Can AI replace Mangools?

Build a closest honest personal substitute for Mangools in an empty repository.
Use Python 3.12, FastAPI, PostgreSQL, Playwright, and a small React frontend; do not offer alternative stacks.
The core loop is: organize keyword ideas, track a user-supplied keyword set with low-volume rank checks from a compliant data provider, and show trends without pretending to recreate a commercial web index.
Make the first run work locally with one documented command.
Store all user data locally by default and make export straightforward.
Put secrets in .env, ship .env.example, and never commit credentials.
Create projects with domains, keywords, target country, language, device, and tags.
Fetch rankings only through the configured compliant API and enforce a daily budget.
Store raw result snapshots and normalized positions so every chart is auditable.
Display current rank, movement, best rank, URL changes, and a compact SERP history.
Import backlink and keyword files from third-party tools without claiming independent coverage.
Add scheduled runs, failure alerts, CSV export, retention settings, and database backups.
Include clear empty, loading, success, and recoverable error states.
Add input validation, safe filenames, and graceful handling of unavailable APIs.
Write focused tests for the core transformation and one end-to-end happy path.
Create a README with setup, architecture, permissions, data location, and backup steps.
Do not add accounts, billing, telemetry, analytics, or a hosted control plane.
Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure.
Deliberately leave out building a general web crawler or backlink index.
Deliberately leave out circumventing search-engine access controls.
Deliberately leave out traffic estimates presented as observed first-party data.
Finish by running the tests and listing the exact commands used.