--- title: "Case study: CC Adoption Wave Explorer" description: "How an automated release-watch pipeline turns 3 Claude Code releases into 37 scored adoption issues and one interactive triage dashboard." canonical: "https://orchestkit.yonyon.ai/docs/showcase/lab/adoption-waves" --- # Case study: CC Adoption Wave Explorer How an automated release-watch pipeline turns 3 Claude Code releases into 37 scored adoption issues and one interactive triage dashboard. import { LazyLabEmbed } from "@/components/lazy"; **Problem.** Claude Code ships multiple releases a week. Every release can carry breaking changes for a plugin with 100+ skills and 36 agents — hooks that stop firing, permission semantics that shift, worktree isolation that tightens. Reading changelogs manually does not scale, and falling one version behind costs hours of debugging. **Approach.** An automated pipeline (described in [How we adopt every Claude Code release](/docs/guides/cc-adoption)) watches upstream releases, snapshots each changelog, and runs an LLM triage that files one scored GitHub issue per change. For the July 2026 wave that meant **37 issues across CC 2.1.210 → 2.1.212**, each with a category (breaking / new command / new field), a 0–20 gap score, and the list of affected skills. The explorer below is the dashboard that wave produced. Filter by version, category, gap score, or affected skill; click cards to build a work batch; the prompt bar composes the triage instruction that goes back into a Claude Code session. **What it demonstrates.** Chart marks use a CVD-validated categorical palette (6 slots, all accessibility checks passing), stat tiles and stacked bars follow a documented encoding standard, and the whole artifact is one dependency-free HTML file generated inside the working session that triaged the wave.