Somebody in r/PromptEngineering just weaponized generational trauma against Claude Code, and the results are hard to argue with. The original poster, going by NumerousDay6102, built a Claude Code skill that grades AI agent output the way a strict Asian parent grades a report card. Perfect, or failure. No “good effort” sticker. No partial credit for trying hard.
The skill is called AsianDad, and it’s exactly what it sounds like. Instead of a warm evaluator nudging your agent toward “pretty close, nice work,” this thing sits there like the parent who asks why you got a 98 and not 100. The creator describes it as dealing “maximum emotional damage” to the AI agent, and somehow, that’s the whole point.
The twist
Here’s what makes this worth your attention: it actually works, but only in one direction. According to the original poster, the harsh grading noticeably improves output quality on logical tasks. Math, code, structured reasoning, the stuff with a clear right answer. Feed that same brutal evaluator a creative writing task, though, and it falls apart completely. The poster says it flat out “doesn’t work with creative tasks at all.”
That split makes sense once you think about it. A strict pass/fail judge needs a way to actually check the answer. Logical tasks have that built in: does the code run, does the math check out, did the agent follow every step. Creative work doesn’t have a clean rubric, so a merciless grader just has nothing solid to grade against. It’ll reject good writing for arbitrary reasons because there’s no ground truth to compare it to.
The creator backed this up with a full writeup, charts, and even a live demo space on Hugging Face so you can see the grading in action before installing anything.
How to actually use this
- 📥 Grab the skill from the creator’s GitHub (search “AsianDadSkill” on GitHub, it’s the repo from yiyubruceliu).
- Drop it into your Claude Code skills setup alongside whatever agent workflow you’re already running.
- 🎯 Point it only at logical, verifiable tasks first: coding, data analysis, math, multi-step reasoning chains.
- 📊 Let it grade the output. It won’t hand out partial credit, so expect a flat pass or a flat fail with no cushioning.
- 🔁 Feed the “failure” verdict straight back into your agent as feedback and rerun the task. The point isn’t the insult, it’s the retry loop that follows. Most agents will quietly clean up their own mistakes once they know a soft “close enough” answer isn’t going to cut it this time.
That last step is really where the value is. A softer evaluator lets your agent coast on “close enough.” A grader with zero tolerance for almost-right forces another pass, and that extra pass is where the quality jump comes from.
Pro tips
- Keep this skill benched for anything creative. Copywriting, story generation, brainstorming, all of it. The poster’s own testing found it actively hurts those tasks, so don’t fight the data.
- Check the Hugging Face demo space before you install anything. It’s a fast way to see real graded examples and decide if the tone fits your workflow.
- Pair it with tasks that already have a verifiable answer. If there’s no objective “correct,” this skill has nothing to hold your agent to, and the whole mechanism breaks down.
- Don’t skip the writeup. The creator included charts showing the before-and-after quality difference, which is more proof than most “I improved my prompt” posts bother to show.
- Treat the first few runs as calibration. If the grader is failing everything, your task might be too vague for a clean pass or fail, not too hard.
The comments on the post are almost as good as the skill itself. One reader joked that the cultural accuracy was “sending” them, comparing it to a parent who asks “so what happened to the other 2 points” over a 98. Another just said it sounds promising and asked to be kept posted on results.
That’s usually a good sign a Reddit find is worth trying: strangers laughing at the premise while quietly admitting they want to test it themselves.
If your agent has been turning in mediocre logical work and you’ve run out of gentle nudges, this might be the blunt instrument you need. Go check out the GitHub repo and the Hugging Face demo, run it against one of your own agent’s outputs, and see if a little manufactured disappointment gets you a better second draft 🚀.
Frequently Asked Questions
Q: Does this strict grading approach work for every type of AI task?
No. The harsh binary evaluation excels at logic and coding tasks where there’s a clear right answer, but it doesn’t work well for creative tasks. As one commenter put it: creative output “crumbles under” binary scoring because there’s no single “correct” output to aim for. Save this tool for structured, fact-based work.
Q: What’s an alternative if strict grading is killing my creative output?
Try the “brutal editor” pattern: pipe your AI’s output through a second Claude instance that critiques and tears apart the work like a tough editor. One commenter reported this approach cuts lazy shortcuts and sloppy answers without the creative downsides of binary grading, you get the gains with more flexibility.
Q: How much should I expect the output quality to improve?
Users report real, measurable gains, especially for coding and logical tasks. The harsh feedback forces the AI to eliminate half-baked answers and shortcuts it would normally get away with. That said, results depend on your task type; logic tasks see the biggest wins.
I found a secret that improves the quality of AI Agents
by u/NumerousDay6102 in PromptEngineering