theagenticguy

theagenticguy / erpaval

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ERPAVal — autonomous software development. Six-phase Explore/Research/Plan/Act/Validate/Compound workflow with classifier-driven routing and a compounding lessons store.

agentic-engineering agentic-sdlc ai-engineering autonomous-agents claude-code
15
1
100% credibility
Found May 12, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This project is a structured workflow that guides AI coding agents through phases of exploration, research, planning, execution, validation, and knowledge compounding to build complex software reliably.

How It Works

1
🔍 Discover ERPAVal

You hear about this smart helper that makes your AI coding buddy handle big projects like a pro team, remembering what it learned before.

2
📥 Add the helper

In your AI coding session, you easily add this workflow tool from the add-ons list with a quick note.

3
💬 Describe your goal

Tell your AI what you want to build, like adding a new feature across several parts of your project.

4
Simple tweak or big build?
✨
Quick fix

AI makes the small change right away and you're done fast.

🗺️
Full project

AI dives into a structured journey to build it properly.

5
🚀 AI plans and builds

You watch as your AI explores your code, researches best ways, crafts a detailed plan, builds piece by piece, checks everything, and saves smart lessons.

6
✅ Review and approve

You peek at the clear plan, make any tweaks, and give the go-ahead feeling confident.

🎉 Project complete and wiser

Your new feature works perfectly, and your AI is now sharper for the next project thanks to saved wisdom.

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Star Growth

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AI-Generated Review

What is erpaval?

ERPAVal is a Python plugin for Claude Code that automates complex software development through a six-phase explore/research/plan/act/validate/compound workflow. It uses classifier-driven routing to skip simple one-file fixes and dive deep into multi-module tasks, while a compounding lessons store captures durable insights from each session for smarter future runs. Developers get structured AI agents that parallelize work, enforce human design reviews, and validate code across static, quality, and security layers.

Why is it gaining traction?

It stands out by closing the memory gap in AI coding—prior lessons auto-inject into new tasks, cutting rework on codebase quirks or API patterns. Classifier-driven routing and eager unblocking speed up complex features by 30-40% via parallel subagents, with mechanical gates ensuring progress without endless loops. Users notice fewer "plausible but wrong" changes, plus easy forking to vendor team conventions.

Who should use this?

Backend Python devs integrating unfamiliar APIs or refactoring across modules in Claude Code sessions. Solo engineers or small teams handling greenfield stacks or rip-and-replace rebuilds, where upfront planning saves hours. Avoid for quick scripts—classifiers route those directly.

Verdict

Fork and experiment on a side project; 15 stars and 1.0% credibility signal early maturity, but thorough docs and plugin hooks deliver immediate value for Claude Code users. Production-ready after custom tweaks.

(198 words)

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