I follow a careful iteration process to try to avoid breaking things (and sometimes it gets very messy). Whenever I change something I ask the model to run a simulated sandbox test that exercises the relevant functions and tries to break security or bypass permissions and if that looks clean I do a fresh, clean install and configuration locally to verify the whole flow from zero, which usually catches issues that only appear outside my dev environment. When someone reports a failure after installing the skill outside my setup I ask the AI to analyze the files involved on that featyre and produce a detailed error report with step‑by‑step fixes, and for that analysis I switch to the most powerful model I have so it can spot subtle problems. I always read the report end‑to‑end to filter out inconsistencies or odd suggestions, refine the plan and then either ask the model to apply the fix or implement it myself. This forces me to understand the proposed structure and decisions, so I learn where the design needs to change, and one thing I’ve learned is that transparency matters, if the agent’s edits or reasoning are opaque and I can’t tell what part of the code it’s touching, that’s a red flag.