How does the skill decide which Claude model to recommend?

It uses a structured decision matrix that classifies tasks by domain (Technical, Business, Creative) and applies 'escalators'—such as high stakes, ambiguity, or broad scope—to determine if a more powerful model like Opus is required.

Can this skill be used for non-technical tasks?

Absolutely. It includes specific decision logic for business strategy, creative writing, marketing copy, and organizational design, ensuring the model matches the required nuance and creative depth.

When should I choose Sonnet over Opus for coding?

Sonnet is recommended for single-file coding, bug fixes, and standard refactoring. Opus is suggested when the task affects 3+ files, involves complex system architecture, or carries high production risk.

Does this skill help save on API costs?

Yes, it identifies 'Haiku-ready' tasks like simple formatting or data extraction, preventing unnecessary spending on more expensive models for low-complexity work.

Claude Model Selector

Name: Claude Model Selector
Author: digital-stoic-org

bydigital-stoic-org

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生産性とワークフロー

Recommends the optimal Claude model for any task by analyzing technical complexity, business stakes, and reasoning requirements.

The Claude Model Selector is a specialized skill designed to optimize AI workflows by matching tasks to the most appropriate Claude model—Haiku, Sonnet, or Opus. It utilizes a sophisticated decision matrix that evaluates tasks across technical, business, and creative domains, factoring in speed, cost, and intelligence requirements. By applying 'complexity escalators' for factors like high-stakes production risks or multi-file architectural changes, the skill ensures you never underpower a critical task or overspend on a simple one.

主な機能

01Multi-domain decision matrix for technical, business, and creative tasks

02Standardized output format with clear reasoning and alternative suggestions

03Automatic complexity escalators for high-stakes or ambiguous requirements

044 GitHub stars

05Detailed cost vs. speed trade-off analysis for every recommendation

06Strategic reasoning for model upgrades based on multi-framework analysis

ユースケース

01Determining if a code refactor is simple enough for Sonnet or requires Opus for architectural integrity

02Selecting models for sensitive business strategy and stakeholder management communications

03Choosing the most cost-effective model for high-volume data extraction or summarization tasks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add digital-stoic-org/agent-skills pick-model

For use in Claude.ai and ChatGPT

主な機能

01Multi-domain decision matrix for technical, business, and creative tasks

02Standardized output format with clear reasoning and alternative suggestions

03Automatic complexity escalators for high-stakes or ambiguous requirements

044 GitHub stars

05Detailed cost vs. speed trade-off analysis for every recommendation

06Strategic reasoning for model upgrades based on multi-framework analysis

ユースケース

01Determining if a code refactor is simple enough for Sonnet or requires Opus for architectural integrity

02Selecting models for sensitive business strategy and stakeholder management communications

03Choosing the most cost-effective model for high-volume data extraction or summarization tasks