Model Selection

by nimadorostkar

Model Selection is an AI Engineering skill for Claude Code, published by nimadorostkar in Claude-Skills-collection.

18 stars2 forkson nimadorostkar/Claude-Skills-collectionAdded 2026/07/16+13% in starsRepository updated 2026/07/14

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Install in seconds

Install Model Selection

Copy Model Selection into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/nimadorostkar/Claude-Skills-collection/tree/main/skills/ai/model-selection ~/.claude/skills/model-selection

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/nimadorostkar/Claude-Skills-collection.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: skills/ai/model-selection/SKILL.md in nimadorostkar/Claude-Skills-collection
Installs to: ~/.claude/skills/model-selection
Collection: One of 50 skills cataloged from this repository
Category: AI Engineering — 3670 skills

What Model Selection does

Helps select the right LLM for a task by evaluating capability, cost, latency, and routing strategies. Use when choosing a model for a new feature, reducing costs, or evaluating model migrations.

Model Selection is cataloged under AI Engineering on DirSkills. Model Selection comes from a repository tagged ai, claude, claude-skills and skills.

Documentation

README

Excerpt

Choose the model that meets the task's requirements at the lowest cost and latency. Most production LLM features run on a model several times more expensive than the task requires, because nobody measured the cheaper one.

When to Use

Choosing a model for a new feature.
Reducing the cost or latency of an existing feature.
Evaluating whether a newly released model is worth migrating to.
Designing a routing strategy across several models.

Capabilities

Task-to-capability matching.
Cost and latency modeling at real volume.
Routing: cheap model first, escalate on difficulty.
Benchmark interpretation and its limits.
Migration and re-evaluation.

Inputs

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Model Selection

What else does nimadorostkar publish alongside Model Selection?
Model Selection is one of 50 skills that DirSkills catalogs from nimadorostkar/Claude-Skills-collection, the repository it ships in. Its siblings there include API Design, Agent Design and Agent Instructions. Each one is a separate skill with its own page in this directory, installs the same way Model Selection does, and is maintained by nimadorostkar in that same repository. The rest of the collection is listed on the nimadorostkar/Claude-Skills-collection page.
How does Model Selection compare to other AI Engineering skills?
Model Selection ranks #3597 by stars among the 3670 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Model Selection against them. Open each page to compare what they document and how they install.