The Problem
AI coding agents are powerful, but they keep hitting the same wall: they don't have the right tools for the job.
Your agent can write code, but can it:
- Fix a broken brew install that just broke Node?
- Record a terminal demo for GitHub?
- Diagnose macOS memory pressure?
- Deploy an iOS app from the command line?
- Post a Reddit thread correctly (AutoMod-aware)?
Most agents can't. Because these tasks need skills — reusable, composable units of capability that an agent can load and execute.
I spent the last few weeks building 25 such skills. Here's what I learned.
What Are Agent Skills?
Think of skills as functions your agent can call — but smarter. Each skill is a Markdown file that teaches your agent:
- When to use this skill
- How to execute it (step-by-step with exact commands)
- What pitfalls to avoid
- How to verify it worked
The 25 Skills — Organized by Category
DevOps & macOS (14 skills)
These cover the infrastructure work every agent developer dreads:
| Skill | What It Does |
|---|---|
| fix-brew-node-dylib-mismatch | Auto-fix Node tooling when brew upgrades break dylibs |
| macos-ram-swap-analysis | Diagnose memory thrashing on macOS |
| ios-app-build-automation | Build iOS apps from CLI (XcodeGen + xcodebuild) |
| self-regulation-brake-system | Stop agent loops with configurable pressure escalation |
| github-demo-recording | Auto-detect and record terminal/GUI demos |
| github-exposure-standard | Format GitHub projects for public release |
| install-tool-with-verification | Install tools with checksum verification |
| macos-backup-overwrite-only | Daily backup that never duplicates |
| reddit-post-launch-workflow | Handle AutoMod, flair rules, karma thresholds |
| social-media-account-registration | Set up accounts across platforms |
| webhook-subscriptions | Event-driven webhooks for agents |
| ai-news-dharma-monitor | Daily AI news monitoring |
| open-notebook-docker-ollama-setup | Deploy local LLM stack |
| hermes-cron-job-scripts | Debug cron job pipelines |
Desktop Automation (3 skills)
| Skill | What It Does |
|---|---|
| cua-driver-install-macos | Install + configure Cua Driver |
| Router Learning System | Auto-selects CLI/CDP/Desktop Automation per task |
| auto-gui-debug-loop | Vision AI + Cua Driver visual debugging |
Web & Browser (3 skills)
| Skill | What It Does |
|---|---|
| cua-browser-github-oauth | Complete GitHub OAuth via browser |
| desktop-browser-automation-cua | Drive Chrome/Safari via Cua Driver |
| social-media-content-automation-via-browser | Post to multiple platforms |
WeChat Mini Program (5 skills)
| Skill | What It Does |
|---|---|
| cocos-creator-install-and-debug | Install + debug Cocos Creator |
| wechat-devtools-gray-screen-debug | Fix gray screen in DevTools |
| miniprogram-ux-feedback-loop | Process UX feedback systematically |
| wx-merit-shop-system | Merit shop purchase system |
| cocos-ts-compile-check-sop | TypeScript compile check |
The Router Learning System
The most interesting piece is the Router. It solves a fundamental problem: your agent has many tools but does not know which to use.
The Router defines task types and maintains fallback paths:
Path 1: CLI (fastest, 0.1s)
Path 2: CDP browser automation (0.5s)
Path 3: Cua Driver desktop automation (1.5s)
Path 4: Vision AI analysis (2.5s)
After 5+ executions, it reorders paths by success rate. Paths with 3+ consecutive failures are skipped. Self-improving, zero manual tuning.
Try It Yourself
All MIT licensed. Clone and drop the skills into your agent's skill directory:
- Core Skills: github.com/chrislamlayer1-gif/hermes-core-skills
- DevOps Skills: github.com/chrislamlayer1-gif/hermes-devops-skills
- Desktop Automation: github.com/chrislamlayer1-gif/cua-desktop-automation-skills
- PR to Cua Driver: github.com/trycua/cua/pull/2072
If you are building AI agents — for coding, testing, or deployment — this pattern is worth adopting.
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