Update documentation files
This commit is contained in:
@@ -0,0 +1,743 @@
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1) Generate Control Plane API scaffold
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Folder layout
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backend/control-plane/
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package.json
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tsconfig.json
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src/
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index.ts
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config.ts
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auth.ts
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registry.ts
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types.ts
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storage/
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firestore.ts
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gcs.ts
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routes/
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tools.ts
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runs.ts
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health.ts
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.env.example
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backend/control-plane/package.json
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{
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"name": "@productos/control-plane",
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"version": "0.1.0",
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"private": true,
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"type": "module",
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"main": "dist/index.js",
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"scripts": {
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"dev": "tsx watch src/index.ts",
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"build": "tsc -p tsconfig.json",
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"start": "node dist/index.js",
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"lint": "eslint ."
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},
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"dependencies": {
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"@google-cloud/firestore": "^7.11.0",
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"@google-cloud/storage": "^7.14.0",
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"@fastify/cors": "^9.0.1",
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"@fastify/helmet": "^12.0.0",
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"@fastify/rate-limit": "^9.1.0",
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"fastify": "^4.28.1",
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"zod": "^3.23.8",
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"nanoid": "^5.0.7"
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},
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"devDependencies": {
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"@types/node": "^22.0.0",
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"tsx": "^4.19.0",
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"typescript": "^5.5.4",
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"eslint": "^9.8.0"
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}
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}
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backend/control-plane/tsconfig.json
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{
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"compilerOptions": {
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"target": "ES2022",
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"module": "ES2022",
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"moduleResolution": "Bundler",
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"outDir": "dist",
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"rootDir": "src",
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"strict": true,
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"esModuleInterop": true,
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"skipLibCheck": true,
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"types": ["node"]
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}
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}
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backend/control-plane/.env.example
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PORT=8080
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GCP_PROJECT_ID=your-project-id
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GCS_BUCKET_ARTIFACTS=productos-artifacts-dev
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FIRESTORE_COLLECTION_RUNS=runs
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FIRESTORE_COLLECTION_TOOLS=tools
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# If you put behind IAP / OAuth later, validate ID tokens here:
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AUTH_MODE=dev # dev | oauth
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backend/control-plane/src/config.ts
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export const config = {
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port: Number(process.env.PORT ?? 8080),
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projectId: process.env.GCP_PROJECT_ID ?? "",
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artifactsBucket: process.env.GCS_BUCKET_ARTIFACTS ?? "",
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runsCollection: process.env.FIRESTORE_COLLECTION_RUNS ?? "runs",
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toolsCollection: process.env.FIRESTORE_COLLECTION_TOOLS ?? "tools",
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authMode: process.env.AUTH_MODE ?? "dev"
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};
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backend/control-plane/src/types.ts
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export type ToolRisk = "low" | "medium" | "high";
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export type ToolDef = {
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name: string;
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description: string;
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risk: ToolRisk;
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executor: {
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kind: "http";
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url: string; // executor base url
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path: string; // executor endpoint path
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};
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inputSchema: unknown; // JSON Schema object
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outputSchema?: unknown; // JSON Schema object
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};
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export type ToolInvokeRequest = {
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tool: string;
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tenant_id: string;
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workspace_id?: string;
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input: unknown;
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dry_run?: boolean;
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};
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export type RunStatus = "queued" | "running" | "succeeded" | "failed";
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export type RunRecord = {
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run_id: string;
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tenant_id: string;
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tool: string;
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status: RunStatus;
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created_at: string;
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updated_at: string;
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input: unknown;
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output?: unknown;
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error?: { message: string; details?: unknown };
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artifacts?: { bucket: string; prefix: string };
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};
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backend/control-plane/src/auth.ts
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import { FastifyRequest } from "fastify";
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import { config } from "./config.js";
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/**
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* V1: dev mode = trust caller (or a shared API key later).
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* V2: validate Google OAuth/IAP identity token and map to tenant/org.
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*/
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export async function requireAuth(req: FastifyRequest) {
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if (config.authMode === "dev") return;
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// Placeholder for OAuth/IAP verification:
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// - read Authorization: Bearer <id_token>
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// - verify token (Google JWKS)
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// - attach req.user
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throw new Error("AUTH_MODE oauth not yet implemented");
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}
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backend/control-plane/src/storage/firestore.ts
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import { Firestore } from "@google-cloud/firestore";
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import { config } from "../config.js";
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import type { RunRecord, ToolDef } from "../types.js";
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const db = new Firestore({ projectId: config.projectId });
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export async function saveRun(run: RunRecord): Promise<void> {
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await db.collection(config.runsCollection).doc(run.run_id).set(run, { merge: true });
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}
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export async function getRun(runId: string): Promise<RunRecord | null> {
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const snap = await db.collection(config.runsCollection).doc(runId).get();
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return snap.exists ? (snap.data() as RunRecord) : null;
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}
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export async function saveTool(tool: ToolDef): Promise<void> {
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await db.collection(config.toolsCollection).doc(tool.name).set(tool, { merge: true });
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}
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export async function listTools(): Promise<ToolDef[]> {
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const snap = await db.collection(config.toolsCollection).get();
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return snap.docs.map(d => d.data() as ToolDef);
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}
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backend/control-plane/src/storage/gcs.ts
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import { Storage } from "@google-cloud/storage";
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import { config } from "../config.js";
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const storage = new Storage({ projectId: config.projectId });
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export async function writeArtifactText(prefix: string, filename: string, content: string) {
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const bucket = storage.bucket(config.artifactsBucket);
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const file = bucket.file(`${prefix}/${filename}`);
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await file.save(content, { contentType: "text/plain" });
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return { bucket: config.artifactsBucket, path: `${prefix}/${filename}` };
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}
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backend/control-plane/src/registry.ts
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import type { ToolDef } from "./types.js";
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import { listTools } from "./storage/firestore.js";
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/**
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* Simple registry. V2: cache + versioning + per-tenant overrides.
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*/
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export async function getRegistry(): Promise<Record<string, ToolDef>> {
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const tools = await listTools();
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return Object.fromEntries(tools.map(t => [t.name, t]));
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}
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backend/control-plane/src/routes/health.ts
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import type { FastifyInstance } from "fastify";
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export async function healthRoutes(app: FastifyInstance) {
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app.get("/healthz", async () => ({ ok: true }));
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}
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backend/control-plane/src/routes/tools.ts
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import type { FastifyInstance } from "fastify";
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import { nanoid } from "nanoid";
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import { requireAuth } from "../auth.js";
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import { getRegistry } from "../registry.js";
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import { saveRun } from "../storage/firestore.js";
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import { writeArtifactText } from "../storage/gcs.js";
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import type { RunRecord, ToolInvokeRequest } from "../types.js";
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async function postJson(url: string, body: unknown) {
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const res = await fetch(url, {
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method: "POST",
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headers: { "content-type": "application/json" },
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body: JSON.stringify(body)
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});
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if (!res.ok) {
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const txt = await res.text();
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throw new Error(`Executor error ${res.status}: ${txt}`);
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}
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return res.json() as Promise<unknown>;
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}
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export async function toolRoutes(app: FastifyInstance) {
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app.get("/tools", async (req) => {
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await requireAuth(req);
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const registry = await getRegistry();
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return { tools: Object.values(registry) };
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});
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app.post<{ Body: ToolInvokeRequest }>("/tools/invoke", async (req) => {
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await requireAuth(req);
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const body = req.body;
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const registry = await getRegistry();
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const tool = registry[body.tool];
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if (!tool) return app.httpErrors.notFound(`Unknown tool: ${body.tool}`);
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const runId = `run_${new Date().toISOString().replace(/[-:.TZ]/g, "")}_${nanoid(8)}`;
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const now = new Date().toISOString();
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const run: RunRecord = {
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run_id: runId,
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tenant_id: body.tenant_id,
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tool: body.tool,
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status: "queued",
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created_at: now,
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updated_at: now,
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input: body.input,
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artifacts: { bucket: process.env.GCS_BUCKET_ARTIFACTS ?? "", prefix: `runs/${runId}` }
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};
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await saveRun(run);
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// record input artifact
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await writeArtifactText(`runs/${runId}`, "input.json", JSON.stringify(body, null, 2));
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// execute (sync for v1; v2: push to Cloud Tasks / Workflows)
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try {
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run.status = "running";
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run.updated_at = new Date().toISOString();
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await saveRun(run);
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if (body.dry_run) {
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run.status = "succeeded";
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run.output = { dry_run: true };
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run.updated_at = new Date().toISOString();
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await saveRun(run);
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await writeArtifactText(`runs/${runId}`, "output.json", JSON.stringify(run.output, null, 2));
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return { run_id: runId, status: run.status };
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}
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const execUrl = `${tool.executor.url}${tool.executor.path}`;
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const output = await postJson(execUrl, {
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run_id: runId,
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tenant_id: body.tenant_id,
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workspace_id: body.workspace_id,
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input: body.input
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});
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run.status = "succeeded";
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run.output = output;
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run.updated_at = new Date().toISOString();
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await saveRun(run);
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await writeArtifactText(`runs/${runId}`, "output.json", JSON.stringify(output, null, 2));
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return { run_id: runId, status: run.status };
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} catch (e: any) {
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run.status = "failed";
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run.error = { message: e?.message ?? "Unknown error" };
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run.updated_at = new Date().toISOString();
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await saveRun(run);
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await writeArtifactText(`runs/${runId}`, "error.json", JSON.stringify(run.error, null, 2));
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return { run_id: runId, status: run.status };
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}
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});
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}
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backend/control-plane/src/routes/runs.ts
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import type { FastifyInstance } from "fastify";
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import { requireAuth } from "../auth.js";
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import { getRun } from "../storage/firestore.js";
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export async function runRoutes(app: FastifyInstance) {
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app.get("/runs/:run_id", async (req) => {
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await requireAuth(req);
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// @ts-expect-error fastify param typing
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const runId = req.params.run_id as string;
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const run = await getRun(runId);
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if (!run) return app.httpErrors.notFound("Run not found");
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return run;
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});
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// V1: logs are stored as artifacts in GCS; IDE can fetch by signed URL later
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app.get("/runs/:run_id/logs", async (req) => {
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await requireAuth(req);
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// stub
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return { note: "V1: logs are in GCS artifacts under runs/<run_id>/" };
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});
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}
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backend/control-plane/src/index.ts
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import Fastify from "fastify";
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import cors from "@fastify/cors";
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import helmet from "@fastify/helmet";
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import rateLimit from "@fastify/rate-limit";
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import { config } from "./config.js";
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import { healthRoutes } from "./routes/health.js";
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import { toolRoutes } from "./routes/tools.js";
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import { runRoutes } from "./routes/runs.js";
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const app = Fastify({ logger: true });
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await app.register(cors, { origin: true });
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await app.register(helmet);
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await app.register(rateLimit, { max: 300, timeWindow: "1 minute" });
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await app.register(healthRoutes);
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await app.register(toolRoutes);
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await app.register(runRoutes);
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app.listen({ port: config.port, host: "0.0.0.0" }).catch((err) => {
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app.log.error(err);
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process.exit(1);
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});
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2) Generate Tool Registry schema
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You want two things:
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A human-editable YAML (source of truth)
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A JSON Schema to validate tool definitions
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contracts/tool-registry.yaml (example)
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version: 1
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tools:
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cloudrun.deploy_service:
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description: Deploy a Cloud Run service via Cloud Build.
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risk: medium
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executor:
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kind: http
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url: https://deploy-executor-xxxxx.a.run.app
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path: /execute/deploy
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inputSchema:
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type: object
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required: [service_name, repo, ref, env]
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properties:
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service_name: { type: string }
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repo: { type: string, description: "Git repo URL" }
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ref: { type: string, description: "Branch, tag, or commit SHA" }
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env: { type: string, enum: ["dev", "staging", "prod"] }
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outputSchema:
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type: object
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properties:
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service_url: { type: string }
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revision: { type: string }
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analytics.get_funnel_summary:
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description: Return funnel metrics for a tenant and time window.
