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Claude Skills represents the biggest advancement in practical AI since the introduction of sub-agents because it solves the fundamental problem of AI consistency in business contexts

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The Reasoning

Skills enable deterministic outputs through custom scripts and precise context loading, addressing the core issue that prevents widespread business AI adoption - unreliable and inconsistent results

What Needs to Be True

  • Business AI adoption is primarily limited by consistency issues rather than capability gaps
  • Deterministic workflows are more valuable than general intelligence for most business use cases
  • The ability to create reusable, specialized AI workflows has more impact than general AI improvements

Counterargument

General AI capability improvements like reasoning and multimodality are more important than workflow specialization

What Would Change This View

If businesses continue to struggle with Skills adoption due to complexity, or if general AI becomes so reliable that specialized workflows aren't needed

Implications for Builders

Prioritize building specialized, repeatable workflows over general AI interfaces

Focus on solving the consistency problem rather than just improving AI capabilities

Invest in tools that allow users to create their own specialized AI workflows

Build business-specific rather than general-purpose AI applications

Example Application

A customer service AI that uses skills to handle specific complaint types consistently outperforms a general chatbot in customer satisfaction and resolution rates, even with less advanced underlying AI

Related Knowledge