Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for AI-coding power users, agent developers, automation builders, knowledge workers, technical writers, AI-curious readers, and any developer who wants an AI agent to analyze video content end-to-end -- and who can pair bradautomates/claude-video with an Anthropic / OpenAI API key for the model surface, Claude Code / Codex / Cursor / Copilot / Gemini CLI / 50+ SKILL.md-compatible agents for
Who should use it
Who should skip it
Move on from bradautomates/claude-video if the licensing terms, language support, or platform requirements do not fit your project.
About this signal
bradautomates/claude-video is tracked by RepoRadar as a claude code / codex / cursor / c in the AI Agent Video Skill section. It was first seen on 2026-07-07 and last updated on 2026-07-07. The current verdict is 'try now' with a Gold tier and easy setup difficulty. Across RepoRadar's eight signals, bradautomates/claude-video is strongest on workflow potential (9.9) and novelty (9.0) and weakest on maturity (6.6) — a profile worth weighing against your own priorities. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned bradautomates/claude-video a composite score of 8.4 out of 10, placing it in the Gold tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 1.0 and never affects the composite score or tier. The risk label of 'low' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to vet an AI agent or MCP server before you wire it in for the checklist behind this score.
Risk explanation
The 4; 412* / 616-fork repo is at active maintenance but the Whisper API fallback adds OpenAI cost for long videos without captions -- treat the first evaluation cycle as a smoke test (install via `/plugin marketplace add` or `npx skills add` + run `/watch <youtube-url> summarize` on a captioned video + confirm zero cost) before relying on the video skill in production; the scene-aware frame extraction (frames.py) burns context on frame analysis -- the consumer SHOULD review the frame-selection logic before deploying to a production video-analysis workflow; the four use-case patterns (analyze content + diagnose bug + summarize + cut hype) cover a wide surface -- the consumer SHOULD decide which patterns to use before deploying.