Code Quality Assurance Program
Establishment of a multi-layered quality protection system:
- Two-stage review mechanism: first generate draft code and then review it twice with the /review command
- Test Driven Development: create the /test command to generate the test cases, then let the AI fill in the implementation code
- Knowledge base constraints: Embedding architectural constraints and specifications in custom directives
- Manual calibration session: Configure critical operations that must be manually verified such as file writes
Quality metrics: combined with built-in Token counting and execution time monitoring, the complexity of AI-generated code can be quantitatively assessed, triggering warnings when metrics are out of the ordinary.
This answer comes from the articleNanocoder: code generation tool that runs in the local terminalThe
May not be reproduced without permission:AI productivity tools " How to avoid quality issues in AI-generated code and ensure maintainability of the output code?
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