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risk: low
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executor:
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kind: http
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url: https://analytics-executor-xxxxx.a.run.app
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path: /execute/funnel
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inputSchema:
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type: object
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required: [range_days]
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properties:
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range_days: { type: integer, minimum: 1, maximum: 365 }
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segment: { type: object }
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contracts/tool-registry.schema.json
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{
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"$schema": "https://json-schema.org/draft/2020-12/schema",
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"$id": "https://productos.dev/schemas/tool-registry.schema.json",
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"type": "object",
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"required": ["version", "tools"],
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"properties": {
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"version": { "type": "integer", "minimum": 1 },
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"tools": {
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"type": "object",
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"additionalProperties": { "$ref": "#/$defs/ToolDef" }
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}
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},
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"$defs": {
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"ToolDef": {
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"type": "object",
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"required": ["description", "risk", "executor", "inputSchema"],
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"properties": {
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||||
"description": { "type": "string" },
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"risk": { "type": "string", "enum": ["low", "medium", "high"] },
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"executor": { "$ref": "#/$defs/Executor" },
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"inputSchema": { "type": "object" },
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||||
"outputSchema": { "type": "object" }
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||||
},
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||||
"additionalProperties": false
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||||
},
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||||
"Executor": {
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||||
"type": "object",
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||||
"required": ["kind", "url", "path"],
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||||
"properties": {
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||||
"kind": { "type": "string", "enum": ["http"] },
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||||
"url": { "type": "string" },
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||||
"path": { "type": "string" }
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||||
},
|
||||
"additionalProperties": false
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||||
}
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||||
}
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||||
}
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||||
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||||
3) Generate VSCodium extension skeleton
|
||||
Folder layout
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||||
client-ide/extensions/gcp-productos/
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||||
package.json
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||||
tsconfig.json
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||||
src/extension.ts
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||||
src/api.ts
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||||
src/ui.ts
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media/icon.png (optional)
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||||
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client-ide/extensions/gcp-productos/package.json
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||||
{
|
||||
"name": "gcp-productos",
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"displayName": "GCP Product OS",
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||||
"description": "Product-centric panels (Code, Marketing, Analytics, Growth...) and backend tool invocation.",
|
||||
"version": "0.0.1",
|
||||
"publisher": "productos",
|
||||
"engines": { "vscode": "^1.90.0" },
|
||||
"categories": ["Other"],
|
||||
"activationEvents": ["onStartupFinished"],
|
||||
"main": "./dist/extension.js",
|
||||
"contributes": {
|
||||
"commands": [
|
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{ "command": "productos.configure", "title": "Product OS: Configure Backend" },
|
||||
{ "command": "productos.tools.list", "title": "Product OS: List Tools" },
|
||||
{ "command": "productos.tools.invoke", "title": "Product OS: Invoke Tool" },
|
||||
{ "command": "productos.runs.open", "title": "Product OS: Open Run" }
|
||||
],
|
||||
"configuration": {
|
||||
"title": "Product OS",
|
||||
"properties": {
|
||||
"productos.backendUrl": {
|
||||
"type": "string",
|
||||
"default": "http://localhost:8080",
|
||||
"description": "Control Plane API base URL"
|
||||
},
|
||||
"productos.tenantId": {
|
||||
"type": "string",
|
||||
"default": "t_dev",
|
||||
"description": "Tenant ID for tool calls"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"scripts": {
|
||||
"build": "tsc -p tsconfig.json",
|
||||
"watch": "tsc -w -p tsconfig.json"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@types/node": "^22.0.0",
|
||||
"@types/vscode": "^1.90.0",
|
||||
"typescript": "^5.5.4"
|
||||
}
|
||||
}
|
||||
|
||||
client-ide/extensions/gcp-productos/tsconfig.json
|
||||
{
|
||||
"compilerOptions": {
|
||||
"target": "ES2022",
|
||||
"module": "CommonJS",
|
||||
"outDir": "dist",
|
||||
"rootDir": "src",
|
||||
"strict": true,
|
||||
"esModuleInterop": true,
|
||||
"skipLibCheck": true
|
||||
}
|
||||
}
|
||||
|
||||
client-ide/extensions/gcp-productos/src/api.ts
|
||||
import * as vscode from "vscode";
|
||||
|
||||
function cfg<T>(key: string): T {
|
||||
return vscode.workspace.getConfiguration("productos").get<T>(key)!;
|
||||
}
|
||||
|
||||
export function backendUrl() {
|
||||
return cfg<string>("backendUrl");
|
||||
}
|
||||
|
||||
export function tenantId() {
|
||||
return cfg<string>("tenantId");
|
||||
}
|
||||
|
||||
export async function listTools(): Promise<any[]> {
|
||||
const res = await fetch(`${backendUrl()}/tools`);
|
||||
if (!res.ok) throw new Error(await res.text());
|
||||
const json = await res.json();
|
||||
return json.tools ?? [];
|
||||
}
|
||||
|
||||
export async function invokeTool(tool: string, input: any) {
|
||||
const res = await fetch(`${backendUrl()}/tools/invoke`, {
|
||||
method: "POST",
|
||||
headers: { "content-type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
tool,
|
||||
tenant_id: tenantId(),
|
||||
input
|
||||
})
|
||||
});
|
||||
if (!res.ok) throw new Error(await res.text());
|
||||
return res.json();
|
||||
}
|
||||
|
||||
export async function getRun(runId: string) {
|
||||
const res = await fetch(`${backendUrl()}/runs/${runId}`);
|
||||
if (!res.ok) throw new Error(await res.text());
|
||||
return res.json();
|
||||
}
|
||||
|
||||
client-ide/extensions/gcp-productos/src/ui.ts
|
||||
import * as vscode from "vscode";
|
||||
import { getRun } from "./api";
|
||||
|
||||
export async function showJson(title: string, obj: any) {
|
||||
const doc = await vscode.workspace.openTextDocument({
|
||||
content: JSON.stringify(obj, null, 2),
|
||||
language: "json"
|
||||
});
|
||||
await vscode.window.showTextDocument(doc, { preview: false });
|
||||
vscode.window.setStatusBarMessage(title, 3000);
|
||||
}
|
||||
|
||||
export async function openRun(runId: string) {
|
||||
const run = await getRun(runId);
|
||||
await showJson(`Run ${runId}`, run);
|
||||
}
|
||||
|
||||
client-ide/extensions/gcp-productos/src/extension.ts
|
||||
import * as vscode from "vscode";
|
||||
import { invokeTool, listTools } from "./api";
|
||||
import { openRun, showJson } from "./ui";
|
||||
|
||||
export function activate(context: vscode.ExtensionContext) {
|
||||
context.subscriptions.push(
|
||||
vscode.commands.registerCommand("productos.configure", async () => {
|
||||
const backendUrl = await vscode.window.showInputBox({ prompt: "Control Plane backend URL" });
|
||||
if (!backendUrl) return;
|
||||
await vscode.workspace.getConfiguration("productos").update("backendUrl", backendUrl, vscode.ConfigurationTarget.Global);
|
||||
vscode.window.showInformationMessage(`Product OS backend set: ${backendUrl}`);
|
||||
})
|
||||
);
|
||||
|
||||
context.subscriptions.push(
|
||||
vscode.commands.registerCommand("productos.tools.list", async () => {
|
||||
const tools = await listTools();
|
||||
await showJson("Tools", tools);
|
||||
})
|
||||
);
|
||||
|
||||
context.subscriptions.push(
|
||||
vscode.commands.registerCommand("productos.tools.invoke", async () => {
|
||||
const tools = await listTools();
|
||||
const pick = await vscode.window.showQuickPick(
|
||||
tools.map((t: any) => ({ label: t.name, description: t.description })),
|
||||
{ placeHolder: "Select a tool to invoke" }
|
||||
);
|
||||
if (!pick) return;
|
||||
|
||||
const inputText = await vscode.window.showInputBox({
|
||||
prompt: "Tool input JSON",
|
||||
value: "{}"
|
||||
});
|
||||
if (!inputText) return;
|
||||
|
||||
const input = JSON.parse(inputText);
|
||||
const result = await invokeTool(pick.label, input);
|
||||
|
||||
await showJson("Invoke Result", result);
|
||||
|
||||
if (result?.run_id) {
|
||||
const open = await vscode.window.showInformationMessage(`Run started: ${result.run_id}`, "Open Run");
|
||||
if (open === "Open Run") await openRun(result.run_id);
|
||||
}
|
||||
})
|
||||
);
|
||||
|
||||
context.subscriptions.push(
|
||||
vscode.commands.registerCommand("productos.runs.open", async () => {
|
||||
const runId = await vscode.window.showInputBox({ prompt: "Run ID" });
|
||||
if (!runId) return;
|
||||
await openRun(runId);
|
||||
})
|
||||
);
|
||||
}
|
||||
|
||||
export function deactivate() {}
|
||||
|
||||
4) Generate Terraform base
|
||||
|
||||
This is a minimal hub-style baseline:
|
||||
|
||||
GCS bucket for artifacts
|
||||
|
||||
Firestore (Native) for runs/tools
|
||||
|
||||
Cloud Run service for Control Plane
|
||||
|
||||
Service accounts + IAM
|
||||
|
||||
Placeholders for executor services
|
||||
|
||||
Folder layout
|
||||
infra/terraform/
|
||||
providers.tf
|
||||
variables.tf
|
||||
outputs.tf
|
||||
main.tf
|
||||
iam.tf
|
||||
|
||||
infra/terraform/providers.tf
|
||||
terraform {
|
||||
required_version = ">= 1.5.0"
|
||||
required_providers {
|
||||
google = {
|
||||
source = "hashicorp/google"
|
||||
version = "~> 5.30"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
provider "google" {
|
||||
project = var.project_id
|
||||
region = var.region
|
||||
}
|
||||
|
||||
infra/terraform/variables.tf
|
||||
variable "project_id" { type = string }
|
||||
variable "region" { type = string default = "us-central1" }
|
||||
variable "artifact_bucket_name" { type = string }
|
||||
variable "control_plane_image" {
|
||||
type = string
|
||||
description = "Container image URI for control-plane (Artifact Registry)."
|
||||
}
|
||||
|
||||
infra/terraform/main.tf
|
||||
resource "google_storage_bucket" "artifacts" {
|
||||
name = var.artifact_bucket_name
|
||||
location = var.region
|
||||
uniform_bucket_level_access = true
|
||||
versioning { enabled = true }
|
||||
}
|
||||
|
||||
# Firestore (Native mode) – requires enabling in console once per project (or via API depending on org policy).
|
||||
resource "google_firestore_database" "default" {
|
||||
name = "(default)"
|
||||
location_id = var.region
|
||||
type = "FIRESTORE_NATIVE"
|
||||
}
|
||||
|
||||
resource "google_service_account" "control_plane_sa" {
|
||||
account_id = "sa-control-plane"
|
||||
display_name = "Product OS Control Plane"
|
||||
}
|
||||
|
||||
resource "google_cloud_run_v2_service" "control_plane" {
|
||||
name = "control-plane"
|
||||
location = var.region
|
||||
|
||||
template {
|
||||
service_account = google_service_account.control_plane_sa.email
|
||||
|
||||
containers {
|
||||
image = var.control_plane_image
|
||||
env {
|
||||
name = "GCP_PROJECT_ID"
|
||||
value = var.project_id
|
||||
}
|
||||
env {
|
||||
name = "GCS_BUCKET_ARTIFACTS"
|
||||
value = google_storage_bucket.artifacts.name
|
||||
}
|
||||
env {
|
||||
name = "AUTH_MODE"
|
||||
value = "dev"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
# Public access optional; prefer IAM auth in production.
|
||||
resource "google_cloud_run_v2_service_iam_member" "control_plane_public" {
|
||||
name = google_cloud_run_v2_service.control_plane.name
|
||||
location = var.region
|
||||
role = "roles/run.invoker"
|
||||
member = "allUsers"
|
||||
}
|
||||
|
||||
infra/terraform/iam.tf
|
||||
# Allow control-plane to write artifacts in GCS
|
||||
resource "google_storage_bucket_iam_member" "control_plane_bucket_writer" {
|
||||
bucket = google_storage_bucket.artifacts.name
|
||||
role = "roles/storage.objectAdmin"
|
||||
member = "serviceAccount:${google_service_account.control_plane_sa.email}"
|
||||
}
|
||||
|
||||
# Firestore access for run/tool metadata
|
||||
resource "google_project_iam_member" "control_plane_firestore" {
|
||||
project = var.project_id
|
||||
role = "roles/datastore.user"
|
||||
member = "serviceAccount:${google_service_account.control_plane_sa.email}"
|
||||
}
|
||||
|
||||
# Placeholder: executor services will each have their own service accounts.
|
||||
# Control-plane should be granted roles/run.invoker on each executor service once created.
|
||||
|
||||
infra/terraform/outputs.tf
|
||||
output "control_plane_url" {
|
||||
value = google_cloud_run_v2_service.control_plane.uri
|
||||
}
|
||||
|
||||
output "artifact_bucket" {
|
||||
value = google_storage_bucket.artifacts.name
|
||||
}
|
||||
@@ -0,0 +1,681 @@
|
||||
Google Cloud Product OS
|
||||
Product-Centric IDE + SaaS Autopilot Platform (Requirements & Architecture)
|
||||
Vision
|
||||
|
||||
Build a Product-Centric IDE and Automation Platform dedicated exclusively to:
|
||||
|
||||
Launching, growing, and operating SaaS products on Google Cloud
|
||||
|
||||
This is NOT a general-purpose IDE.
