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BrunoAMSilva
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model-recommendation

Analyze .agent.md and .prompt.md files to recommend optimal AI models with cost implications.

Updated Apr 14, 2026
Search Tags:#deprecation#cost-optimization#context-aware#copilot#model-selection#prompt-analysis#model-recommendation
One-click install
npx skills add https://github.com/BrunoAMSilva/my-config --skill model-recommendation-brunoamsilva
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: model-recommendation
Source: https://github.com/BrunoAMSilva/my-config/tree/main/coding/skills/model-recommendation
Command: npx skills add https://github.com/BrunoAMSilva/my-config --skill model-recommendation-brunoamsilva

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyze Copilot chatmode and prompt files to determine the best-fit AI model for a given task, balancing reasoning needs, code quality, context capacity, vision support, and subscription cost constraints. This removes guesswork from model selection and provides actionable, subscription-aware recommendations and migration guidance.

Core Features & Use Cases

  • File Analysis: Parse .agent.md and .prompt.md frontmatter and body to extract description, tools, model hints, and task intent.
  • Task Categorization: Classify tasks by complexity, reasoning depth, code needs, multi-turn requirements, and context size to map to model strengths.
  • Model Recommendations: Produce a primary model choice, 1-2 alternatives, cost/multiplier impact for Free/Pro/Pro+, deprecation warnings, and frontmatter update suggestions.
  • Integration Advice: Offer VS Code selection steps, auto-selection suitability, tool-capability alignment, and Context7 verification pointers for up-to-date model data.
  • Use Case Examples: From simple formatting prompts to large-scale architectural reviews, provide tailored model trade-offs and migration paths.

Quick Start

Analyze the specified .agent.md or .prompt.md file and return a primary model recommendation, one or two alternatives with trade-offs, multiplier cost implications for the user's subscription tier, and the exact frontmatter update to apply.

Frequently Asked Questions about model-recommendation

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

FAQPage Schema
How do I choose the best AI model for Copilot prompt files?▼

To choose the best AI model for Copilot prompt files, analyze task complexity, reasoning depth, and context size within .agent.md or .prompt.md frontmatter. This categorizes requirements to map model strengths against subscription constraints.

Can I get cost and deprecation warnings when selecting Copilot chat models?▼

Yes, you can get cost and deprecation warnings when selecting Copilot chat models. Model recommendations include multiplier cost implications for Free, Pro, and Pro+ tiers, alongside explicit deprecation warnings and migration guidance.

What is the best way to update frontmatter for optimal model selection?▼

The best way to update frontmatter for optimal model selection is to parse YAML metadata and apply generated frontmatter edits. This provides actionable updates tailored to task intent, required tools, and context capacity.

Does this model recommendation process work with VS Code selection and Context7?▼

Yes, the model recommendation process works with VS Code selection and Context7. It provides VS Code selection steps, auto-selection suitability checks, and Context7 verification pointers to ensure up-to-date model data alignment.

When do I need to analyze task complexity for AI model recommendations?▼

You need to analyze task complexity for AI model recommendations when balancing reasoning needs, code quality, and context capacity. This applies to scenarios ranging from simple formatting prompts to large-scale architectural reviews.

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