To decide if AI is appropriate for a task, apply a simple filter. The work should involve structure, repetition, and context. Crucially, it must also be a task where human oversight is still possible and beneficial. If these conditions aren't met, using an AI tool may be inefficient or risky.

The real value of custom AI skills comes from continuous refinement, not initial creation. A skill is only truly effective when it produces results that are 99% accurate with minimal human edits. This iterative process, which can take dozens of hours, is what transforms a novel tool into an indispensable workflow.

The key to creating effective and reliable AI workflows is distinguishing between tasks AI excels at (mechanical, repetitive actions) and those it struggles with (judgment, nuanced decisions). Focus on automating the mechanical parts first to build a valuable and trustworthy product.

High productivity isn't about using AI for everything. It's a disciplined workflow: breaking a task into sub-problems, using an LLM for high-leverage parts like scaffolding and tests, and reserving human focus for the core implementation. This avoids the sunk cost of forcing AI on unsuitable tasks.

Before investing in robust API connections, test a workflow's value with the simplest possible version, even if it's held together by screenshots and voice commands. If you don't consistently use the 'janky' version for a week, the idea isn't valuable enough to build properly, saving significant time and effort.

Don't assume AI can effectively perform a task that doesn't already have a well-defined standard operating procedure (SOP). The best use of AI is to infuse efficiency into individual steps of an existing, successful manual process, rather than expecting it to complete the entire process on its own.

To find valuable AI use cases, start with projects that save time (efficiency gains). Next, focus on improving the quality of existing outputs. Finally, pursue entirely new capabilities that were previously impossible, creating a roadmap from immediate to transformative value.

The rapid pace of AI development is overwhelming. Instead of trying to automate everything, the most effective approach is to maintain a playful curiosity. Focus on experimenting with AI to solve a single, specific, repeatable problem in your workflow, making adoption both manageable and effective.

Instead of pre-designing a complex AI system, first achieve your desired output through a manual, iterative conversation. Then, instruct the AI to review the entire session and convert that successful workflow into a reusable "skill." This reverse-engineers a perfect system from a proven process.