midudev/autoskills

One command. Your entire AI skill stack. Installed.

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Summary Information

Updated 26 minutes ago
Added to GitGenius on September 1st, 2026
Created on March 25th, 2026
Open Issues & Pull Requests: 33 (+0)
GitHub issues: Enabled
Number of forks: 625
Total Stargazers: 6,936 (+0)
Total Subscribers: 25 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 32.5 hours
Mean response time: 5.5 days
90th percentile: 9.6 days
Tracked items: 41

How this project is maintained

About 18% of issues opened in the past year have never received a reply. 72% of issues opened in the past year have been closed, leaving a working backlog. Three people close 95% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 14
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 124 days
Stale 30+ days: 14
Stale 90+ days: 14

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • enhancement (21)
  • bug (16)
  • more info provided (1)

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Detailed Description

autoskills is a command-line tool that scans your project, detects your technology stack, and automatically installs curated AI agent skills.

The tool solves the problem of manually discovering and configuring AI skills for your specific tech stack. It works by running a single command in your project root, scanning configuration files like package.json and Gradle files to identify which technologies you use, then selecting matching AI agent skills from a curated registry. Only the relevant skill files are downloaded, verified against SHA-256 hashes in a manifest, and written locally to your project.

The tool suits developers working across modern frontend, backend, mobile, cloud, and media stacks who want AI assistance tailored to their specific technologies without manual configuration. It works with a broad range of frameworks and tools including React, Next.js, Vue, TypeScript, Node.js, Go, Express, NestJS, Spring Boot, Flutter, Prisma, Vercel, Cloudflare, and many others. The security model distinguishes it from alternatives by avoiding live downloads from third-party sources at runtime; instead, skills are pre-synced into a repository-local registry where maintainers scan them for prompt-injection and supply-chain risks before recording them with hashes in a manifest.

Development activity shows consistent engagement with the codebase through regular commits addressing both features and fixes. The project maintains an active issue tracker with prompt responses to user reports and feature requests. Pull requests receive timely review and feedback, indicating active maintenance. Documentation is kept current alongside code changes, and the maintainers actively incorporate community contributions into releases.