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See if bloated 'skills' are degrading your coding agent's performance

Hacker News

See if bloated 'skills' are degrading your coding agent's performance

I read the 'Skills: Attention is all you have' paper from earlier this month and it struck a chord with me. They researched 50k agent skills and found that a ton of them have all these instructions to try to force invocation, as a result of which they all compete for attention and degrade the performance and usefulness. It's kind of a tragedy of the commons situation because if you develop a skill, of course you want to tell the agent to "BE SURE TO INVOKE THIS WHEN USER MENTIONS X" or whatever, but then all the skills add the same language and the agent doesn't know what to do. I built a skill grader to grade Agent skills based on the paper's methodology. Paste Github repo and see score. My own skill got a C, by the way, so I'm improving it by reducing keywords and description bloat, etc. I hope we can get to a place where users all reject the bloated skill trend... it just never made sense intuitively to have thousands of words of context loaded to do simple things.. cough G stack...

Visit productView on Hacker NewsAug 25, 2026
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, user, context · Missing: mac, agents, macos
92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive, users · Missing: plus, platform, reviews
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, users, way · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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