Engineers are experiencing unreliability when using AI agents, especially when they need to invoke custom skills. To resolve this, tests were conducted on agents using a specific iOS architecture skill. The goal was to quantify skill invocation reliability and identify optimization techniques. A core testing tool was built based on a Bash script; this orchestrated automated testing using prompts, capturing logs, and checking results. Positive and negative test cases were defined and used to evaluate the skill's ability to be invoked. Log parsing techniques were implemented to detect the skill's invocation based on JSON output patterns. Key performance metrics like success rate and accuracy were calculated to assess the agents' performance. Initial testing revealed that both agents had imperfect skill invocation rates, especially with ambiguous prompts. Several optimizations were discovered, including enhancing the skill description, using aggressive language, and adding a skills table. Combining multiple techniques provided improved results, particularly for the Codex agent. The conclusion highlighted the importance of testing and improving skill invocation processes. Developers must use high-quality, thorough prompts to maximize AI agent effectiveness.