How does this differ from standard AI code suggestions?

This skill adopts a high-level Staff Engineer mindset, prioritizing system-level risks and documentation alignment over simple syntax fixes.

Will it automatically approve my pull requests?

No, the skill is configured to never rubber-stamp changes; it will always prioritize calling out material risks or missing tests.

Can it review implementation plans before code is written?

Yes, it is designed to review both physical code changes (PRs/patches) and high-level ticket implementation strategies.

Does it check for documentation consistency?

Yes, it specifically scans relevant documentation files to ensure code changes do not deviate from documented architecture or APIs.

Staff-Level Code Review

Name: Staff-Level Code Review
Author: majtom2grndctrl

bymajtom2grndctrl

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Collaboration Tools

Performs rigorous, system-level code reviews using a Staff Software Engineer mindset to ensure architectural alignment and code quality.

About

The Staff-Level Code Review skill transforms Claude into a high-level technical auditor, focusing on architectural integrity, security, and long-term maintainability. Unlike standard AI suggestions, this skill forces the model to ground its analysis in existing documentation and ticket context, ensuring that every review is pragmatic and risk-aware. It is particularly useful for teams looking to maintain high standards in complex codebases by identifying missing tests, API mismatches, and potential regressions before they reach production.

Key Features

Gap Detection: Explicitly identifies missing unit tests and verification gaps.
Context-Aware Analysis: Grounds reviews in project tickets and technical documentation.
Structured Findings: Prioritizes feedback by severity and includes specific file references.
Staff Engineer Mindset: Adopts a risk-focused, pragmatic perspective for all reviews.
1 GitHub stars
Architecture Alignment: Ensures code changes align with docs/index.md and system patterns.

Use Cases

Evaluating Pull Requests for potential regressions and performance bottlenecks.
Reviewing implementation plans against ticket requirements before coding begins.
Verifying that new features comply with existing architectural guidelines and internal APIs.

About

Key Features

Gap Detection: Explicitly identifies missing unit tests and verification gaps.
Context-Aware Analysis: Grounds reviews in project tickets and technical documentation.
Structured Findings: Prioritizes feedback by severity and includes specific file references.
Staff Engineer Mindset: Adopts a risk-focused, pragmatic perspective for all reviews.
1 GitHub stars
Architecture Alignment: Ensures code changes align with docs/index.md and system patterns.

Use Cases

Evaluating Pull Requests for potential regressions and performance bottlenecks.
Reviewing implementation plans against ticket requirements before coding begins.
Verifying that new features comply with existing architectural guidelines and internal APIs.