Show HN: Skill Container – a decent way to run and distribute agent skills (opens in new tab)

Hi HN — I’m trying to figure out a simple/open way to package agent skills with a clear runtime contract and a sane distribution/update story. What I found frustrating in many existing skill formats is that they’re basically lack of portability: - unclear how to run (Python shebangs, hidden deps, host-specific assumptions) - easy to pollute local environments and drift across machines/versions - little to no safety boundary And distribution is often hand-wavy too: no standard install/update/uninstall flow, no integrity/pinning story.

I found that simply introducing OCI containers solves pretty much all of this in one move.

So, Skill Container’s approach is: OCI containers + GitHub distribution. - Each skill is a repo: SKILL.md + Containerfile + a real CLI entrypoint (e.g. cli.py) + deps in pyproject.toml - Authors publish images to GHCR (ghcr.io/...); users “install” by cloning the repo - Running is just docker run --rm ... ghcr.io/<owner>/<skill>:<tag> --help with explicit mounts; --help is the discovery surface - Updates are predictable: git pull for docs + docker pull for runtime (keeping docs and behavior in sync)

Any opinions would be appreciated!