Can use generated-code visual qa with a defined task, suitable inputs, and a basic validation check.
augmented skill · Stage 08
Generated-code visual QA
Anchor skill
Generated-code visual QA helps a designer carry design intent into code, specifications, and implementation decisions. It combines AI-enabled speed with explicit evidence checks, correction, and accountable human judgment.
Why it matters
This is an anchor skill. Weakness here limits maturity for the entire Build Enablement stage because it protects the non-negotiable human checkpoint.
Stage outcome
Translate the validated design into developer-ready code, specs, and assets without losing design intent
What good looks like
01
Frames generated-code visual qa against a clear goal, relevant evidence, and the stage quality gate.
02
Directs AI with scoped inputs, reviews uncertainty, and redirects weak or unsupported output.
03
Documents the decision, trade-off, correction, or override so another designer can follow the reasoning.
Checkpoint application
Apply this skill before the Stage 08 gate is approved. The accountable designer must be able to explain how it changed the work, what evidence was considered, and why the result is safe to advance.
Common failure patterns
- Reducing review because generated code or specifications appear complete and professionally formatted.
- Accepting fluent or high-confidence output without testing its evidence, assumptions, exclusions, and downstream effects.
- Recording the final artefact while omitting the rejected option, correction, trade-off, or override that explains the decision.
Five-minute practice
Use it on the work you already have.
Take one current build enablement artifact. Apply generated-code visual qa for five minutes, then record one AI contribution, one human correction, and the evidence that justified the final decision.
Evidence of capability
- A reviewed implementation specification, generated-code review, or design QA record showing the skill in use.
- A short rationale linking the decision to evidence, risk, and intended outcome.
- An example of an AI suggestion that was corrected, rejected, or deliberately accepted.
Leadership assessment questions
01
Can the designer demonstrate generated-code visual qa on a real build enablement decision?
02
What observable evidence distinguishes competent performance from confident explanation?
03
Would weakness in this skill make the human checkpoint ceremonial rather than protective?
Maturity ladder
Can diagnose weak output, redirect the work, combine sources, and explain the resulting trade-off.
Can decide when not to delegate, own the human checkpoint, and remain accountable for the outcome.
Backlinks and relationships