phodal

phodal / entrix

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A Harness Engineering tool for turning quality rules, architecture constraints, and validation steps into executable guardrails.

19
1
89% credibility
Found Mar 23, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Entrix is a tool for embedding automated quality checks, risk detection, and review triggers into software development workflows to maintain code fitness.

How It Works

1
📖 Discover Entrix

You hear about a helpful tool that keeps your team's code changes safe and high-quality, especially with fast AI-generated updates.

2
🛠️ Add it to your project

You easily bring the tool into your work space so it's ready to help right away.

3
📝 Set your quality rules

You jot down simple guidelines in a notes folder about what makes a good change, like passing tests or staying simple.

4
🔍 Run your first check

You tell it to review recent updates and instantly see a score showing how solid they are.

5
⚠️ Spot risky changes

It flags any big or suspicious updates that might need a closer look from the team.

6
🔄 Make it automatic

You set it to check changes every time, so quality stays high without extra effort.

🎉 Build confidently

Your team now releases reliable updates faster, with fewer surprises and more trust in every change.

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Star Growth

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AI-Generated Review

What is entrix?

Entrix is a Python CLI tool that turns quality rules and architecture constraints into automated guardrails, scanning git diffs for fitness checks defined in simple YAML frontmatter files. It runs tiered validations—fast lints, normal tests, deep scans—with weighted scores, hard gates, and review triggers for risky changes like oversized diffs or high-impact paths. Developers get traceable evidence, JSON reports, and graph-based impact analysis to catch issues early in the AI code-gen era.

Why is it gaining traction?

Unlike static linters or basic CI scripts, entrix is change-aware: it skips irrelevant checks on git diffs, estimates blast radius and test coverage via code graphs, and exposes tools as an MCP server for AI agents in harness engineering ai workflows. Preset configs adapt to monorepos, pre-commit hooks block oversized files, and commands like `entrix graph impact --base HEAD~1` deliver actionable insights without setup hassle. Harness github actions integration keeps it lightweight yet powerful for harness engineering excellence.

Who should use this?

Engineering managers at scale-ups enforcing architecture fitness in monorepos, like those juggling Rust crates and TypeScript apps. Backend teams needing review triggers for core changes, or frontend devs blocking god-files via file budgets. Anyone onboarding AI-generated code who wants codified gates over manual PR reviews.

Verdict

Try it for local hooks and CI if you're in harness engineering—solid docs and PyPI-ready at v0.1.9, stable for workflows despite 19 stars and 0.9% credibility score signaling early alpha status. Polish graph deps and presets for broader adoption.

(198 words)

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