quorum-cli

Automate code reviews with local AST analysis and LLM reasoning.

2|Updated Mar 24, 2026
One-click install
npx skills add https://github.com/jsnyder/quorum --skill quorum-cli
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quorum-cli
Source: https://github.com/jsnyder/quorum/tree/main/skills/quorum-cli
Command: npx skills add https://github.com/jsnyder/quorum --skill quorum-cli

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires quorum, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

quorum-cli simplifies and enhances the code review process by integrating local AST analysis, LLM review capabilities, and a variety of modes tailored for different use cases.

Core Features & Use Cases

  • Multi-Source Code Review: Combines local AST analysis with LLM cold read, linter orchestration, and ast-grep rules for comprehensive reviews.
  • Feedback-Calibrated Findings: Uses your feedback history to refine and calibrate findings for better accuracy.
  • Flexible Review Modes: Offers local-only, LLM-augmented, parallel, compact, ensemble, and daemon modes to fit different review scenarios.

Quick Start

Run the following command to start a review:

quorum review src/auth.py

Frequently Asked Questions about quorum-cli

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

FAQPage Schema
How do I automate code review with AST analysis?▼

Automate code review by running local AST analysis combined with ast-grep rules and linter orchestration to parse code structure and identify issues without relying solely on manual inspection.

How do I use LLM reasoning for software quality checks?▼

Use LLM reasoning for software quality checks by configuring an OpenAI-compatible endpoint and API key to perform cold read reviews of source files alongside local linter orchestration.

Do I need an API key to run LLM-augmented code review?▼

Yes, you need a configured API key and an OpenAI-compatible endpoint to run LLM-augmented code review. You can alternatively use local-only modes if you prefer to avoid external API dependencies.

What's the best way to improve linter accuracy with feedback history?▼

The best way to improve linter accuracy is to use feedback-calibrated findings, where your historical review feedback refines AST analysis and LLM reasoning to reduce false positives over time.

What are the limitations of local-only code review modes?▼

Local-only code review modes limit analysis to AST parsing and linter orchestration, missing the deeper semantic context and complex logic flaws that LLM-based reasoning can identify.