AI Model A/B Test Harness FAQs

Question 1

What kind of output does this skill produce?

Accepted Answer

It generates detailed markdown evidence files for each model and a final Summary Report that categorizes test cases into 'Safe to Downgrade', 'Keep on Opus', or 'Needs More Testing'.

Question 2

Can I test multiple scenarios at the same time?

Accepted Answer

While it runs the baseline and proposed model in parallel for a single case, it processes multiple test cases within a file sequentially to ensure scoring accuracy and prevent context overload.

Question 3

Why is the scoring system binary?

Accepted Answer

A binary system (Pass/Fail) is used to eliminate subjective 'close enough' judgments, forcing a clear determination of whether the proposed model meets the exact success criteria.

Question 4

What is the primary purpose of the model-ab-test skill?

Accepted Answer

It is designed to determine if a specific task or agent can be successfully handled by a smaller, faster model (like Sonnet or Haiku) without losing the quality provided by a larger model (like Opus).

Question 5

How does it ensure the A/B test is fair?

Accepted Answer

The skill dispatches both model requests in a single parallel message using identical prompts and strict 'Discipline Rules' that prevent the user or agent from giving an unfair advantage to either model.

AI Model A/B Test Harness

Key Features

Use Cases

AI Model A/B Test Harness

Key Features

Use Cases