Cavyro

Cavyro is AI-assisted, not vibe-coded. I'm a senior engineer with many years of experience; Claude Code wrote most of the code, but every feature started as a spec I wrote and reviewed, and every change went through the same quality gates a human PR would.

How it's set up:

• A "work" repo as the brain. It sits next to the code repos (Rails API, React app, AI/MCP service, Telegram bot, SwiftUI iOS app, website) and holds ~50 knowledge docs on architecture, domain rules and conventions, plus ~40 feature specs. Claude reads the spec and the relevant docs before touching code, so it doesn't guess.
• Specs before code. Each feature gets a spec with decisions, open questions and scope. Claude builds against it; whatever is left over goes into a "next steps" file instead of dying in the chat.
• Skills as guardrails. Each repo ships its own Claude Code skills (Rails Way, RSpec, zero-downtime migrations, React architecture, design system, copy voice), packaged as plugins so they load in any session.
• Hooks, not hopes. A pre-tool hook blocks edits to the read-only reference checkouts; all coding happens in git worktrees, each with its own live preview. A session-start hook maps in-flight branches to the docs that cover them.
• A knowledge graph linking code domains, docs and specs, so Claude can ask "what touches deals?" before a change.
• Quality gates: RuboCop, RSpec, OpenAPI contract specs, typecheck, lint, complexity limits and E2E tests run before anything lands. Weekly releases from develop to main with Conventional Commits.
• MCP everywhere: Claude Code drives deploys and logs through MCP, and Cavyro ships its own MCP server (listed in the MCP Registry) so AI agents can work with your CRM.
• A friction log: when Claude gets something wrong twice, the fix goes into a doc, skill or hook, not just the conversation.

The result: one engineer shipping a multi-repo SaaS with the discipline of a team.