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rseng-contributor-onboardinglisted

Covers turning users into contributors and contributors into regulars: curating genuinely good first issues, onboarding paths and checklists generated from the repository, first-PR shepherding, mentorship and buddy structures, and measuring where the contribution funnel leaks. Use when a project wants contributors but gets none, when first-time contributors do not return, when the user asks for good-first-issue curation, onboarding documentation or mentorship structure, or when a team member or student is joining a research software project.
fdiblen/rseng-agent-skills · ★ 20 · AI & Automation · score 81
Install: claude install-skill fdiblen/rseng-agent-skills
# Contributor onboarding Contributors are made, not found: someone used the software, hit an edge, and the project either made the path from "I could fix this" to a merged PR walkable - or it did not. Onboarding is engineering that path deliberately, for external contributors and equally for the new student or teammate joining the project (the same path, walked faster). The funnel framing keeps it honest: user -> first contact -> first contribution -> second contribution -> regular; each transition can be measured (rseng-community-metrics) and fixed. ## Good first issues: curation, not labeling A good first issue is a real, wanted task that is genuinely doable without tribal knowledge - and the label is a promise: - Curate each one: state the problem, point at the relevant files, sketch the approach, name the definition of done and the test to add (rseng-testing) - fifteen minutes of curation per issue, repaid by every newcomer it serves. An agent can draft these from the backlog well (rseng-project-tracking). - Keep the shelf stocked and fresh: a handful available, claimed ones released after inactivity, solved ones replaced - an empty or stale good-first-issue shelf tells newcomers the invitation was decoration (directories like goodfirstissue.dev index the label; being indexed with stale issues hurts). - Non-code first issues count: docs gaps, example datasets, FAQ entries (rseng-user-support's pipeline generates them naturally) - often the truest f