Treat Skills as permanent, reusable team members (e.g., a "copywriter"). Use Projects for context-specific, temporary initiatives with a clear start and end, like a seasonal marketing campaign. This mental model clarifies when to use each feature.
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An effective AI development workflow involves treating models as a team of specialists. Use Claude as the reliable 'workhorse' for building an application from the ground up, while leveraging models like Gemini or GPT-4 as 'advisory models' for creative input and alternative problem-solving perspectives.
The paradigm is shifting from using AI as a general chatbot to building a team of 'digital employees.' Claude Skills allow users to encapsulate a specific, repeatable workflow—like drafting a newsletter from tweets—into a tool that can be executed on demand, creating a specialized agent for that job.
Instead of guessing which skills to create, describe your business to Claude and ask it to recommend the 10 most valuable, custom skills you should build. This leverages the AI's understanding to bootstrap your own AI-powered workflow.
The term "agent" is overloaded. Claude Code agents excel at complex, immediate, human-supervised tasks (e.g., researching and writing a one-off PRD). In contrast, platforms like N8N or Lindy are better suited for building automated, recurring workflows that run on a schedule (e.g., daily competitor monitoring).
Building a single, all-purpose AI is like hiring one person for every company role. To maximize accuracy and creativity, build multiple custom GPTs, each trained for a specific function like copywriting or operations, and have them collaborate.
Don't view AI tools as just software; treat them like junior team members. Apply management principles: 'hire' the right model for the job (People), define how it should work through structured prompts (Process), and give it a clear, narrow goal (Purpose). This mental model maximizes their effectiveness.
A truly effective skill isn't created in one shot. The best practice is to treat the first version as a draft, then iteratively refine it through research, self-critique, and testing to make the AI "think like an expert, not just follow steps."
Instead of holding context for multiple projects in their heads, PMs create separate, fully-loaded AI agents (in Claude or ChatGPT) for each initiative. These "brains" are fed with all relevant files and instructions, allowing the PM to instantly get up to speed and work more efficiently.
Unlike Claude Projects or OpenAI's Custom GPTs which apply a general context to all chats, Claude Skills are task-specific instruction sets that can be dynamically called upon within any conversation. This allows for reusable, on-demand workflows without being locked into a specific project's context.
An effective skill goes beyond a simple instruction. It should be structured like an expert's toolkit, including established frameworks (e.g., AIDA for copywriting), a scoring system for evaluation, and a defined output template for consistency and clarity.