--- slug: "arvoretech-hub-pi" source_type: "readme" source_url: "https://cdn.jsdelivr.net/gh/arvoreeducacao/rhm@main/README.md" repo: "https://github.com/arvoreeducacao/rhm" source_file: "README.md" branch: "main" --- # Repo Hub **You describe a feature. Your AI codes it, reviews it, tests it, and opens a pull request.** Repo Hub is a configuration file (`hub.yaml`) that teaches your AI coding assistant how your company builds software. You declare your repositories, your tools, and your skills. The AI uses them — from understanding requirements to delivering a tested PR. Think of it like a **docker-compose for AI-powered development**. Instead of defining containers, you define how your AI should work. ``` You: "Add profile editing to user settings" ↓ AI refines requirements → writes backend + frontend → reviews code → runs tests → opens PR → notifies Slack ↓ You: review the PR ``` Built and battle-tested by [Arvore](https://arvore.com.br), where 10 engineers use this every day to ship real software. --- ## The 30-second version **Without Repo Hub**, your AI coding assistant: - Sees one repo at a time (doesn't know the API changed when editing the frontend) - Does one thing at a time (you manually orchestrate every step) - Can't use your tools (can't check logs, query databases, or run browser tests) **With Repo Hub**, you write a config file that tells the AI: - **Which repos** to work on (and it sees all of them at once) - **Which tools** it can use (databases, monitoring, browser testing, etc.) - **Which skills** it can pull (how you refine, review, test, and build) One CLI command generates the config your editor needs. Done. --- ## What is this, actually? ### It's a YAML file Everything starts with `hub.yaml`. Here's a minimal example: ```yaml name: my-company repos: - name: api url: git@github.com:company/api.git tech: nestjs - name: frontend url: git@github.com:company/frontend.git tech: nextjs mcps: - name: postgresql - name: playwright skills: [refinement, code-review, qa-testing] ``` ### It becomes editor instructions When you run `hub generate --editor `, the CLI reads your YAML and produces config files your editor understands: ``` hub generate --editor cursor → .cursor/rules/orchestrator.mdc (the AI's capabilities prompt) → .cursor/skills/*.md (specialized knowledge, pulled on demand) → .cursor/mcp.json (tool connections) hub generate --editor kiro → .kiro/steering/orchestrator.md (the AI's capabilities prompt) → .kiro/skills/*.md (specialized knowledge, pulled on demand) → .kiro/settings/mcp.json (tool connections) → AGENTS.md (standard agents.md) ``` ### Your editor is the runtime There's no server. No daemon. No separate process. Your AI editor (Cursor, Claude Code, Kiro) reads the generated config — repositories, conventions, connected tools, installed skills — and works with whatever each task needs. --- ## Key concepts (the jargon, explained) | Concept | What it means | Analogy | |---------|--------------|---------| | **Skills** | Specialized knowledge the AI pulls on demand — how to review, test, refine, or work in a given stack | Onboarding docs for a new hire | | **MCPs** | Plugins that connect AI to your tools (databases, monitoring, etc.) | Browser extensions, but for AI | | **Subagents** | Fresh-context helpers spawned on demand (e.g. an independent review) | A second pair of eyes when you want one | | **Capabilities prompt** | The generated instructions: repositories, conventions, and connected tools | A workspace orientation for the AI | | **Hub Workspace** | A folder containing all your repos (each keeps its own git) | A VS Code workspace, but for AI | ### What are MCPs? MCP stands for **Model Context Protocol**. In practice, it's a standard way to give AI access to external tools. Instead of you copy-pasting database schemas or log outputs, the AI queries them directly. Examples: - **Database MCP**: AI queries your PostgreSQL schema to write correct migrations - **Datadog MCP**: AI reads error logs to debug a production issue - **Playwright MCP**: AI opens a browser and clicks through your app to test it - **AWS Secrets MCP**: AI reads environment config without you sharing credentials --- ## Quick Start ```bash npx @arvoretech/hub init my-hub cd my-hub npx @arvoretech/hub add-repo git@github.com:company/api.git --tech nestjs npx @arvoretech/hub add-repo git@github.com:company/frontend.git --tech nextjs npx @arvoretech/hub setup npx @arvoretech/hub generate --editor cursor ``` Open in your editor (Cursor, Kiro, etc.). Describe a feature. Watch it happen. --- ## What does the AI actually do? Here's a concrete example of what happens when you say _"Add profile editing to the user settings page"_: ``` 1. REFINEMENT The AI asks: "Should users be able to change their email? Should there be an avatar upload? What fields are editable?" → Writes a requirements doc 2. IMPLEMENTATION The AI implements across the right repos — PATCH /users/profile on the backend, the settings form on the frontend — following each repo's patterns (pulling the relevant stack skills as needed). 