the Whisper transcription fallback (Groq or OpenAI). Many public videos have free captions, which the tool uses first. You can run it with --no-whisper to disable transcription entirely and rely solely on frame analysis.