|
||||
This is a Product Operating System (Product OS) designed to unify:
|
||||
|
||||
Code
|
||||
|
||||
Marketing
|
||||
|
||||
Analytics
|
||||
|
||||
Growth
|
||||
|
||||
Support
|
||||
|
||||
Experiments
|
||||
|
||||
Infrastructure
|
||||
|
||||
AI-driven automation
|
||||
|
||||
into one coherent platform.
|
||||
|
||||
It delivers:
|
||||
|
||||
A Cursor-like experience
|
||||
|
||||
Without Cursor cost
|
||||
|
||||
Powered by Gemini (Vertex AI)
|
||||
|
||||
Optimized specifically for Google Cloud
|
||||
|
||||
Focused exclusively on building & automating products
|
||||
|
||||
Core Product Principles
|
||||
1. Product-Centric, Not Code-Centric
|
||||
|
||||
This platform optimizes for:
|
||||
|
||||
Shipping, launching, growing, and optimizing products, not just writing code.
|
||||
|
||||
2. Opinionated for Google Cloud
|
||||
|
||||
This system is:
|
||||
|
||||
Cloud Run-first
|
||||
|
||||
Firestore / Cloud SQL-native
|
||||
|
||||
BigQuery-native
|
||||
|
||||
Cloud Build-native
|
||||
|
||||
Gemini-native
|
||||
|
||||
No AWS, no Azure, no multi-cloud abstraction.
|
||||
|
||||
3. Automation First
|
||||
|
||||
Everything is:
|
||||
|
||||
Automatable
|
||||
|
||||
Observable
|
||||
|
||||
Auditable
|
||||
|
||||
Optimizable
|
||||
|
||||
4. AI as a Product Operator
|
||||
|
||||
The AI is not just a coding assistant.
|
||||
It is a:
|
||||
|
||||
Product Operator AI
|
||||
capable of coordinating marketing, growth, support, analytics, and code.
|
||||
|
||||
IDE Structure: Product-Centric Layout
|
||||
|
||||
Instead of a traditional IDE layout, the system must expose:
|
||||
|
||||
Product OS
|
||||
├── Code
|
||||
├── Marketing
|
||||
├── Analytics
|
||||
├── Growth
|
||||
├── Support
|
||||
├── Experiments
|
||||
└── Infrastructure
|
||||
|
||||
|
||||
Each section is first-class and AI-assisted.
|
||||
|
||||
Section Requirements
|
||||
1. Code Section
|
||||
|
||||
Purpose:
|
||||
|
||||
Build and deploy product services
|
||||
|
||||
Must support:
|
||||
|
||||
Cloud Run services
|
||||
|
||||
Cloud SQL / Firestore integration
|
||||
|
||||
Secrets management
|
||||
|
||||
Logs & traces
|
||||
|
||||
Rollbacks
|
||||
|
||||
Service templates
|
||||
|
||||
Not required:
|
||||
|
||||
Arbitrary framework support
|
||||
|
||||
Every programming language
|
||||
|
||||
Optimized languages:
|
||||
|
||||
TypeScript / Node
|
||||
|
||||
Python
|
||||
|
||||
2. Marketing Section
|
||||
|
||||
Purpose:
|
||||
|
||||
Automate go-to-market and content execution
|
||||
|
||||
Must support:
|
||||
|
||||
Campaign generation
|
||||
|
||||
Social scheduling (Missinglettr)
|
||||
|
||||
Blog generation & updates
|
||||
|
||||
Landing page updates
|
||||
|
||||
Brand voice control
|
||||
|
||||
Product update → campaign pipeline
|
||||
|
||||
AI must:
|
||||
|
||||
Convert product changes into launch content
|
||||
|
||||
Adapt content to brand style
|
||||
|
||||
3. Analytics Section
|
||||
|
||||
Purpose:
|
||||
|
||||
Understand product performance and causality
|
||||
|
||||
Must support:
|
||||
|
||||
Funnels
|
||||
|
||||
Retention
|
||||
|
||||
Activation
|
||||
|
||||
Cohorts
|
||||
|
||||
LTV
|
||||
|
||||
Causal drivers
|
||||
|
||||
Experiment results
|
||||
|
||||
NOT a SQL editor.
|
||||
This is a Product Intelligence Interface.
|
||||
|
||||
AI must answer:
|
||||
|
||||
“Why did conversion change?”
|
||||
“What caused activation to drop?”
|
||||
“What should we test next?”
|
||||
|
||||
4. Growth Section
|
||||
|
||||
Purpose:
|
||||
|
||||
Optimize onboarding and conversion
|
||||
|
||||
Must support:
|
||||
|
||||
Funnel definitions
|
||||
|
||||
Onboarding flows
|
||||
|
||||
Growth experiments
|
||||
|
||||
A/B tests
|
||||
|
||||
Nudge systems
|
||||
|
||||
Conversion optimization
|
||||
|
||||
AI must:
|
||||
|
||||
Detect drop-offs
|
||||
|
||||
Recommend experiments
|
||||
|
||||
Evaluate uplift
|
||||
|
||||
5. Support Section
|
||||
|
||||
Purpose:
|
||||
|
||||
Integrate customer feedback and product health
|
||||
|
||||
Must support:
|
||||
|
||||
Ticket ingestion
|
||||
|
||||
AI-assisted replies
|
||||
|
||||
Knowledge base generation
|
||||
|
||||
Product issue detection
|
||||
|
||||
Feedback loops into product & marketing
|
||||
|
||||
6. Experiments Section
|
||||
|
||||
Purpose:
|
||||
|
||||
Enable continuous product optimization
|
||||
|
||||
Must support:
|
||||
|
||||
Hypothesis creation
|
||||
|
||||
Experiment creation
|
||||
|
||||
Assignment
|
||||
|
||||
Result analysis
|
||||
|
||||
Causal impact estimation
|
||||
|
||||
Recommendation engine
|
||||
|
||||
7. Infrastructure Section
|
||||
|
||||
Purpose:
|
||||
|
||||
Hide GCP complexity behind product workflows
|
||||
|
||||
Must support:
|
||||
|
||||
Cloud Run provisioning
|
||||
|
||||
Pub/Sub
|
||||
|
||||
Cloud SQL / Firestore
|
||||
|
||||
IAM abstraction
|
||||
|
||||
Deploy / rollback
|
||||
|
||||
Resource health
|
||||
|
||||
No raw IAM or Terraform exposure by default.
|
||||
Everything should be expressed as product-level actions.
|
||||
|
||||
AI System Design
|
||||
Supervisor AI (Product Operator)
|
||||
|
||||
This is NOT a coding agent.
|
||||
|
||||
It is a:
|
||||
|
||||
Product Operator AI
|
||||
capable of coordinating decisions across:
|
||||
|
||||
Marketing
|
||||
|
||||
Growth
|
||||
|
||||
Product
|
||||
|
||||
Support
|
||||
|
||||
Analytics
|
||||
|
||||
Experiments
|
||||
|
||||
Responsibilities:
|
||||
|
||||
Interpret product goals
|
||||
|
||||
Prioritize actions
|
||||
|
||||
Dispatch tasks to tools
|
||||
|
||||
Enforce policies
|
||||
|
||||
Learn from outcomes
|
||||
|
||||
Tool Execution Model (Critical Design Decision)
|
||||
Backend Tool Execution (Option 1)
|
||||
|
||||
All tools execute on the backend.
|
||||
|
||||
The IDE:
|
||||
|
||||
NEVER runs gcloud
|
||||
|
||||
NEVER holds cloud credentials
|
||||
|
||||
NEVER touches databases directly
|
||||
|
||||
Instead:
|
||||
|
||||
IDE / Agent → Control Plane API → Executors → GCP Services
|
||||
|
||||
|
||||
Benefits:
|
||||
|
||||
Security
|
||||
|
||||
Auditing
|
||||
|
||||
Shared automation with SaaS autopilot
|
||||
|
||||
Centralized policy enforcement
|
||||
|
||||
No local cloud configuration
|
||||
|
||||
Control Plane Architecture
|
||||
Control Plane API
|
||||
|
||||
A Cloud Run service responsible for:
|
||||
|
||||
Authentication
|
||||
|
||||
Tool registry
|
||||
|
||||
Tool invocation routing
|
||||
|
||||
Policy enforcement
|
||||
|
||||
Run tracking
|
||||
|
||||
Artifact storage (GCS)
|
||||
|
||||
Gemini proxy
|
||||
|
||||
Core endpoints:
|
||||
|
||||
POST /tools/invoke
|
||||
GET /runs/{id}
|
||||
GET /runs/{id}/logs
|
||||
GET /tools
|
||||
GET /artifacts/{run_id}
|
||||
|
||||
Tool Registry
|
||||
|
||||
All actions are formalized as tools.
|
||||
|
||||
Example:
|
||||
|
||||
cloudrun.deploy_service
|
||||
analytics.get_funnel_summary
|
||||
firestore.update_company_brain
|
||||
missinglettr.publish_campaign
|
||||
experiments.create_ab_test
|
||||
|
||||
|
||||
Each tool defines:
|
||||
|
||||
Input schema
|
||||
|
||||
Output schema
|
||||
|
||||
Risk level
|
||||
|
||||
Executor mapping
|
||||
|
||||
Used by:
|
||||
|
||||
IDE
|
||||
|
||||
Supervisor AI
|
||||
|
||||
Web Dashboard
|
||||
|
||||
Executors (Domain Services)
|
||||
|
||||
Each executor is a Cloud Run service with scoped permissions.
|
||||
|
||||
Deploy Executor
|
||||
|
||||
Cloud Build
|
||||
|
||||
Cloud Run
|
||||
|
||||
Artifact Registry
|
||||
|
||||
Analytics Executor
|
||||
|
||||
BigQuery
|
||||
|
||||
Causality modeling
|
||||
|
||||
Funnel analysis
|
||||
|
||||
Firestore Executor
|
||||
|
||||
Company Brain
|
||||
|
||||
Styles
|
||||
|
||||
Configs
|
||||
|
||||
SQL Executor
|
||||
|
||||
Summaries from Cloud SQL
|
||||
|
||||
Read-heavy
|
||||
|
||||
Missinglettr Executor
|
||||
|
||||
Campaign publishing
|
||||
|
||||
Scheduling
|
||||
|
||||
Data Layer
|
||||
Firestore
|
||||
|
||||
Company Brain
|
||||
|
||||
Style profiles
|
||||
|
||||
Tool registry
|
||||
|
||||
Policy configs
|
||||
|
||||
Run metadata
|
||||
|
||||
GCS
|
||||
|
||||
Logs
|
||||
|
||||
Artifacts
|
||||
|
||||
AI outputs
|
||||
|
||||
Generated patches
|
||||
|
||||
Trace data
|
||||
|
||||
BigQuery
|
||||
|
||||
Events
|
||||
|
||||
Causality models
|
||||
|
||||
Experiments
|
||||
|
||||
Analytics warehouse
|
||||
|
||||
AI Code Editing Strategy
|
||||
|
||||
We do NOT build a new editor.
