zhouxiaoka/autoclip

AutoClip|一个链接,一键出片。开源 AI 视频剪辑桌面工具,将播客、访谈、课程等长视频自动剪成短视频,生成字幕、封面和发布文案,适配抖音、小红书、TikTok、Reels 与 YouTube Shorts。Open-source AI video clipping & content repurposing.

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zhouxiaoka/autoclip has 9k+ stars and 1.5k+ forks on GitHub, is written in Python, and was last pushed on Oct 17, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 6 minutes ago
Added to GitGenius on September 9th, 2026
Created on July 8th, 2025
Open Issues & Pull Requests: 27 (+1)
GitHub issues: Enabled
Number of forks: 1,706
Total Stargazers: 9,476 (+1)
Total Subscribers: 39 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 4.4 days
Mean response time: 53.3 days
90th percentile: 232.8 days
Tracked items: 117

Maintainer activity

1 person did triage or write work on this repository in the last 12 months.

Counts unlabeled, assigned, unassigned, milestoned, demilestoned, locked, unlocked over the last 12 months. These are issue and pull request events that require triage or write permission. Commits and code review are not counted. labeled and renamed are excluded because GitHub issue forms record the issue author as the actor. Figures from October 11, 2026. This count is not comparable across projects: each project's automation decides which of these events a person emits. How this is counted.

How this project is maintained

About 5% of issues opened in the past year have never received a reply. Work labelled "from-app" is answered fastest, typically in about 7 hours, while "docker" waits about 4 weeks. 90% of issues opened in the past year have since been closed. Three people close 98% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 10
New in 7 days: 5
Closed in 7 days: 2
Avg open age: 6 days
Stale 30+ days: 0
Stale 90+ days: 0

Recent activity

Opened in 7 days: 4
Closed in 7 days: 2
Comments in 7 days: 0
Events in 7 days: 10

Top labels

  • bug (63)
  • from-app (39)
  • windows (25)
  • needs-info (19)
  • needs-triage (18)
  • question (17)
  • feature (16)
  • docker (14)

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Detailed Description

AutoClip is an AI-powered video clipping and highlight generation tool that automatically downloads videos from platforms like YouTube and Bilibili, analyzes them to extract compelling segments, and intelligently generates compilations.

The tool addresses the time-consuming manual process of finding and editing video highlights. It works by downloading videos from multiple platforms or accepting local file uploads, then using a large language model (Tongyi Qianwen) to analyze video content and identify interesting moments. The system automatically cuts these segments and can recommend or create video compilations based on the analysis. It employs a modern frontend-backend architecture with asynchronous task processing via Celery and Redis, real-time progress updates through WebSocket, and a React-based web interface.

AutoClip suits creators and content managers who regularly work with video material from YouTube or Bilibili and need to extract highlights at scale. It works best for projects where you have access to the Tongyi Qianwen API and can deploy either via Docker or locally with Python, Node.js, Redis, and FFmpeg. The tool is particularly valuable if you want to automate the discovery phase of video editing rather than manually scrubbing through footage. Several features remain in development, including mobile responsiveness, multi-account Bilibili management, automatic upload to Bilibili, and visual subtitle editing.

The project shows active development with ongoing feature expansion. Work continues on capabilities beyond core clipping functionality, indicating the maintainers are building toward a more complete video production pipeline. The codebase uses modern frameworks across both backend and frontend, suggesting attention to maintainability and developer experience. Documentation includes deployment guides for both Docker and local setups, reflecting consideration for different user environments.