Eriemon

Eriemon / verilog-generator

Public

Agent skill for Verilog-2001 RTL generation workflows.

agent-skill codex-skill eda fpga hardware-generation
18
2
100% credibility
Found May 13, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

An agent skill that guides AI assistants through creating reliable, synthesizable Verilog-2001 hardware designs with validation and testbenches.

How It Works

1
🔍 Discover a smart helper for hardware designs

You hear about a special tool that lets your AI assistant create reliable computer chip blueprints in Verilog.

2
🧩 Add it to your AI toolkit

You connect this skill to your AI coding helper so it knows how to build hardware safely.

3
📝 Describe your hardware idea

You tell your AI what kind of chip module you need, like inputs, outputs, and behaviors.

4
⚡ AI builds your design

Your AI thinks step-by-step, creates the Verilog code, test files, and checks everything works.

5
✅ Review and test the results

You check the generated code, run tests, and see it passes with clear reports.

🎉 Ready-to-build chip design

You now have synthesizable Verilog ready for your hardware project—safe and verified!

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Star Growth

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AI-Generated Review

What is verilog-generator?

This Python package is an agent skill that supercharges AI coding agents like GitHub Copilot (CLI, VSCode, IntelliJ), Claude on Anthropic, or agent GitHub setups to generate reliable Verilog-2001 RTL modules and self-checking testbenches from hardware specs. It structures workflows from intent confirmation—module ports, clocks/resets, interfaces like AXI-Stream or APB—to validated, synthesizable code, with CLI commands for spec scaffolding, prompt rendering, and static validation. Users get deterministic helpers for agent skills examples, skipping AI hallucinations in parity generator Verilog code or complex buses.

Why is it gaining traction?

Unlike generic code gen tools, it enforces precision gates (interface contracts, reference models, vector audits) before output, making agent GitHub code reliable for hardware where bugs cost cycles. The agent skills marketplace integration via YAML metadata fits agent skills Anthropic/Claude flows or GitHub agent HQ, with resumeable workflows and external tool checks (Vivado, yosys) that real devs notice. Narrow Verilog-2001 focus beats broad LLMs on RTL discipline.

Who should use this?

FPGA/ASIC engineers prototyping RTL with agent GitHub Copilot in VSCode or Claude agents, especially for AXI interfaces or quick parity generator Verilog code. Suited for hardware teams in agent skills library experimenting with agent skills vs MCP, or academics validating AI-generated testbenches without full sim setups.

Verdict

Promising alpha for agent skills repo users (18 stars, 1.0% credibility score), with strong bilingual docs, CLI (`verilog-gen scaffold/validate`), and API for custom agents—but low adoption signals immaturity; test locally before production workflows. (187 words)

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