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review-model-guidance

Guide AI model selection for code review tasks by complexity and cost.

48|18|Updated Aug 30, 2025
Search Tags:#code review#task optimization#llm#model selection#subtasks#ai models
One-click install
npx skills add https://github.com/in-the-loop-labs/pair-review --skill review-model-guidance
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: review-model-guidance
Source: https://github.com/in-the-loop-labs/pair-review/tree/main/.pi/skills/review-model-guidance
Command: npx skills add https://github.com/in-the-loop-labs/pair-review --skill review-model-guidance

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps users select the most appropriate AI model for different code review tasks, optimizing for cost and performance by matching model capabilities to task complexity.

Core Features & Use Cases

  • Model Selection Guidance: Provides heuristics for choosing between balanced, deep reasoning, and code generation models based on task type (e.g., architectural analysis, bug finding, code generation).
  • Cost Optimization: Explicitly forbids prohibitively expensive models and suggests cost-effective alternatives.
  • Subtask Strategy: Details how to leverage parallel subtasks with per-task model selection for complex reviews.
  • Use Case: When reviewing a complex architectural change, use this skill to guide the selection of a deep reasoning model like anthropic/claude-opus-4-6 for nuanced analysis, while a simpler bug-finding task might use openai/o3.

Quick Start

Use the review-model-guidance skill to get advice on selecting the best model for analyzing security implications in a code diff.

Frequently Asked Questions about review-model-guidance

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

FAQPage Schema
How do I select the best AI model for code review tasks?▼

Selecting an AI model for code review requires matching model capabilities to task complexity, optimizing for cost and specific analysis types like bug finding or architectural reasoning.

What's the best way to optimize costs when using LLMs for code review?▼

To optimize costs in LLM code review, explicitly avoid prohibitively expensive models and use cost-effective alternatives suited for the specific analysis type, such as bug finding.

Can I use different AI models for subtasks within a single code review?▼

Yes, you can leverage parallel subtasks with per-task model selection to handle complex code reviews, assigning deep reasoning models to nuanced analysis and simpler models to bug finding.

When should I use deep reasoning models over code generation models for code analysis?▼

Use deep reasoning models for complex architectural changes requiring nuanced analysis, while code generation or balanced models are better suited for simpler bug finding tasks.

Does model selection guidance vary by programming languages and frameworks?▼

Yes, effective model selection requires awareness of specific model strengths and capabilities tailored to different programming languages and frameworks during code review.

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