|
||||
|
||||
We use:
|
||||
|
||||
VS Code APIs
|
||||
|
||||
Patch-based updates
|
||||
|
||||
Flow:
|
||||
|
||||
AI generates structured diffs
|
||||
|
||||
IDE previews changes
|
||||
|
||||
User approves
|
||||
|
||||
IDE applies locally
|
||||
|
||||
Backend executes deploy/test
|
||||
|
||||
Later:
|
||||
|
||||
Backend can open PRs automatically
|
||||
|
||||
IDE Base Technology
|
||||
Editor Base
|
||||
|
||||
We use:
|
||||
✅ VSCodium
|
||||
|
||||
Not Code-OSS directly.
|
||||
|
||||
Reasons:
|
||||
|
||||
Open source
|
||||
|
||||
OpenVSX marketplace
|
||||
|
||||
Low maintenance
|
||||
|
||||
Redistributable
|
||||
|
||||
Fast to ship
|
||||
|
||||
Language Strategy
|
||||
|
||||
We support only:
|
||||
|
||||
TypeScript / Node
|
||||
|
||||
Python
|
||||
|
||||
This allows:
|
||||
|
||||
Better templates
|
||||
|
||||
Better debugging
|
||||
|
||||
Better automation
|
||||
|
||||
Faster AI alignment
|
||||
|
||||
IAM Strategy
|
||||
Users
|
||||
|
||||
OAuth only
|
||||
|
||||
No GCP IAM exposure
|
||||
|
||||
Backend Service Accounts
|
||||
|
||||
Least privilege
|
||||
|
||||
Per-executor roles
|
||||
|
||||
No key files
|
||||
|
||||
Workload identity only
|
||||
|
||||
Product vs General IDE: Explicit Non-Goals
|
||||
|
||||
This platform is NOT:
|
||||
|
||||
A general code editor
|
||||
|
||||
A multi-cloud IDE
|
||||
|
||||
A framework playground
|
||||
|
||||
A replacement for VS Code for all use cases
|
||||
|
||||
It IS:
|
||||
|
||||
A Product Operating System
|
||||
|
||||
A SaaS automation platform
|
||||
|
||||
A GCP-native product launcher
|
||||
|
||||
An AI-driven product operator
|
||||
|
||||
Target Users
|
||||
|
||||
Solo founders
|
||||
|
||||
Indie hackers
|
||||
|
||||
Startup teams
|
||||
|
||||
AI-first SaaS companies
|
||||
|
||||
Product-led growth teams
|
||||
|
||||
Strategic Differentiation
|
||||
|
||||
You are not competing with:
|
||||
|
||||
VS Code
|
||||
|
||||
Cursor
|
||||
|
||||
JetBrains
|
||||
|
||||
You are competing with:
|
||||
|
||||
10+ disconnected tools:
|
||||
|
||||
Segment
|
||||
|
||||
HubSpot
|
||||
|
||||
Mixpanel
|
||||
|
||||
Amplitude
|
||||
|
||||
Intercom
|
||||
|
||||
Zapier
|
||||
|
||||
Notion
|
||||
|
||||
Google Cloud Console
|
||||
|
||||
Marketing schedulers
|
||||
|
||||
Experiment platforms
|
||||
|
||||
You replace them with:
|
||||
|
||||
One Product Operating System
|
||||
|
||||
Build Roadmap
|
||||
Phase 1: Core Platform
|
||||
|
||||
Control Plane API
|
||||
|
||||
Deploy Executor
|
||||
|
||||
VSCodium Extension (Deploy + Logs)
|
||||
|
||||
Gemini integration
|
||||
|
||||
Phase 2: Product Intelligence
|
||||
|
||||
Firestore Executor (Company Brain)
|
||||
|
||||
Analytics Executor
|
||||
|
||||
Funnel + driver tools
|
||||
|
||||
Phase 3: Automation
|
||||
|
||||
Marketing Executor
|
||||
|
||||
Growth + Experimentation
|
||||
|
||||
Supervisor AI
|
||||
|
||||
Phase 4: Full Autopilot
|
||||
|
||||
Approval workflows
|
||||
|
||||
PR automation
|
||||
|
||||
Continuous optimization
|
||||
|
||||
Multi-tenant SaaS
|
||||
|
||||
Final Statement
|
||||
|
||||
This platform exists to enable:
|
||||
|
||||
One-click product launch, AI-driven growth, and autonomous SaaS operation on Google Cloud.
|
||||
|
||||
It is:
|
||||
|
||||
A Product OS
|
||||
|
||||
An AI Product Operator
|
||||
|
||||
A Cursor-like experience
|
||||
|
||||
A GCP-native automation platform
|
||||
949
architecture.md
949
architecture.md
@@ -0,0 +1,949 @@
|
||||
1) Recommended reference architecture (Web SaaS-first, 1 product = 1 GCP project per env)
|
||||
Project model
|
||||
|
||||
One product = one GCP project per environment
|
||||
|
||||
product-foo-dev
|
||||
|
||||
product-foo-staging
|
||||
|
||||
product-foo-prod
|
||||
|
||||
Optional “platform” projects (yours, not the customer’s):
|
||||
|
||||
productos-control-plane (your backend + tool registry + auth)
|
||||
|
||||
productos-observability (optional central dashboards / cross-product rollups)
|
||||
|
||||
productos-billing-export (optional BigQuery billing export aggregation)
|
||||
|
||||
High-level runtime pattern
|
||||
|
||||
IDE + Supervisor AI never touch DBs/services directly.
|
||||
They call your Control Plane API, which routes to domain Executors (Cloud Run services) with least-privilege service accounts.
|
||||
|
||||
VSCodium IDE (Product OS UI) Supervisor AI (Vertex)
|
||||
\ /
|
||||
\ /
|
||||
-----> Control Plane API ----
|
||||
|
|
||||
-------------------------------------------------
|
||||
| | | | |
|
||||
Deploy Exec Analytics Exec Firestore SQL Exec Marketing Exec
|
||||
(Cloud Build (BigQuery jobs) Exec Exec (Missinglettr,
|
||||
+ Cloud Run) (Company (Cloud email provider)
|
||||
Brain) SQL)
|
||||
|
||||
Per-product (customer) project: “product-foo-prod”
|
||||
|
||||
Must-have services
|
||||
|
||||
Cloud Run: product services + executors (if you deploy executors into product project)
|
||||
|
||||
Cloud SQL (Postgres/MySQL): transactional app data
|
||||
|
||||
Firestore: config + “Company Brain” + style profiles + run metadata (if you keep metadata per product)
|
||||
|
||||
BigQuery: event warehouse + analytics datasets/views + experimentation tables
|
||||
|
||||
Pub/Sub: event bus for product events + tool events
|
||||
|
||||
Cloud Tasks / Workflows / Scheduler: durable automation + cron-based routines
|
||||
|
||||
Secret Manager: tokens, DB creds, OAuth secrets (never in code)
|
||||
|
||||
Logging/Monitoring/Trace: observability
|
||||
|
||||
Where to place executors
|
||||
|
||||
Simplest: executors live in the product project (tight coupling, simple data access)
|
||||
|
||||
More “platform”: executors live in your platform project, and access product resources cross-project (strong central control, but more IAM + org policy considerations)
|
||||
|
||||
For your “product per project” approach, I recommend:
|
||||
|
||||
Deploy executor can live in platform (deploy across projects)
|
||||
|
||||
Data executors (SQL/Firestore/BigQuery) often live in product project (least-cross-project permissions)
|
||||
|
||||
Data flows
|
||||
|
||||
Events: Product apps → Pub/Sub → BigQuery (raw + curated)
|
||||
|
||||
Causation/insights: Analytics Exec reads BigQuery → writes Insight Objects to:
|
||||
|
||||
BigQuery tables (truth)
|
||||
|
||||
GCS artifacts (reports)
|
||||
|
||||
Firestore (summary pointers for UI)
|
||||
|
||||
Marketing: Marketing Exec pulls Insight Objects + Company Brain → generates campaigns → publishes via Missinglettr/social APIs; stores outputs in GCS + metadata in Firestore
|
||||
|
||||
2) Service-by-service IAM roles matrix (least privilege template)
|
||||
Identities (service accounts)
|
||||
|
||||
You’ll typically have:
|
||||
|
||||
sa-control-plane (platform): routes tool calls, enforces policy, writes run metadata/artifacts
|
||||
|
||||
sa-deploy-executor (platform): triggers builds and deploys to Cloud Run in product projects
|
||||
|
||||
sa-analytics-executor (product): reads BigQuery + writes insights
|
||||
|
||||
sa-firestore-executor (product): reads/writes Company Brain + configs
|
||||
|
||||
sa-sql-executor (product): connects to Cloud SQL (plus DB user for SQL-level permissions)
|
||||
|
||||
sa-marketing-executor (platform or product): reads insights + calls Missinglettr/email providers; reads secrets
|
||||
|
||||
Where I say “product project”, apply it to each env project (dev/staging/prod).
|
||||
|
||||
IAM matrix (by service)
|
||||
Service / Scope Principal Roles (suggested) Notes
|
||||
Cloud Run (product) sa-deploy-executor roles/run.admin (or narrower), roles/iam.serviceAccountUser (only on the runtime SA), roles/run.invoker (optional) Deploy revisions. Narrow iam.serviceAccountUser to only the runtime SA used by the service being deployed.
|
||||
Cloud Build (platform or product) sa-deploy-executor roles/cloudbuild.builds.editor (or builds.builder depending on workflow) Triggers builds. Many teams keep builds centralized in platform.
|
||||
Artifact Registry sa-deploy-executor roles/artifactregistry.writer Push images. If per-product registries, scope accordingly.
|
||||
Secret Manager (platform/product) sa-marketing-executor, sa-deploy-executor roles/secretmanager.secretAccessor Only for the specific secrets needed.
|
||||
BigQuery dataset (product) sa-analytics-executor roles/bigquery.dataViewer + roles/bigquery.jobUser Dataset-level grants. Prefer views/curated datasets.
|
||||
BigQuery dataset (product write) sa-analytics-executor roles/bigquery.dataEditor (only for insight tables dataset) Separate datasets: events_raw (read), events_curated (read), insights (write).
|
||||
Firestore (product) sa-firestore-executor roles/datastore.user (or roles/datastore.viewer) Use viewer when possible; writer only for Brain/config updates.
|
||||
Cloud SQL (product) sa-sql-executor roles/cloudsql.client IAM to connect; SQL permissions handled by DB user(s).
|
||||
Pub/Sub (product) Producers roles/pubsub.publisher For product services emitting events.
|
||||
Pub/Sub (product) Consumers/executors roles/pubsub.subscriber For analytics/executor ingestion.
|
||||
Cloud Tasks (product/platform) sa-control-plane or orchestrator roles/cloudtasks.enqueuer + roles/cloudtasks.viewer If you queue tool runs or retries.
|
||||
Workflows (product/platform) sa-control-plane roles/workflows.invoker For orchestrated multi-step automations.
|
||||
Cloud Storage (GCS artifacts) sa-control-plane roles/storage.objectAdmin (bucket-level) Write run artifacts; consider objectCreator + separate delete policy if you want immutability.
|
||||
Cloud Run executors (wherever hosted) sa-control-plane roles/run.invoker Control Plane calls executors over HTTP.
|
||||
Strongly recommended scoping rules
|
||||
|
||||
Grant BigQuery roles at the dataset level, not project level.
|
||||
|
||||
Use separate datasets for raw, curated, and insights.
|
||||
|
||||
For Cloud SQL, enforce read-only DB users for most endpoints; create a separate writer user only when needed.
