Skillzero – save tokens by omitting skills from agent context

Hacker News

Hi HN, this is a first for me. I use agents for multiple things, from daily life, fitness, video editing, to (performance) engineering. For some of those I have skills globally installed, because I may need them across different projects. But this means in any unrelated task, all skill names + descriptions will still make it into the agent context [1]. This is not the end of the world, but also not great as it could lead to your agent no longer invoking skills reliably (see Addy Osmani's https://addyo.substack.com/p/audit-your-agent-files ). With skillzero, you can manage the skills via CLI. It splits them in 3 categories: - hidden skills (entirely removed from context) → they now behave like /-commands - collection skills (bundled into one skill, so the agent sees only one name + description) → agent can still invoke these when it thinks it should use them - regular skills (untouched by skillzero) You can install it via `npx skillzero` (or `pnpx` or whatever you prefer). [1]: Actually Claude Code & Codex have an optimization, if the name+desc's exceed 1%/2% of the context window, both will omit some skills entirely. Happy to hear any feedback!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

90%90% 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.

TrustMRRFits verified-revenue profile · Strong signals: video, fitness · Missing: mobile apps, ios, personal

61%61% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% 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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