3. REVIEW The AI spawns a fresh-context subagent that pulls the `code-review` skill: "The frontend doesn't handle the avatar upload error case" → fixes it 4. QA Pulls the `qa-testing` skill: opens a browser, fills in the form, submits, verifies → reports pass/fail with screenshots 5. DELIVERY Creates PRs in each repo Posts summary in #eng-prs on Slack Updates the task in Linear ``` **You review the PRs. That's your job now.** --- ## Not a monorepo This is important: **your repos stay completely independent**. Their own git history, their own branches, their own PRs. Repo Hub doesn't merge anything. It's a **workspace layer** — a folder that contains your repos side by side so the AI can see them all at once. Like opening multiple projects in VS Code, but with shared context. - No migration needed - No shared build system - No lock-in --- ## The full hub.yaml Here's what a production config looks like: ```yaml name: my-company repos: - name: api path: ./api url: git@github.com:company/api.git tech: nestjs env_file: .env commands: install: pnpm install dev: pnpm dev build: pnpm build lint: pnpm lint test: pnpm test skills: [backend-nestjs] - name: frontend path: ./frontend url: git@github.com:company/frontend.git tech: nextjs env_file: .env.local commands: install: pnpm install dev: pnpm dev build: pnpm build services: - name: postgres image: postgres:16 port: 5432 - name: redis image: redis:7-alpine port: 6379 - name: sandbox type: sandbox port: 8080 env: profiles: local: description: "Local development" staging: aws_profile: my-company-stg secrets: api: api-staging-secret prod: aws_profile: my-company-prd secrets: api: api-prod-secret mcps: - name: postgresql package: "@arvoretech/postgresql-mcp" - name: playwright package: "@playwright/mcp" - name: datadog package: "@arvoretech/datadog-mcp" integrations: github: pr_branch_pattern: "{linear_id}-{slug}" slack: channels: prs: "#eng-prs" linear: team: Engineering skills: [refinement, code-review, qa-testing, debugging] ``` --- ## Available MCPs | MCP | What AI can do with it | |-----|----------------------| | `@arvoretech/postgresql-mcp` | Query your database schema and data (read-only) | | `@arvoretech/mysql-mcp` | Query MySQL databases (read-only) | | `@arvoretech/aws-secrets-manager-mcp` | Read environment configuration and secrets | | `@arvoretech/datadog-mcp` | Read metrics, logs, and traces for debugging | | `@arvoretech/npm-registry-mcp` | Check package security and versions | | `@arvoretech/tempmail-mcp` | Create temporary emails for E2E test flows | | `@arvoretech/memory-mcp` | Team memory with semantic search | | `@arvoretech/launchdarkly-mcp` | Feature flag management | | `@arvoretech/mcp-proxy` | Intelligent proxy that reduces token usage via `mcp_search` / `mcp_call` | | `@arvoretech/google-chat-mcp` | Manage Google Chat spaces, members, and messages | | `@arvoretech/meet-transcriptions-mcp` | Semantic search across meeting transcriptions | | `@arvoretech/sendgrid-mcp` | Manage SendGrid dynamic email templates | | `@arvoretech/runtime-lens-mcp` | Runtime inspection with inline values for React, NestJS, and Next.js | | `@playwright/mcp` | Control a browser to test your app | MCPs are maintained at [arvore-mcp-servers](https://github.com/arvoreeducacao/arvore-mcp-servers). --- ## Supported Editors | Editor | Status | Command | |--------|--------|---------| | Pi | Supported | `hub generate --editor pi` (runs via the `hub-pi` runtime extension) | | Cursor | Supported | `hub generate --editor cursor` | | Claude Code | Supported | `hub generate --editor claude-code` | | Kiro | Supported | `hub generate --editor kiro` | | OpenCode | Supported | `hub generate --editor opencode` | | Windsurf | Planned | — | | Copilot Workspace | Planned | — | > **Pi** is special: it runs the [`@arvoretech/hub-pi`](https://github.com/arvoreeducacao/rhm/tree/HEAD/packages/pi) extension, which reads the config and derives the capabilities prompt, skills, and MCP wiring **at runtime** — no `AGENTS.md` or editor files are generated. `hub generate --editor pi` just registers the extension in `.pi/settings.json`. --- ## Real Results At Arvore, Repo Hub powers our entire development workflow: - **10x productivity** with a team 3x smaller - **9 repositories** managed as a single AI-aware workspace - **A library of skills** the AI pulls on demand (refine, review, test, per-stack patterns) - **19 tool connections** giving AI access to databases, monitoring, secrets, and testing This is not a demo. We ship production software with this every week. --- ## Project Structure ``` repo-hub-manifest/ ├── packages/cli/ # @arvoretech/hub CLI ├── packages/core/ # shared config + prompt builders ├── packages/pi/ # Pi runtime extension ├── skills/ # skill libraries (refinement, code-review, qa-testing, stacks…) ├── docs/ # reference documentation └── examples/ # example configurations ├── arvore/ # real-world: 9 repos └── nestjs-nextjs/ # minimal: 2 repos ``` --- ## Contributing We welcome contributions. Areas where help is needed: - **Editor adapters** — Windsurf, Copilot Workspace - **Skills** — More frameworks (Go, Python/Django, Java/Spring, Vue, Svelte) and capabilities - **MCPs** — New tool integrations - **Documentation** — Guides, tutorials, videos ## License MIT ---

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