|
||||
|
||||
Keep a “high risk” policy that requires approval for:
|
||||
|
||||
pricing changes
|
||||
|
||||
billing actions
|
||||
|
||||
production destructive infra
|
||||
|
||||
legal/claim-heavy marketing copy
|
||||
|
||||
3) Agent tool catalog (seed tool registry mapped to GCP services)
|
||||
|
||||
This is a starter “tool universe” your Supervisor AI + IDE can call. I’ve grouped by module and listed the backing GCP service.
|
||||
|
||||
A) Code module (build/test/deploy)
|
||||
Tool name Purpose Executes in Backed by
|
||||
repo.apply_patch Apply diff to repo (local or PR flow) Control Plane / Repo service (GitHub App or local workspace)
|
||||
repo.open_pr Open PR with changes Control Plane GitHub App
|
||||
build.run_tests Run unit tests Executor (local/offline or remote) Cloud Build / local runner
|
||||
cloudrun.deploy_service Build + deploy service Deploy Exec Cloud Build + Cloud Run
|
||||
cloudrun.rollback_service Roll back revision Deploy Exec Cloud Run
|
||||
cloudrun.get_service_status Health, revisions, URL Deploy Exec Cloud Run
|
||||
logs.tail Tail logs for service/run Observability Exec Cloud Logging
|
||||
B) Marketing module (campaign creation + publishing)
|
||||
Tool name Purpose Executes in Backed by
|
||||
brand.get_profile Fetch voice/style/claims Firestore Exec Firestore
|
||||
brand.update_profile Update voice/style rules Firestore Exec Firestore
|
||||
marketing.generate_campaign_plan Create campaign plan from insight/product update Marketing Exec Vertex AI (Gemini)
|
||||
marketing.generate_channel_posts Generate platform-specific posts Marketing Exec Vertex AI (Gemini)
|
||||
marketing.publish_missinglettr Schedule/publish via Missinglettr Marketing Exec Missinglettr API + Secret Manager
|
||||
marketing.publish_email Send email campaign Marketing Exec Email provider (SendGrid/etc) + Secret Manager
|
||||
marketing.store_assets Save creatives/outputs Marketing Exec GCS
|
||||
marketing.get_campaign_status Poll publish status Marketing Exec Missinglettr / provider APIs
|
||||
C) Analytics module (events, funnels, causation)
|
||||
Tool name Purpose Executes in Backed by
|
||||
events.ingest Ingest events (if you own ingestion endpoint) Analytics/Ingress Exec Pub/Sub + BigQuery
|
||||
analytics.funnel_summary Funnel metrics Analytics Exec BigQuery
|
||||
analytics.cohort_retention Retention cohorts Analytics Exec BigQuery
|
||||
analytics.anomaly_detect Detect anomalies in KPIs Analytics Exec BigQuery / BQML
|
||||
analytics.top_drivers Feature/sequence drivers Analytics Exec BigQuery / BQML / Vertex
|
||||
analytics.causal_uplift Uplift/causal impact estimate Analytics Exec BigQuery + Vertex (optional)
|
||||
analytics.write_insight Persist insight object Analytics Exec BigQuery + Firestore pointer + GCS artifact
|
||||
D) Growth module (onboarding + lifecycle optimization)
|
||||
Tool name Purpose Executes in Backed by
|
||||
growth.identify_dropoffs Identify where users drop Analytics Exec BigQuery
|
||||
growth.propose_experiment Generate experiment hypothesis/design Growth Exec Gemini + policies
|
||||
experiments.create Create experiment definition Experiments Exec Firestore/SQL + your assignment service
|
||||
experiments.evaluate Evaluate results Analytics/Experiments Exec BigQuery
|
||||
growth.generate_lifecycle_messages Draft onboarding/lifecycle content Marketing/Growth Exec Gemini
|
||||
E) Support module (feedback + ticket assist)
|
||||
Tool name Purpose Executes in Backed by
|
||||
support.ingest_tickets Pull tickets from provider Support Exec Zendesk/Intercom API
|
||||
support.summarize_ticket Summarize and classify Support Exec Gemini
|
||||
support.draft_reply Draft response Support Exec Gemini + brand profile
|
||||
support.update_kb Generate/update KB article Support Exec CMS/Docs + GCS
|
||||
support.escalate_issue Create issue/task Support Exec GitHub Issues/Jira/etc
|
||||
F) Infrastructure module (safe, templated ops only)
|
||||
Tool name Purpose Executes in Backed by
|
||||
infra.provision_service_template Create a Cloud Run service template Infra Exec Terraform/Cloud APIs
|
||||
infra.provision_database Create Cloud SQL/Firestore config Infra Exec Cloud SQL / Firestore
|
||||
infra.provision_pubsub Topics/subscriptions Infra Exec Pub/Sub
|
||||
infra.rotate_secret Rotate/refresh secrets Infra Exec Secret Manager
|
||||
infra.cost_report Cost summary for product Analytics/FinOps Exec Billing export → BigQuery
|
||||
G) Auth module (product auth + internal access)
|
||||
Tool name Purpose Executes in Backed by
|
||||
auth.configure_identity_platform Set up end-user auth Auth/Infra Exec Identity Platform
|
||||
auth.configure_iap Protect internal tools Infra Exec IAP
|
||||
auth.create_oauth_client Create OAuth creds for integrations Infra Exec Google OAuth / Secret Manager
|
||||
Recommended “v1 tool registry seed” (small, shippable)
|
||||
|
||||
If you want a tight first release, seed only these tools:
|
||||
|
||||
Code
|
||||
|
||||
cloudrun.deploy_service
|
||||
|
||||
cloudrun.get_service_status
|
||||
|
||||
logs.tail
|
||||
|
||||
Company Brain
|
||||
|
||||
brand.get_profile
|
||||
|
||||
brand.update_profile
|
||||
|
||||
Analytics
|
||||
|
||||
analytics.funnel_summary
|
||||
|
||||
analytics.top_drivers
|
||||
|
||||
analytics.write_insight
|
||||
|
||||
Marketing
|
||||
|
||||
marketing.generate_channel_posts
|
||||
|
||||
marketing.publish_missinglettr
|
||||
|
||||
That’s enough to demonstrate the full Product OS loop:
|
||||
deploy → events → insight → campaign → publish → measure.
|
||||
|
||||
If you want, I can also output this as an actual tool-registry.yaml starter file (matching the schema we drafted earlier) with concrete JSON Schemas for each of the v1 tools.
|
||||
|
||||
yes gor for it
|
||||
|
||||
Here’s a starter tool-registry.yaml you can drop into contracts/tool-registry.yaml. It matches the schema we defined earlier and includes concrete JSON Schemas for the v1 seed tools:
|
||||
|
||||
cloudrun.deploy_service
|
||||
|
||||
cloudrun.get_service_status
|
||||
|
||||
logs.tail
|
||||
|
||||
brand.get_profile
|
||||
|
||||
brand.update_profile
|
||||
|
||||
analytics.funnel_summary
|
||||
|
||||
analytics.top_drivers
|
||||
|
||||
analytics.write_insight
|
||||
|
||||
marketing.generate_channel_posts
|
||||
|
||||
marketing.publish_missinglettr
|
||||
|
||||
Replace the executor.url placeholders with your actual Cloud Run service URLs.
|
||||
|
||||
version: 1
|
||||
|
||||
tools:
|
||||
|
||||
# ----------------------------
|
||||
# CODE / DEPLOYMENT
|
||||
# ----------------------------
|
||||
|
||||
cloudrun.deploy_service:
|
||||
description: Build and deploy a Cloud Run service using Cloud Build. Returns the service URL and deployed revision.
|
||||
risk: medium
|
||||
executor:
|
||||
kind: http
|
||||
url: https://deploy-executor-REPLACE.a.run.app
|
||||
path: /execute/cloudrun/deploy
|
||||
inputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [service_name, repo, ref, env, region]
|
||||
properties:
|
||||
service_name:
|
||||
type: string
|
||||
minLength: 1
|
||||
description: Cloud Run service name.
|
||||
repo:
|
||||
type: string
|
||||
minLength: 1
|
||||
description: Git repo URL (HTTPS).
|
||||
ref:
|
||||
type: string
|
||||
minLength: 1
|
||||
description: Git ref (branch/tag/SHA).
|
||||
env:
|
||||
type: string
|
||||
enum: [dev, staging, prod]
|
||||
region:
|
||||
type: string
|
||||
minLength: 1
|
||||
description: GCP region for the Cloud Run service (e.g., us-central1).
|
||||
build:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
properties:
|
||||
dockerfile_path:
|
||||
type: string
|
||||
default: Dockerfile
|
||||
build_context:
|
||||
type: string
|
||||
default: .
|
||||
env_vars:
|
||||
type: object
|
||||
additionalProperties:
|
||||
type: string
|
||||
description: Environment variables to set during build/deploy (non-secret).
|
||||
deploy:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
properties:
|
||||
cpu:
|
||||
type: string
|
||||
description: Cloud Run CPU (e.g., "1", "2").
|
||||
memory:
|
||||
type: string
|
||||
description: Cloud Run memory (e.g., "512Mi", "1Gi").
|
||||
min_instances:
|
||||
type: integer
|
||||
minimum: 0
|
||||
max_instances:
|
||||
type: integer
|
||||
minimum: 1
|
||||
concurrency:
|
||||
type: integer
|
||||
minimum: 1
|
||||
timeout_seconds:
|
||||
type: integer
|
||||
minimum: 1
|
||||
maximum: 3600
|
||||
service_account_email:
|
||||
type: string
|
||||
description: Runtime service account email for the Cloud Run service.
|
||||
allow_unauthenticated:
|
||||
type: boolean
|
||||
default: false
|
||||
outputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [service_url, revision]
|
||||
properties:
|
||||
service_url:
|
||||
type: string
|
||||
revision:
|
||||
type: string
|
||||
build_id:
|
||||
type: string
|
||||
warnings:
|
||||
type: array
|
||||
items:
|
||||
type: string
|
||||
|
||||
cloudrun.get_service_status:
|
||||
description: Fetch Cloud Run service status including latest revision and URL.
|
||||
risk: low
|
||||
executor:
|
||||
kind: http
|
||||
url: https://deploy-executor-REPLACE.a.run.app
|
||||
path: /execute/cloudrun/status
|
||||
inputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [service_name, region]
|
||||
properties:
|
||||
service_name:
|
||||
type: string
|
||||
minLength: 1
|
||||
region:
|
||||
type: string
|
||||
minLength: 1
|
||||
outputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [service_name, region, service_url, latest_ready_revision, status]
|
||||
properties:
|
||||
service_name:
|
||||
type: string
|
||||
region:
|
||||
type: string
|
||||
service_url:
|
||||
type: string
|
||||
latest_ready_revision:
|
||||
type: string
|
||||
status:
|
||||
type: string
|
||||
enum: [ready, deploying, error, unknown]
|
||||
last_deploy_time:
|
||||
type: string
|
||||
description: ISO timestamp if available.
|
||||
|
||||
logs.tail:
|
||||
description: Tail recent logs for a Cloud Run service or for a specific run_id. Returns log lines (best-effort).
|
||||
risk: low
|
||||
executor:
|
||||
kind: http
|
||||
url: https://observability-executor-REPLACE.a.run.app
|
||||
path: /execute/logs/tail
|
||||
inputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [scope, limit]
|
||||
properties:
|
||||
scope:
|
||||
type: string
|
||||
enum: [service, run]
|
||||
description: Tail logs by service or by tool run.
|
||||
service_name:
|
||||
type: string
|
||||
description: Required if scope=service.
|
||||
region:
|
||||
type: string
|
||||
description: Optional when scope=service, depending on your log query strategy.
|
||||
run_id:
|
||||
type: string
|
||||
description: Required if scope=run.
