rtl-acquire

Name: rtl-acquire
Availability: InStock
Author: Shenshan

Acquire RTL designs and synthesize them into pre-layout graphs.

41|1|Updated Apr 11, 2026

Search Tags:#synthesis #RTL #graph analysis #corpus acquisition #Yosys #OpenROAD #PyG

Ready to run

npx skills add https://github.com/ShenShan123/agent-r2g --skill rtl-acquire

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rtl-acquire
Source: https://github.com/ShenShan123/agent-r2g/tree/main/r2g-skills/rtl-acquire
Command: npx skills add https://github.com/ShenShan123/agent-r2g --skill rtl-acquire

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires Yosys, OpenROAD, PyG, python3, git, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the discovery, screening, and acquisition of RTL designs at scale, followed by synthesis into pre-layout graphs, enabling the creation of large-scale training datasets for RTL graph analysis.

Core Features & Use Cases

RTL Corpus Acquisition: Systematically discover and acquire RTL designs from various sources (local repositories, manifests, keyword search).
Synthesis: Synthesize acquired RTL designs into pre-layout graphs.
Use Case: Imagine you need a large dataset of RTL designs for training a graph analysis model. Use this Skill to acquire and synthesize thousands of designs into a graph format suitable for training.

Quick Start

Run the acquisition and synthesis workflow for RTL designs with the command: python3 scripts/run_expansion_round.py --discover --run-retry

Frequently Asked Questions about rtl-acquire

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I acquire RTL designs at scale for graph analysis model training?▼

To acquire RTL designs at scale for graph analysis, you can automate the discovery, screening, and acquisition of RTL designs from local repositories, manifests, or keyword searches. This process systematically builds a large corpus of RTL designs suitable for training graph analysis models.

How do I convert RTL designs into pre-layout graphs using Yosys and OpenROAD?▼

To convert RTL designs into pre-layout graphs using Yosys and OpenROAD, you run an automated synthesis workflow that processes the acquired RTL designs. This converts the RTL designs into a pre-layout graph format suitable for training graph analysis models with PyG.

Do I need PyG and Yosys to synthesize RTL designs into pre-layout graphs?▼

Yes, you need PyG, Yosys, and OpenROAD installed to synthesize RTL designs into pre-layout graphs. These dependencies are strictly required for the synthesis process and for converting the RTL designs into the graph format used for training graph analysis models.

What is the best way to automate RTL corpus expansion and validation?▼

The best way to automate RTL corpus expansion and validation is to use dedicated scripts that handle discovery, synthesis, and validation. You can run the acquisition and synthesis workflow with a single command to expand and validate large-scale RTL corpora efficiently.

Can I use locally stored RTL repositories for large-scale corpus acquisition?▼

Yes, you can use locally stored RTL repositories for large-scale corpus acquisition. The acquisition workflow systematically discovers and acquires RTL designs from various sources, including local repositories, manifests, and keyword searches, to build a comprehensive training dataset.