Hi Rain, Thanks for your feedback. To keep the Skill's effectiveness measurable, I’m planning to:
1. Build a Benchmarking Dataset: Create a set of "ground truth" prompts and expected outputs based on Dubbo's docs to catch regressions early. 2. Automated Evaluation: Use a "model-as-a-judge" setup (like using GPT-4 to grade the Skill's output) to get a consistency score during development. 3. CI Integration: Eventually, I want to hook these tests into the CI pipeline so we can see if new changes drop the accuracy or performance. 4. I will also be working hand in hand with you mentor to make sure things are running well. 5. Basically, the goal is to treat the Skill's output like code―if it doesn't pass the benchmark suite, it's not ready. What do you think about that approach for the initial phase? ________________________________ From: Rain Yu <[email protected]> Sent: Tuesday, April 7, 2026 6:53 AM To: CORNELLIUS LIMO <[email protected]> Cc: [email protected] <[email protected]> Subject: Re: GSOC-proposal I have the same question about this proposal, which is how to continuously evaluate whether the capability of this Skill is effective? CORNELLIUS LIMO <[email protected]> 于2026年3月29日周日 17:17写道： > Hi mentor, > Can you please review my proposal. > Any reviews for changes I would appreciate. >