|
||||
limit:
|
||||
type: integer
|
||||
minimum: 1
|
||||
maximum: 2000
|
||||
default: 200
|
||||
since_seconds:
|
||||
type: integer
|
||||
minimum: 1
|
||||
maximum: 86400
|
||||
default: 900
|
||||
outputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [lines]
|
||||
properties:
|
||||
lines:
|
||||
type: array
|
||||
items:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [timestamp, text]
|
||||
properties:
|
||||
timestamp:
|
||||
type: string
|
||||
severity:
|
||||
type: string
|
||||
text:
|
||||
type: string
|
||||
|
||||
# ----------------------------
|
||||
# COMPANY BRAIN (BRAND + STYLE)
|
||||
# ----------------------------
|
||||
|
||||
brand.get_profile:
|
||||
description: Retrieve the tenant's brand profile (voice, tone, positioning, compliance constraints).
|
||||
risk: low
|
||||
executor:
|
||||
kind: http
|
||||
url: https://firestore-executor-REPLACE.a.run.app
|
||||
path: /execute/brand/get_profile
|
||||
inputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [profile_id]
|
||||
properties:
|
||||
profile_id:
|
||||
type: string
|
||||
minLength: 1
|
||||
description: Brand profile identifier (e.g., "default").
|
||||
outputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [profile_id, brand]
|
||||
properties:
|
||||
profile_id:
|
||||
type: string
|
||||
brand:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [name, voice, audience, claims_policy]
|
||||
properties:
|
||||
name:
|
||||
type: string
|
||||
voice:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [tone, style_notes, do, dont]
|
||||
properties:
|
||||
tone:
|
||||
type: array
|
||||
items: { type: string }
|
||||
style_notes:
|
||||
type: array
|
||||
items: { type: string }
|
||||
do:
|
||||
type: array
|
||||
items: { type: string }
|
||||
dont:
|
||||
type: array
|
||||
items: { type: string }
|
||||
audience:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
properties:
|
||||
primary:
|
||||
type: string
|
||||
secondary:
|
||||
type: string
|
||||
claims_policy:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
properties:
|
||||
forbidden_claims:
|
||||
type: array
|
||||
items: { type: string }
|
||||
required_disclaimers:
|
||||
type: array
|
||||
items: { type: string }
|
||||
compliance_notes:
|
||||
type: array
|
||||
items: { type: string }
|
||||
|
||||
brand.update_profile:
|
||||
description: Update the tenant's brand profile. Write operations should be validated and audited.
|
||||
risk: medium
|
||||
executor:
|
||||
kind: http
|
||||
url: https://firestore-executor-REPLACE.a.run.app
|
||||
path: /execute/brand/update_profile
|
||||
inputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [profile_id, patch]
|
||||
properties:
|
||||
profile_id:
|
||||
type: string
|
||||
minLength: 1
|
||||
patch:
|
||||
type: object
|
||||
description: Partial update object; executor must validate allowed fields.
|
||||
outputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [ok, updated_at]
|
||||
properties:
|
||||
ok:
|
||||
type: boolean
|
||||
updated_at:
|
||||
type: string
|
||||
|
||||
# ----------------------------
|
||||
# ANALYTICS / CAUSATION (V1 metrics + drivers)
|
||||
# ----------------------------
|
||||
|
||||
analytics.funnel_summary:
|
||||
description: Return funnel metrics for a time window. Uses curated events in BigQuery.
|
||||
risk: low
|
||||
executor:
|
||||
kind: http
|
||||
url: https://analytics-executor-REPLACE.a.run.app
|
||||
path: /execute/analytics/funnel_summary
|
||||
inputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [range_days, funnel]
|
||||
properties:
|
||||
range_days:
|
||||
type: integer
|
||||
minimum: 1
|
||||
maximum: 365
|
||||
funnel:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [name, steps]
|
||||
properties:
|
||||
name:
|
||||
type: string
|
||||
steps:
|
||||
type: array
|
||||
minItems: 2
|
||||
items:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [event_name]
|
||||
properties:
|
||||
event_name:
|
||||
type: string
|
||||
filter:
|
||||
type: object
|
||||
description: Optional event property filters (executor-defined).
|
||||
segment:
|
||||
type: object
|
||||
description: Optional segment definition (executor-defined).
|
||||
outputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [funnel_name, range_days, steps]
|
||||
properties:
|
||||
funnel_name:
|
||||
type: string
|
||||
range_days:
|
||||
type: integer
|
||||
steps:
|
||||
type: array
|
||||
items:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [event_name, users, conversion_from_prev]
|
||||
properties:
|
||||
event_name:
|
||||
type: string
|
||||
users:
|
||||
type: integer
|
||||
minimum: 0
|
||||
conversion_from_prev:
|
||||
type: number
|
||||
minimum: 0
|
||||
maximum: 1
|
||||
|
||||
analytics.top_drivers:
|
||||
description: Identify top correlated drivers for a target metric/event (v1: correlation/feature importance; later: causality).
|
||||
risk: low
|
||||
executor:
|
||||
kind: http
|
||||
url: https://analytics-executor-REPLACE.a.run.app
|
||||
path: /execute/analytics/top_drivers
|
||||
inputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [range_days, target]
|
||||
properties:
|
||||
range_days:
|
||||
type: integer
|
||||
minimum: 1
|
||||
maximum: 365
|
||||
target:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [metric]
|
||||
properties:
|
||||
metric:
|
||||
type: string
|
||||
description: Named metric (e.g., "trial_to_paid", "activation_rate") or event-based metric.
|
||||
event_name:
|
||||
type: string
|
||||
description: Optional: if metric is event-based, supply event_name.
|
||||
candidate_features:
|
||||
type: array
|
||||
items:
|
||||
type: string
|
||||
description: Optional list of features/properties to consider.
|
||||
segment:
|
||||
type: object
|
||||
description: Optional segmentation.
|
||||
outputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [target, range_days, drivers]
|
||||
properties:
|
||||
target:
|
||||
type: object
|
||||
range_days:
|
||||
type: integer
|
||||
drivers:
|
||||
type: array
|
||||
items:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [name, score, direction, evidence]
|
||||
properties:
|
||||
name:
|
||||
type: string
|
||||
score:
|
||||
type: number
|
||||
direction:
|
||||
type: string
|
||||
enum: [positive, negative, mixed, unknown]
|
||||
evidence:
|
||||
type: string
|
||||
description: Human-readable summary of why this driver matters.
|
||||
confidence:
|
||||
type: number
|
||||
minimum: 0
|
||||
maximum: 1
|
||||
|
||||
analytics.write_insight:
|
||||
description: Persist an insight object (BigQuery table + Firestore pointer + GCS artifact). Returns an insight_id.
|
||||
risk: medium
|
||||
executor:
|
||||
kind: http
|
||||
url: https://analytics-executor-REPLACE.a.run.app
|
||||
path: /execute/analytics/write_insight
|
||||
inputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [insight]
|
||||
properties:
|
||||
insight:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [type, title, summary, severity, confidence, window, recommendations]
|
||||
properties:
|
||||
type:
|
||||
type: string
|
||||
enum: [funnel_drop, anomaly, driver, experiment_result, general]
|
||||
title:
|
||||
type: string
|
||||
summary:
|
||||
type: string
|
||||
severity:
|
||||
type: string
|
||||
enum: [info, low, medium, high, critical]
|
||||
confidence:
|
||||
type: number
|
||||
minimum: 0
|
||||
maximum: 1
|
||||
window:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [range_days]
|
||||
properties:
|
||||
range_days:
|
||||
type: integer
|
||||
minimum: 1
|
||||
maximum: 365
|
||||
context:
|
||||
type: object
|
||||
description: Arbitrary structured context (metric names, segments, charts pointers).
|
||||
recommendations:
|
||||
type: array
|
||||
minItems: 1
|
||||
items:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [action, rationale]
|
||||
properties:
|
||||
action:
|
||||
type: string
|
||||
rationale:
|
||||
type: string
|
||||
links:
|
||||
type: array
|
||||
items:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [label, url]
|
||||
properties:
|
||||
label: { type: string }
|
||||
url: { type: string }
|
||||
outputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [insight_id, stored]
|
||||
properties:
|
||||
insight_id:
|
||||
type: string
|
||||
stored:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [bigquery, firestore, gcs]
|
||||
properties:
|
||||
bigquery:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [dataset, table]
|
||||
properties:
|
||||
dataset: { type: string }
|
||||
table: { type: string }
|
||||
firestore:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [collection, doc_id]
|
||||
properties:
|
||||
collection: { type: string }
|
||||
doc_id: { type: string }
|
||||
gcs:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [bucket, prefix]
|
||||
properties:
|
||||
bucket: { type: string }
|
||||
prefix: { type: string }
|
||||
|
||||
# ----------------------------
|
||||
# MARKETING (GENERATION + PUBLISH)
|
||||
# ----------------------------
|
||||
|
||||
marketing.generate_channel_posts:
|
||||
description: Generate platform-specific social posts from a campaign brief + brand profile.
|
||||
risk: low
|
||||
executor:
|
||||
kind: http
|
||||
url: https://marketing-executor-REPLACE.a.run.app
|
||||
path: /execute/marketing/generate_channel_posts
|
||||
inputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [brief, channels, brand_profile_id]
|
||||
properties:
|
||||
brand_profile_id:
|
||||
type: string
|
||||
description: Brand profile id to load (e.g., "default").
|
||||
brief:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [goal, product, audience, key_points]
|
||||
properties:
|
||||
goal:
|
||||
type: string
|
||||
description: What outcome are we driving? (e.g., "trial signups")
|
||||
product:
|
||||
type: string
|
||||
audience:
|
||||
type: string
|
||||
key_points:
|
||||
type: array
|
||||
minItems: 1
|
||||
items: { type: string }
|
||||
offer:
|
||||
type: string
|
||||
call_to_action:
|
||||
type: string
|
||||
landing_page_url:
|
||||
type: string
|
||||
channels:
|
||||
type: array
|
||||
minItems: 1
|
||||
items:
|
||||
type: string
|
||||
enum: [x, linkedin, facebook, instagram, tiktok, youtube, pinterest, reddit, google_business, mastodon, bluesky, threads]
|
||||
variations_per_channel:
|
||||
type: integer
|
||||
minimum: 1
|
||||
maximum: 10
|
||||
default: 3
|
||||
constraints:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
properties:
|
||||
max_length:
|
||||
type: integer
|
||||
minimum: 50
|
||||
maximum: 4000
|
||||
emoji_level:
|
||||
type: string
|
||||
enum: [none, light, medium, heavy]
|
||||
default: light
|
||||
include_hashtags:
|
||||
type: boolean
|
||||
default: true
|
||||
outputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [channels]
|
||||
properties:
|
||||
channels:
|
||||
type: array
|
||||
items:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [channel, posts]
|
||||
properties:
|
||||
channel:
|
||||
type: string
|
||||
posts:
|
||||
type: array
|
||||
items:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [text]
|
||||
properties:
|
||||
text: { type: string }
|
||||
title: { type: string }
|
||||
alt_text: { type: string }
|
||||
hashtags:
|
||||
type: array
|
||||
items: { type: string }
|
||||
media_suggestions:
|
||||
type: array
|
||||
items: { type: string }
|
||||
|
||||
marketing.publish_missinglettr:
|
||||
description: Publish or schedule a campaign via Missinglettr using stored OAuth/token secrets.
|
||||
risk: medium
|
||||
executor:
|
||||
kind: http
|
||||
url: https://marketing-executor-REPLACE.a.run.app
|
||||
path: /execute/marketing/publish_missinglettr
|
||||
inputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [campaign, schedule]
|
||||
properties:
|
||||
campaign:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [name, posts]
|
||||
properties:
|
||||
name:
|
||||
type: string
|
||||
posts:
|
||||
type: array
|
||||
minItems: 1
|
||||
items:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [channel, text]
|
||||
properties:
|
||||
channel:
|
||||
type: string
|
||||
enum: [x, linkedin, facebook, instagram, tiktok, youtube, pinterest, reddit, google_business, mastodon, bluesky, threads]
|
||||
text:
|
||||
type: string
|
||||
media_urls:
|
||||
type: array
|
||||
items: { type: string }
|
||||
link_url:
|
||||
type: string
|
||||
schedule:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [mode]
|
||||
properties:
|
||||
mode:
|
||||
type: string
|
||||
enum: [now, scheduled]
|
||||
start_time:
|
||||
type: string
|
||||
description: ISO timestamp required if mode=scheduled.
