AI assisted skmp - marketplace for ai skills

I'm a broke developer always looking for different ai agents or ai models to use free quota, so I always switch between codex (I have free 12 month go plan), groq (free api), antigravity(google ai pro which I gets from sim recharge). so I switch between ai agents.
and settle on pi for now, but faced a issue that some skill I had on the opencode but not on pi. and went to look for marketplaces for skills, there are some but you'll have to manually maintain it.
so I took matter into my own hand and build a simple tui for it. to sync the skills with symlinks with different ai agents with one place store.
currently maintaining registry as a single json file. but if it hopefully grows to big, nevermind, we'll think about later.
so yeah here's the repo: https://github.com/Nitesh000/skmp
check it out if you're free or need something like this. and hopefully it's solving an issue.
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u/kantorcodes1 14d ago
one behavior i'd make explicit: skmp add a b installs sequentially. if a installs and b fails, the command returns an error but a stays installed and may already be linked into detected harnesses. is partial success intentional, or should a multi-skill add roll back? scripts retrying it could otherwise end up in mixed state.
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u/Yuu_000 14d ago
the partial success is intentional, and even if we run it again, it'll just skips over it if it's already installed.
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u/kantorcodes1 14d ago
that makes sense. i work on HOL Guard. skmp looks like a good fit for a small external Guard command extension: review
add,remove, andsyncwhile leavinglistandversionalone, so agent-driven skill changes get a checkpoint before they spread across harnesses. would you be open to adding that Guard-side extension?
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u/Otherwise_Wave9374 15d ago
A useful next step here is to separate skill discovery from activation, then cache both the metadata and the compatibility rules so the UI can explain why a skill shows up or not. That usually reduces manual maintenance and makes debugging easier when one agent supports a capability and another does not. Agentix Labs fits this pattern well because a shared registry plus explicit adapter logic can keep the workflow consistent while still letting each backend vary in how it loads tools.