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Production-grade engineering skills for AI coding agents.
This repository page has enough original analysis, source evidence, and comparison context to be treated as an indexable Git-Stars review.
100
review score
Problem solved
It addresses the inconsistency and lack of engineering rigor in AI-generated code by embedding explicit workflows and quality gates into agent behavior. Unlike generic prompting, it provides structured, repeatable processes that enforce best practices like TDD, code review, and performance measurement, reducing the need for manual oversight.
Deployment reality
The available setup signal starts with: npx skills add addyosmani/agent-skills. Treat this as a starting point, then ask Codex or Claude to inspect the README, environment variables, runtime version, and deployment target before production use.
Commercial use
The recorded license is MIT. This is a useful commercial-use signal, but teams should still verify license text, dependencies, model/API terms, and trademark constraints.
Capability ceiling
Developers can build full software projects with AI agents that autonomously plan, implement, test, review, and ship code while maintaining quality standards. Real-world use cases include rapid prototyping, feature development with enforced TDD, automated code review before merge, and web performance optimization. The ceiling is high: with /build auto, agents can execute an entire task list with minimal human intervention, pausing only on failures or risky steps, making it suitable for production-level development.
Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.
addyosmani/agent-skills is tracked as a JavaScript project in the AI Agent area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.
Momentum check: the repository has 88k total stars, with +0 today, +3.3k this week, and +0 this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.
Maintenance check: current activity is Active; the latest push was 8 days ago, and the open issue queue is 103, about 0.12% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.
Adoption check: 9.4k forks and 88k watchers suggest how often the project is reused or followed. License signal: MIT. Always verify license compatibility before commercial or internal use.
Practical fit: this project is most relevant when you need JavaScript teams evaluating ecosystem-native tooling. Be cautious when you need a legal review, security audit, or production SLA.
Source check: Git-Stars currently has 2 explicit source reference(s) for this report and a recent growth signal of 5.5k. Follow the original GitHub repository for final install, security, and release information.
Analysis
ReadyOriginal problem, capability, and verdict guidance are available.
Sources
ReadyRepository metadata and README/source references are attached.
License
ReadyMIT is recorded for review.
Maintenance
ReadyRecent activity is visible in repository metadata.
Alternatives
ReadyEnough nearby projects exist for comparison.
Momentum
88k Stars
Reuse
9.4k Forks
Attention
88k Watchers
Maintenance
active
License
MIT
Open issues
103
Agent Skills is a collection of production-grade engineering skills for AI coding agents, encoding the workflows, quality gates, and best practices that senior engineers use. It provides slash commands and automatic skill activation to guide agents through the development lifecycle from spec to ship.
Key Features
- 8 slash commands mapping to the development lifecycle (spec, plan, build, test, review, webperf, code-simplify, ship) - Automatic skill activation based on the task (e.g., API design, frontend UI) - Works with 70+ agents via the skills CLI, plus native integrations for Claude Code, Cursor, Gemini CLI, and more
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
npx skills add addyosmani/agent-skills90
Health Score
Active
Commit Activity
Feb 15, 2026
Created
Aug 14, 2026
Last push
+222
Today's growth
+3.0k
7-day growth
+5.5k
30-day growth
Forks
Open
Watchers
addyosmani
GitHub profileobra/superpowers
An agentic skills framework & software development methodology that works.
affaan-m/ECC
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
NousResearch/hermes-agent
The agent that grows with you
mattpocock/skills
Skills for Real Engineers. Straight from my .agents directory.
anomalyco/opencode
The open source coding agent.
✓
License
✓
Forked
✓ Active
Maintained
Problem Solved
It addresses the inconsistency and lack of engineering rigor in AI-generated code by embedding explicit workflows and quality gates into agent behavior. Unlike generic prompting, it provides structured, repeatable processes that enforce best practices like TDD, code review, and performance measurement, reducing the need for manual oversight.
Capabilities
Developers can build full software projects with AI agents that autonomously plan, implement, test, review, and ship code while maintaining quality standards. Real-world use cases include rapid prototyping, feature development with enforced TDD, automated code review before merge, and web performance optimization. The ceiling is high: with /build auto, agents can execute an entire task list with minimal human intervention, pausing only on failures or risky steps, making it suitable for production-level development.
Bottom Line
This is ideal for developers and teams who want to leverage AI coding agents for production work without sacrificing code quality, especially those using Claude Code, Cursor, or similar tools. It's less suitable for those who prefer full manual control or who need a lightweight, non-opinionated assistant. The key trade-off is the upfront setup and the need to trust the agent's adherence to the encoded workflows.