# The LinkedIn Version

> *For the quick start, see [start-here.md](start-here.md).*

<img src="images/linkedin-hero.png" alt="AI Agent Network" width="100%">

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I wasn't going to post this.

But after a year of daily iteration on one system, I figured someone might find it useful.

So here goes.

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Twelve months ago I was using Claude Code the way everyone does.

Copy. Paste. Correct. Iterate. Spend more time fixing output than doing the work.

Then I stopped treating AI like a search bar and started treating it like a team with specialists.

That one architectural decision changed how I build software, moderate communities, write content, and manage research.

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**Here's what actually happened.**

A generalist doing specialist work produces generic results. The fix is routing, not a better model.

So I built a system where every request gets classified and dispatched to a domain expert.

You say "debug this Go test," a Go engineer loads with a gated debugging methodology.

You say "write a blog post about observability," a content pipeline launches parallel research agents, then generates with voice validation.

You say "review this PR," three reviewers run simultaneously. Security. Business logic. Architecture.

The router handles dispatch. You describe work. That's it.

<img src="images/linkedin-agents.png" alt="AI Agent Specialists" width="100%">

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Here are 7 things I learned building this:

🧠 **Routing is everything.** A single `/do` command classifies intent and picks the right specialist. No prompt engineering required from the user.

🚀 **Agents need methodology, not just knowledge.** Every agent pairs with a skill. TDD, systematic debugging, code review pipelines. Knowledge without process produces inconsistent results.

🔥 **Review at scale needs parallel execution.** Three specialist reviewers running simultaneously catches things one generalist never would.

💡 **Self-improvement is the real advantage.** The system captures learnings from every session and graduates them into permanent agent instructions. Every failure becomes a structural improvement.

🎯 **Hooks are underrated.** Lifecycle automation, firing at session start, after errors, before context compression, handles repetitive work without manual intervention.

⚡ **This isn't just for code.** Research pipelines. Content creation with voice matching. Community moderation. Data analysis. The agent-skill-hook pattern works for any structured workflow.

🏗️ **Compounding beats prompting.** Every session makes the next one better. That's the architecture, not a feature.

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I need to be transparent about something.

This took a year of daily iteration.

A year of daily use on real work. Finding gaps, filling them, watching the system improve itself.

The result:

- 44 domain specialist agents across languages, infrastructure, review, research, content
- 124 workflow skills covering everything from TDD to article writing to Reddit moderation
- 83 lifecycle hooks that fire at session boundaries to inject context, capture learnings, enforce gates
- A learning database that tracks patterns and graduates them into agent behavior
- Parallel review pipelines that catch issues before they reach production
- Content pipelines that handle research, drafting, voice validation end to end
- Anti-AI writing detection that keeps generated content from sounding like generated content

Was it worth it?

I type `/do` and the right specialist shows up with the right methodology loaded. I don't prompt anymore. I delegate.

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Most people miss this about AI tooling.

The gap between AI-assisted and AI-native isn't about the model.

It's about the scaffolding around it.

Agents that know your domain. Skills that enforce methodology. Hooks that automate the repetitive parts. A learning system that turns every session into improvement.

Your `.claude/` directory might be the most underused part of your development workflow. This toolkit fills it with a team.

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The repo is open source. MIT licensed. Three commands to install:

```
git clone https://github.com/notque/vexjoy-agent.git
cd vexjoy-agent
./install.sh
```

It works with any project, any language, any workflow.

If this resonated:

⭐ Star the repo, it helps others find it

🔀 Fork it and build your own agents

💬 I'm always happy to discuss how agentic workflows change the way teams operate

<img src="images/linkedin-flywheel.png" alt="The Compounding AI Flywheel" width="100%">

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You don't need a better model.

You need a better system.

Agree? 👇

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*#AI #ClaudeCode #AgenticAI #OpenSource #DevTools #BuildInPublic #LLMOps #AIEngineering*
