senior-code-reviewer

Generate CODE_REVIEW.md or MODEL_SUMMARY.md with findings, risks, and priorities.

Updated Apr 11, 2026

Search Tags:#architecture #documentation #checkpoints #code-review #training-logs #model-analysis

Ready to run

npx skills add https://github.com/fredericpoitevin/.cursor --skill senior-code-reviewer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: senior-code-reviewer
Source: https://github.com/fredericpoitevin/.cursor/tree/main/skills/senior-code-reviewer
Command: npx skills add https://github.com/fredericpoitevin/.cursor --skill senior-code-reviewer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Senior software engineers and ML researchers need deep, structured reviews of codebases or models to produce actionable summaries that reveal architecture, flaws, and priorities.

Core Features & Use Cases

Orientation: survey project structure to identify model files, configs, training logs, and evaluation artifacts.
Architecture Review: analyze data flows, modules, embedding choices, and potential design flaws.
Results & Priorities: synthesize training results, checkpoints, and metrics into a prioritized action plan.
Output: generate a CODE_REVIEW.md or MODEL_SUMMARY.md that documents findings for researchers and engineers.

Quick Start

Provide a comprehensive codebase review and export a MODEL_SUMMARY.md in the current directory.

Frequently Asked Questions about senior-code-reviewer

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

FAQPage Schema

How do I generate a structured code review for my codebase?▼

To generate a structured code review, survey the project structure, analyze architecture and data flows, then synthesize findings, risks, and priorities into a CODE_REVIEW.md document.

Can I analyze ML training logs and checkpoints to summarize model results?▼

Yes, you can analyze training logs and checkpoints to synthesize metrics and results into a prioritized action plan, exported as a MODEL_SUMMARY.md document for researchers.

What is included in an architecture audit for machine learning projects?▼

An architecture audit includes surveying project structure for model files and configs, analyzing data flows and embedding choices, and identifying potential design flaws across the project.

Does this code review tool work for both software engineering and ML model analysis?▼

Yes, this code review tool works for both software engineering and ML projects, performing architecture audits and model analysis to produce structured documentation of findings and risks.

How do I document architecture design flaws and prioritized fixes?▼

Document architecture design flaws by analyzing modules and data flows, then consolidating identified risks and action items into a structured markdown summary with prioritized fixes.