skill
od-contribute
One-click contribution flow for Open Design (nexu-io/open-design) — even for non-coders. Pick one of four cards (ship a Skill or Design System you made with OD; translate docs; fix a typo / write a blog; report a bug), the agent validates and opens a PR (or issue) for you. Trigger words contribute to open design, ship my OD skill, ship my OD design system, translate OD docs, report an OD bug, od-contribute.
Description as published by the maintainer. Source
- active
active — Most recent push to the repository was 2026-08-06.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| GitHub stars | 84,161 | Stars on the repository that contains this skill, not on the skill itself. A collection of fifty skills shares one number, so it says nothing about this particular skill. | cumulative, all time | GitHub | |
| Last commit | 2026-08-06 | Most recent push to the containing repository. It may reflect work on a different skill in the same collection. | point in time | GitHub | |
| repository status | active | The repository holding this skill exists and is not archived. | as of fetch | GitHub |
Will this work with your setup?
Install location suggests this is meant for claude-code. Installed under .claude/skills/, the Claude Code convention. Not a test result: we have not run it.
We have not run this skill against a task with and without it enabled, so we cannot tell you whether it improves anything, what it costs in tokens, or whether it duplicates behaviour your harness already has. When we have run that test, the result will appear on this page with the task, the versions and the budget it used.
The skill definition lives at .claude/skills/od-contribute/SKILL.md in https://github.com/nexu-io/open-design.
Where to get it
Bring your own setup
We take apart real AI setups every week and show what broke, what cost too much, and what the trace actually said. If you run agents on real work, that is where the useful conversation is.
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