This Skill enforces AI to proactively add runtime logs during daily development.
A clear problem has been observed: when writing or modifying code, AI tends to focus on whether the functionality works, and often neglects to write logs. Many pieces of code achieve their intended features but lack necessary runtime logs, making it difficult for humans to troubleshoot issues later, and also hindering post-deployment operations, monitoring, and maintenance.
Without logs, when exceptions occur, developers can only rely on guessing, reproducing the issue, or temporarily adding debug information to locate the problem. Missing critical business logs also makes it hard for the system to determine whether the runtime process is normal, whether user operations succeeded, whether external dependencies are functioning correctly, or which step a task has reached.
Therefore, this Skill requires AI to treat logging as part of the program's maintainability and operability, rather than adding it after problems arise.
After using this Skill, AI should add appropriate logs at critical business flows, exception branches, external dependency calls, task execution results, and other key locations, while correctly distinguishing between log levels such as DEBUG, INFO, WARN, and ERROR.
If the project does not yet have a unified logging library, AI should first implement one and use it consistently in subsequent code, avoiding scattered print statements or ad-hoc log wrappers.
The goal of this Skill is not to make programs print大量 meaningless logs, but to enable programs to have clear, stable, secure, and searchable runtime logs that facilitate future troubleshooting, behavior analysis, anomaly detection, and system operations support.