|
||||
timezone:
|
||||
type: string
|
||||
default: UTC
|
||||
idempotency_key:
|
||||
type: string
|
||||
description: Optional idempotency key to prevent duplicates.
|
||||
outputSchema:
|
||||
type: object
|
||||
additionalProperties: false
|
||||
required: [provider, campaign_id, status]
|
||||
properties:
|
||||
provider:
|
||||
type: string
|
||||
enum: [missinglettr]
|
||||
campaign_id:
|
||||
type: string
|
||||
status:
|
||||
type: string
|
||||
enum: [queued, scheduled, published, failed]
|
||||
provider_response:
|
||||
type: object
|
||||
description: Raw provider response (redacted as needed).
|
||||
@@ -0,0 +1,610 @@
|
||||
Google Cloud Product OS
|
||||
Technical Specification
|
||||
|
||||
Product-Centric IDE + SaaS Autopilot Platform
|
||||
|
||||
1. Purpose
|
||||
|
||||
This document defines the technical architecture, components, interfaces, and implementation plan for building a:
|
||||
|
||||
Google Cloud–native, Gemini-powered Product Operating System (Product OS)
|
||||
|
||||
The platform unifies:
|
||||
|
||||
Code development
|
||||
|
||||
Product launch
|
||||
|
||||
Marketing automation
|
||||
|
||||
Analytics and causality
|
||||
|
||||
Growth optimization
|
||||
|
||||
Support automation
|
||||
|
||||
Experimentation
|
||||
|
||||
Infrastructure management
|
||||
|
||||
into a single product-centric IDE and automation system.
|
||||
|
||||
This is not a general-purpose IDE.
|
||||
It is a Product OS for launching and operating SaaS products on Google Cloud.
|
||||
|
||||
2. Core Design Principles
|
||||
2.1 Product-Centric Orientation
|
||||
|
||||
The platform optimizes for:
|
||||
|
||||
Shipping products
|
||||
|
||||
Launching features
|
||||
|
||||
Running marketing
|
||||
|
||||
Optimizing growth
|
||||
|
||||
Operating infrastructure
|
||||
|
||||
Automating decisions
|
||||
|
||||
Not for:
|
||||
|
||||
Arbitrary coding workflows
|
||||
|
||||
Multi-cloud portability
|
||||
|
||||
Framework experimentation
|
||||
|
||||
2.2 Opinionated for Google Cloud
|
||||
|
||||
The platform is single-cloud and deeply integrated with:
|
||||
|
||||
Cloud Run
|
||||
|
||||
Cloud Build
|
||||
|
||||
Artifact Registry
|
||||
|
||||
Firestore
|
||||
|
||||
Cloud SQL
|
||||
|
||||
BigQuery
|
||||
|
||||
Pub/Sub
|
||||
|
||||
Vertex AI (Gemini)
|
||||
|
||||
No AWS or Azure abstraction layers are supported.
|
||||
|
||||
2.3 Backend Tool Execution (Security Model)
|
||||
|
||||
All automation executes on the backend.
|
||||
|
||||
The IDE:
|
||||
|
||||
Never runs gcloud
|
||||
|
||||
Never runs Terraform
|
||||
|
||||
Never holds GCP credentials
|
||||
|
||||
Never touches databases directly
|
||||
|
||||
Instead:
|
||||
|
||||
IDE / Supervisor AI
|
||||
↓
|
||||
Control Plane API
|
||||
↓
|
||||
Executors
|
||||
↓
|
||||
GCP Services
|
||||
|
||||
2.4 AI as a Product Operator
|
||||
|
||||
The AI is not a coding assistant.
|
||||
|
||||
It is a:
|
||||
|
||||
Product Operator AI
|
||||
|
||||
Responsibilities:
|
||||
|
||||
Interpret product goals
|
||||
|
||||
Read analytics and insights
|
||||
|
||||
Decide actions
|
||||
|
||||
Dispatch tools
|
||||
|
||||
Enforce policies
|
||||
|
||||
Learn from outcomes
|
||||
|
||||
3. High-Level Architecture
|
||||
┌─────────────────────────────┐
|
||||
│ VSCodium IDE Client │
|
||||
│ (Product-Centric UI Shell) │
|
||||
└──────────────┬──────────────┘
|
||||
│
|
||||
▼
|
||||
┌──────────────────────────┐
|
||||
│ Control Plane API │
|
||||
│ (Tool Router + Policy) │
|
||||
└──────────────┬───────────┘
|
||||
│
|
||||
┌──────────────┬───────────┼─────────────┬──────────────┐
|
||||
▼ ▼ ▼ ▼ ▼
|
||||
Deploy Executor Analytics Exec Firestore Exec SQL Exec Missinglettr Exec
|
||||
Cloud Build+Run BigQuery Firestore Cloud SQL Social Posting
|
||||
|
||||
│
|
||||
┌──────▼───────┐
|
||||
│ GCS Store │
|
||||
│ Artifacts │
|
||||
└──────────────┘
|
||||
|
||||
4. IDE Client Architecture
|
||||
4.1 Base Editor
|
||||
|
||||
VSCodium distribution
|
||||
|
||||
OpenVSX marketplace
|
||||
|
||||
Preinstalled extensions
|
||||
|
||||
Preconfigured settings
|
||||
|
||||
Custom UI panels
|
||||
|
||||
4.2 Product-Centric Navigation
|
||||
|
||||
The IDE must expose:
|
||||
|
||||
Product OS
|
||||
├── Code
|
||||
├── Marketing
|
||||
├── Analytics
|
||||
├── Growth
|
||||
├── Support
|
||||
├── Experiments
|
||||
└── Infrastructure
|
||||
|
||||
|
||||
Each section is:
|
||||
|
||||
First-class
|
||||
|
||||
AI-assisted
|
||||
|
||||
Connected to backend tools
|
||||
|
||||
4.3 IDE Responsibilities
|
||||
|
||||
The IDE handles:
|
||||
|
||||
File editing
|
||||
|
||||
Patch preview & application
|
||||
|
||||
Project context collection
|
||||
|
||||
Tool invocation UI
|
||||
|
||||
Artifact viewing
|
||||
|
||||
Logs & traces display
|
||||
|
||||
The IDE does NOT:
|
||||
|
||||
Execute cloud commands
|
||||
|
||||
Store secrets
|
||||
|
||||
Perform deployments
|
||||
|
||||
Perform database queries
|
||||
|
||||
5. Control Plane API
|
||||
5.1 Purpose
|
||||
|
||||
The Control Plane is the central orchestration backend.
|
||||
|
||||
Responsibilities:
|
||||
|
||||
Auth
|
||||
|
||||
Tool registry
|
||||
|
||||
Tool invocation routing
|
||||
|
||||
Policy enforcement
|
||||
|
||||
Run tracking
|
||||
|
||||
Artifact storage (GCS)
|
||||
|
||||
Gemini proxy
|
||||
|
||||
5.2 Core Endpoints
|
||||
POST /tools/invoke
|
||||
GET /runs/{run_id}
|
||||
GET /runs/{run_id}/logs
|
||||
GET /tools
|
||||
GET /artifacts/{run_id}
|
||||
|
||||
5.3 Tool Invocation Contract
|
||||
Request
|
||||
{
|
||||
"tool": "cloudrun.deploy_service",
|
||||
"tenant_id": "t_123",
|
||||
"workspace_id": "w_456",
|
||||
"input": {
|
||||
"service_name": "marketing-gateway",
|
||||
"repo": "github.com/org/repo",
|
||||
"ref": "main",
|
||||
"env": "prod"
|
||||
},
|
||||
"dry_run": false
|
||||
}
|
||||
|
||||
Response
|
||||
{
|
||||
"run_id": "run_20260119_abc",
|
||||
"status": "queued"
|
||||
}
|
||||
|
||||
6. Tool Registry
|
||||
|
||||
All executable actions are declared as tools.
|
||||
|
||||
6.1 Tool Schema
|
||||
tools:
|
||||
cloudrun.deploy_service:
|
||||
description: Deploy a Cloud Run service
|
||||
input_schema:
|
||||
service_name: string
|
||||
repo: string
|
||||
ref: string
|
||||
env: string
|
||||
output_schema:
|
||||
service_url: string
|
||||
risk: medium
|
||||
executor: deploy-executor
|
||||
|
||||
6.2 Registry Responsibilities
|
||||
|
||||
Input validation
|
||||
|
||||
Output validation
|
||||
|
||||
Risk classification
|
||||
|
||||
Executor routing
|
||||
|
||||
Used by:
|
||||
|
||||
IDE
|
||||
|
||||
Supervisor AI
|
||||
|
||||
Web dashboard
|
||||
|
||||
7. Executors (Domain Services)
|
||||
|
||||
Each executor is a Cloud Run service with its own service account.
|
||||
|
||||
7.1 Deploy Executor
|
||||
|
||||
Purpose:
|
||||
|
||||
Build and deploy services
|
||||
|
||||
Tools:
|
||||
|
||||
cloudrun.deploy_service
|
||||
|
||||
cloudrun.tail_logs
|
||||
|
||||
cloudrun.rollback
|
||||
|
||||
GCP APIs:
|
||||
|
||||
Cloud Build
|
||||
|
||||
Cloud Run
|
||||
|
||||
Artifact Registry
|
||||
|
||||
IAM:
|
||||
|
||||
roles/cloudbuild.builds.editor
|
||||
|
||||
roles/run.admin (scoped)
|
||||
|
||||
roles/artifactregistry.writer
|
||||
|
||||
7.2 Analytics Executor (OpsOS)
|
||||
|
||||
Purpose:
|
||||
|
||||
Product intelligence and causality
|
||||
|
||||
Tools:
|
||||
|
||||
analytics.get_funnel_summary
|
||||
|
||||
analytics.get_top_drivers
|
||||
|
||||
analytics.get_anomalies
|
||||
|
||||
GCP APIs:
|
||||
|
||||
BigQuery
|
||||
|
||||
BigQuery ML
|
||||
|
||||
IAM:
|
||||
|
||||
roles/bigquery.dataViewer
|
||||
|
||||
roles/bigquery.jobUser
|
||||
|
||||
7.3 Firestore Executor
|
||||
|
||||
Purpose:
|
||||
|
||||
Company Brain + configs
|
||||
|
||||
Tools:
|
||||
|
||||
firestore.get_company_brain
|
||||
|
||||
firestore.update_company_brain
|
||||
|
||||
GCP APIs:
|
||||
|
||||
Firestore
|
||||
|
||||
IAM:
|
||||
|
||||
roles/datastore.user
|
||||
|
||||
7.4 SQL Executor
|
||||
|
||||
Purpose:
|
||||
|
||||
Transactional summaries
|
||||
|
||||
Tools:
|
||||
|
||||
sql.get_subscription_summary
|
||||
|
||||
sql.get_user_metrics
|
||||
|
||||
GCP APIs:
|
||||
|
||||
Cloud SQL
|
||||
|
||||
IAM:
|
||||
|
||||
roles/cloudsql.client
|
||||
|
||||
DB-level users
|
||||
|
||||
7.5 Missinglettr Executor
|
||||
|
||||
Purpose:
|
||||
|
||||
Social publishing
|
||||
|
||||
Tools:
|
||||
|
||||
missinglettr.publish_campaign
|
||||
|
||||
missinglettr.get_campaign_status
|
||||
|
||||
Secrets:
|
||||
|
||||
Missinglettr API tokens
|
||||
|
||||
IAM:
|
||||
|
||||
roles/secretmanager.secretAccessor
|
||||
|
||||
8. Data Storage
|
||||
8.1 Firestore
|
||||
|
||||
Used for:
|
||||
|
||||
Company Brain
|
||||
|
||||
Tool registry
|
||||
|
||||
Policy configs
|
||||
|
||||
Style profiles
|
||||
|
||||
Run metadata
|
||||
|
||||
8.2 GCS
|
||||
|
||||
Used for:
|
||||
|
||||
Logs
|
||||
|
||||
AI outputs
|
||||
|
||||
Generated patches
|
||||
|
||||
Deployment artifacts
|
||||
|
||||
Prompt snapshots
|
||||
|
||||
8.3 BigQuery
|
||||
|
||||
Used for:
|
||||
|
||||
Event warehouse
|
||||
|
||||
Funnels
|
||||
|
||||
Causality models
|
||||
|
||||
Experiment results
|
||||
|
||||
9. AI Integration
|
||||
9.1 Gemini Proxy
|
||||
|
||||
All AI calls go through Control Plane.
