dbx-linus-review

Identify core problems and risks in submitted code artifacts with evidence.

5|1|Updated Jan 18, 2026
One-click install
npx skills add https://github.com/DBvc/skills --skill dbx-linus-review
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dbx-linus-review
Source: https://github.com/DBvc/skills/tree/main/skills/dbx-linus-review
Command: npx skills add https://github.com/DBvc/skills --skill dbx-linus-review

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill provides blunt, evidence-based technical critique of code changes, architecture plans, and implementation proposals, ensuring issues are surfaced with data-driven reasoning rather than vibes.

Core Features & Use Cases

  • Strict, Linus-style review of diffs, patches, and designs.
  • Evidence-driven findings including data model, ownership, compatibility, and risk assessment.
  • Actionable outputs with clear implications, fixes, and confidence levels for merge decisions.

Quick Start

Provide a diff, code snippet, or architecture proposal and request a Linus-style strict review.

Frequently Asked Questions about dbx-linus-review

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

FAQPage Schema
What is a Linus-style code review and how does it evaluate technical risk?▼

A Linus-style code review is a strict, pragmatic evaluation of artifacts that identifies core problems and articulates risks to users and systems. It scopes findings to data models, ownership, compatibility, and practicality using concrete evidence.

How do I get an evidence-based architecture review for a patch or design proposal?▼

To get an evidence-based architecture review, provide a diff, code snippet, or architecture proposal. The review scopes to data models and compatibility, outputting concrete findings with suggested fixes and confidence levels for merge decisions.

Can I use a strict technical evaluation to assess data model ownership and compatibility?▼

Yes, you can use a strict technical evaluation to assess data model ownership and compatibility. It scopes the review to these specific areas, prioritizing real-world impact and providing data-driven reasoning rather than subjective vibes.

Does this strict review approach provide actionable fixes with confidence levels for merge decisions?▼

Yes, this strict review approach provides actionable outputs. Findings include concrete evidence, impacts, suggested fixes, and confidence levels, ensuring clear implications for your merge decisions.

What are the limitations of a Linus-style code review for implementation proposals?▼

The limitation of a Linus-style code review is its strict, blunt nature focused purely on technical critique. It prioritizes real-world impact and practicality, which may omit softer collaborative feedback typically found in standard code reviews.