AI-Generated Code Quality Control: A Spec-Driven Workflow with Automated Skills

The author presents a practical workflow for managing code quality when AI-assisted coding dramatically increases code volume. The workflow was distilled from real-world pitfalls and is itself written with AI help.

Core Workflow Steps

Write Spec (Spec-Driven Development) – Define specifications before coding, mirroring the habit of experienced engineers who design first.

Review and Refine Spec with AI – Iteratively correct the spec through dialogue with AI.

Execute Spec to Generate Code – AI produces implementation from the finalized spec.

Automatically Invoke Code Review Skill – AI performs an initial code review.

Automatically Invoke Regression Check Skill – Verify that new changes do not break existing functionality.

Automatically Invoke Test Data Rehearsal Skill – Provide concrete input-output examples in the spec (e.g., input AAA → output BBB; input CCC and KKK → output XXXX). AI executes the code against these cases to validate expected behavior.

Automatically Invoke Non-Functional Requirements Check Skill – Assess stability under adverse conditions such as third-party service failures or traffic spikes.

Manual Code Review (Mandatory) – Human engineers must review the code before acceptance; this step is non-negotiable for production safety.

Optional: Code Clarity Detection Skill

Optional: Code Extensibility Skill

Optional: Code Security Detection Skill – Added because the author's previous company required a security test report before every release, treating security as equally critical as stability.

Accept Code and Trigger Git Workflow – Automate commit/push via git skills.

Optional: Auto-Deploy to Test Environment – Integrate with CI/CD pipelines (e.g., Alibaba Cloud Effect) for automatic deployment.

Reasoning Behind Key Steps

Spec-Driven Development forces a design-before-code mindset. The spec becomes the thinking and communication medium; with AI, the dialogue that previously happened with senior colleagues or documentation now occurs directly with the model, refining the design iteratively.

Three Automated Verification Skills (regression check, test data rehearsal, non-functional check) address the core risk of AI-generated code: functional correctness alone is insufficient. The test data rehearsal uses concrete examples supplied in the spec, letting AI execute and compare actual outputs against expected results. The non-functional check covers operational concerns like dependency failures and load surges.

Mandatory Human Review is emphasized as an immutable safeguard. Regardless of AI capability, production environments serve real users and demand a programmer's reverence for reliability.

Optional Skills and Contextual Adaptation

Clarity, extensibility, and security skills are optional and should be adopted based on organizational requirements. The security skill originated from a prior company's mandatory pre-release security audit process.

Automation and Tooling

All skills are implemented as AI-written scripts or prompts. The workflow integrates with git for version control and can hook into CI/CD systems for automated test-environment deployment.

The author concludes that any skill in this workflow can be created by AI given a clear idea, reducing the need to seek external templates.