Descriptions:
Dylan Davis, who runs an AI consultancy, presents a disciplined four-step framework for building reliable skills in Claude and ChatGPT — and explains why most people’s skills underperform by skipping two of those steps. The framework covers Mapping (optional but useful), Proof (mandatory: actually completing the task with AI before encapsulating it), Capture (extracting a reusable skill from the proven conversation), and Patching (a structured method for fixing skills that degrade over time).
The core argument is that building a skill before proving out the process bakes mistakes in at speed. Davis provides specific copy-paste prompts for each stage, including an AI interview prompt that asks the model to gather process details one question at a time (capped at 15 questions), and a capture prompt that explicitly instructs the AI to abstract away client-specific or date-specific details so the skill generalizes correctly. He also recommends adding binary self-grading criteria at the end of any skill — stressing that vague criteria lead to vague self-assessments.
The video is aimed at business users, consultants, and operators who use Claude or ChatGPT for recurring knowledge work tasks like proposals, payroll reconciliation, or branding-compliant documents. The decision criteria for when to build a skill at all — will I repeat this, does quality matter, can it work across conversations — adds practical guardrails before anyone invests time in the framework.
📺 Source: Dylan Davis · Published May 20, 2026
🏷️ Format: Tutorial Demo
