Spring Boot is a Java workable choice for library / sdk. GreatCTO auto-detects both — adds the library archetype overlay, wires library-specific gates, and runs 34 specialist agents around your existing Spring Boot workflow.
GreatCTO reads your pom.xml / build.gradle and detects spring-boot + library archetype from signals: imports, file structure, env vars, README hints.
Attaches the library archetype overlay: archetype-specific reviewer + compliance gates. Override if your specifics differ; the defaults are sensible for Spring Boot-style projects.
qa-engineer runs ./gradlew check / SpotBugs / JaCoCo coverage; security-officer scans for deserialization sinks + Spring SpEL injection; performance-engineer profiles JVM + GC patterns.
Bugs you've hit before in other Spring Boot projects (connection-pool exhaustion, ORM N+1 queries, retry storms) — the agent's Step 0 includes the prior detection order. MTTR drops 94 % on second occurrence (methodology).
$ cd my-spring-boot-app && npx great-cto init ✓ scanning manifests… found manifest ✓ stack: spring-boot (Java) ✓ archetype: library ⚠ archetype + stack combo is unusual — review overlay manually ✓ 34 agents ready $ /start "add library feature" ▸ architect drafting ARCH-library.md… ▸ pm decomposing into beads tasks… ⚐ gate:plan — your approval needed
Approve → 3 senior-devs run in parallel worktrees → 5 reviewers fan out in parallel → gate:ship → deploy. One real run walked stage-by-stage: /proof.
Spring Boot (Java) is not a typical fit for library / sdk. The archetype overlay still attaches, but you may want to override defaults more aggressively. Check the library archetype page for the typical stack list and decide if your case is the right tool / right archetype.
No black-box "AI does it all" loop. GreatCTO is a deterministic state machine — 8 stages, 22 nodes, 2 human gates. Every node maps to a real agent on GitHub. Inspect the state machine →
$ npx great-cto init
Free, MIT, runs locally. Works in Claude Code, Cursor, OpenAI Codex CLI, Aider, and Continue.