|
||||
|
||||
Responsibilities:
|
||||
|
||||
Auth
|
||||
|
||||
Rate limiting
|
||||
|
||||
Prompt registry
|
||||
|
||||
Logging
|
||||
|
||||
Cost controls
|
||||
|
||||
9.2 AI Patch Contract
|
||||
|
||||
Gemini must return:
|
||||
|
||||
{
|
||||
"files": [
|
||||
{
|
||||
"path": "src/main.ts",
|
||||
"diff": "@@ -1,3 +1,6 @@ ..."
|
||||
}
|
||||
],
|
||||
"commands": [
|
||||
"npm test"
|
||||
],
|
||||
"summary": "Add logging middleware"
|
||||
}
|
||||
|
||||
10. IAM Strategy
|
||||
10.1 Users
|
||||
|
||||
OAuth only
|
||||
|
||||
No GCP IAM
|
||||
|
||||
No key files
|
||||
|
||||
10.2 Backend
|
||||
|
||||
Workload identity
|
||||
|
||||
No long-lived keys
|
||||
|
||||
Least privilege
|
||||
|
||||
Per-executor roles
|
||||
|
||||
11. Supported Languages
|
||||
|
||||
TypeScript / Node
|
||||
|
||||
Python
|
||||
|
||||
No additional languages in v1.
|
||||
|
||||
12. SaaS Autopilot Layer
|
||||
|
||||
A Supervisor AI Agent runs in Vertex AI Agent Designer.
|
||||
|
||||
It calls the same tools as the IDE.
|
||||
|
||||
Supervisor AI → Control Plane → Executors
|
||||
|
||||
13. Non-Goals
|
||||
|
||||
The platform does NOT:
|
||||
|
||||
Replace VS Code generically
|
||||
|
||||
Support all frameworks
|
||||
|
||||
Support multi-cloud
|
||||
|
||||
Allow raw IAM editing
|
||||
|
||||
Execute cloud commands locally
|
||||
|
||||
14. Repository Structure
|
||||
/platform
|
||||
/client-ide
|
||||
/vscodium
|
||||
/extensions
|
||||
/backend
|
||||
/control-plane
|
||||
/executors
|
||||
/contracts
|
||||
/infra
|
||||
/docs
|
||||
|
||||
15. Implementation Phases
|
||||
Phase 1 – Core
|
||||
|
||||
Control Plane API
|
||||
|
||||
Deploy Executor
|
||||
|
||||
Gemini Proxy
|
||||
|
||||
IDE Deploy UI
|
||||
|
||||
Phase 2 – Intelligence
|
||||
|
||||
Firestore Executor
|
||||
|
||||
Analytics Executor
|
||||
|
||||
Funnel + driver tools
|
||||
|
||||
Phase 3 – Automation
|
||||
|
||||
Missinglettr Executor
|
||||
|
||||
Growth + Experiments
|
||||
|
||||
Supervisor AI
|
||||
|
||||
16. Final Statement
|
||||
|
||||
This system is a:
|
||||
|
||||
Google Cloud–native Product Operating System
|
||||
for launching, growing, and automating SaaS products
|
||||
using Gemini and backend-controlled automation.
|
||||
|
||||
Optional Next Steps
|
||||
|
||||
Generate Control Plane API scaffold
|
||||
|
||||
Generate Tool Registry schema
|
||||
|
||||
Generate VSCodium extension skeleton
|
||||
|
||||
Generate Terraform base
|
||||
|
||||
If you want, I can next generate:
|
||||
|
||||
The Control Plane API OpenAPI spec
|
||||
|
||||
The Tool Registry schema file
|
||||
|
||||
The First Executor service skeleton
|
||||
|
||||
The VSCodium extension skeleton
|
||||
|
||||
Tell me which one you want first.
|
||||
289
vision-ext.md
289
vision-ext.md
@@ -0,0 +1,289 @@
|
||||
Final Direction Summary: Replacing Cursor for Your Use Case
|
||||
Core Goal
|
||||
|
||||
You want:
|
||||
|
||||
A Cursor-like chat experience
|
||||
|
||||
Integrated with:
|
||||
|
||||
your codebase
|
||||
|
||||
Google Cloud services
|
||||
|
||||
your product workflows
|
||||
|
||||
Without paying for Cursor or depending on OpenAI/Cursor infra.
|
||||
|
||||
We aligned on an approach that gives you this, while keeping costs, maintenance, and risk manageable.
|
||||
|
||||
The Chosen Architecture
|
||||
1. Use VSCodium as your editor base
|
||||
|
||||
Instead of Cursor or VS Code:
|
||||
|
||||
Open-source
|
||||
|
||||
Redistributable
|
||||
|
||||
No telemetry/licensing issues
|
||||
|
||||
Compatible with VS Code extensions
|
||||
|
||||
Lets you ship your own IDE experience
|
||||
|
||||
You are not building a new editor, you are building a product cockpit on top of a proven editor shell.
|
||||
|
||||
2. Build your product experience as an Extension (not a fork)
|
||||
|
||||
We agreed:
|
||||
|
||||
Extension-first is the right V1 strategy.
|
||||
|
||||
Because with an extension you can:
|
||||
|
||||
Add your own Product OS UI
|
||||
|
||||
Build your own chat interface
|
||||
|
||||
Integrate Gemini + GCP + tools
|
||||
|
||||
Ship cross-platform quickly
|
||||
|
||||
Avoid the heavy maintenance cost of a fork
|
||||
|
||||
A fork only becomes justified later if you need:
|
||||
|
||||
Hard shell changes
|
||||
|
||||
Locked-down layouts
|
||||
|
||||
Enterprise kiosk behavior
|
||||
|
||||
3. Use an Open-Source Chat UI Instead of Cursor
|
||||
|
||||
To avoid building chat UI from scratch, we landed on:
|
||||
|
||||
✅ Best starting point: Open-source chat extensions
|
||||
|
||||
You can reuse or extend:
|
||||
|
||||
Option A (Recommended)
|
||||
|
||||
Copilot Chat UI (open-sourced by Microsoft)
|
||||
|
||||
Production-grade chat UI
|
||||
|
||||
MIT license
|
||||
|
||||
Can be repointed to:
|
||||
|
||||
your backend
|
||||
|
||||
Gemini / Vertex AI
|
||||
|
||||
Gives you:
|
||||
|
||||
streaming responses
|
||||
|
||||
history
|
||||
|
||||
context-aware UX
|
||||
|
||||
Option B (Fast prototyping)
|
||||
|
||||
Continue
|
||||
|
||||
Open-source
|
||||
|
||||
Already works in VSCodium
|
||||
|
||||
Can connect to:
|
||||
|
||||
local LLMs
|
||||
|
||||
remote APIs (your Gemini backend)
|
||||
|
||||
Great for validating UX quickly
|
||||
|
||||
This gives you:
|
||||
|
||||
A Cursor-like chat UX without Cursor.
|
||||
|
||||
4. Gemini + Control Plane replaces Cursor’s backend
|
||||
|
||||
Instead of:
|
||||
|
||||
Cursor → OpenAI → Cursor tools
|
||||
|
||||
You will have:
|
||||
|
||||
VSCodium → Your Extension → Control Plane → Gemini (Vertex AI) + GCP Tools
|
||||
|
||||
Your backend becomes the intelligence layer:
|
||||
|
||||
/chat endpoint → Gemini
|
||||
|
||||
/tools/invoke → deploy, logs, analytics, campaigns, etc
|
||||
|
||||
policy enforcement
|
||||
|
||||
cost tracking
|
||||
|
||||
product-aware reasoning
|
||||
|
||||
This gives you:
|
||||
|
||||
full ownership
|
||||
|
||||
no vendor lock-in
|
||||
|
||||
better monetization control
|
||||
|
||||
5. Code Generation Does NOT require rebuilding everything
|
||||
|
||||
We clarified:
|
||||
You do NOT need to rebuild a full editor or execution engine to generate code.
|
||||
|
||||
You only need:
|
||||
|
||||
Minimal tooling:
|
||||
|
||||
Model returns:
|
||||
|
||||
structured diffs
|
||||
|
||||
optional commands
|
||||
|
||||
Extension:
|
||||
|
||||
previews changes
|
||||
|
||||
applies patches
|
||||
|
||||
optionally runs tests
|
||||
|
||||
Everything else (editing, git, terminals) is already provided by VSCodium.
|
||||
|
||||
So you get:
|
||||
|
||||
Cursor-like “generate code and apply it” behavior
|
||||
without building Cursor from scratch.
|
||||
|
||||
6. Direct Cloud Access: Use Signed URLs, Not Service Accounts
|
||||
|
||||
We aligned on:
|
||||
|
||||
Don’t give the IDE persistent cloud credentials
|
||||
|
||||
Use:
|
||||
|
||||
Control Plane → signed URLs → GCS
|
||||
|
||||
This gives you:
|
||||
|
||||
better security
|
||||
|
||||
easier monetization
|
||||
|
||||
easy migration later
|
||||
|
||||
avoids long-term risk
|
||||
|
||||
You can still have:
|
||||
|
||||
Direct data transfer
|
||||
without exposing cloud identities.
|
||||
|
||||
7. Product OS > Code Chat Only
|
||||
|
||||
You’re not just building a “code helper chat”.
|
||||
|
||||
You’re building a Product OS, where chat can:
|
||||
|
||||
generate code
|
||||
|
||||
deploy services
|
||||
|
||||
analyze funnels
|
||||
|
||||
generate campaigns
|
||||
|
||||
summarize experiments
|
||||
|
||||
optimize onboarding
|
||||
|
||||
respond to support tickets
|
||||
|
||||
That’s your differentiator over Cursor:
|
||||
|
||||
Cursor is a coding assistant
|
||||
You’re building a product automation cockpit
|
||||
|
||||
What This Means Practically
|
||||
|
||||
You will:
|
||||
|
||||
Run VSCodium
|
||||
|
||||
Install:
|
||||
|
||||
Your Product OS extension
|
||||
|
||||
An open-source chat UI (or embed it)
|
||||
|
||||
Connect it to:
|
||||
|
||||
Your Control Plane
|
||||
|
||||
Gemini on Vertex AI
|
||||
|
||||
Add:
|
||||
|
||||
Tool invocation
|
||||
|
||||
Product modules (marketing, analytics, growth, etc)
|
||||
|
||||
Ship:
|
||||
|
||||
A Cursor-free AI IDE focused on launching and running products
|
||||
|
||||
What You Avoid
|
||||
|
||||
By this approach, you avoid:
|
||||
|
||||
Paying Cursor per seat
|
||||
|
||||
Being locked into OpenAI routing
|
||||
|
||||
Forking VS Code prematurely
|
||||
|
||||
Owning an editor platform too early
|
||||
|
||||
Maintaining a custom compiler/distribution pipeline
|
||||
|
||||
Final Position
|
||||
|
||||
You do not need Cursor.
|
||||
|
||||
You can build:
|
||||
|
||||
A great chat interface
|
||||
|
||||
With code + GCP integration
|
||||
|
||||
On VSCodium
|
||||
|
||||
With open-source UI
|
||||
|
||||
Powered by Gemini
|
||||
|
||||
And fully controlled by you
|
||||
|
||||
If you’d like, next I can:
|
||||
|
||||
Lay out a concrete build roadmap (V1 → V3)
|
||||
|
||||
Or give you a minimal stack diagram + repo layout
|
||||
|
||||
Or produce a starter technical spec for your Product OS Chat + Tooling platform
|
||||
Reference in New Issue
Block a user