Kann ich BrandGEO vibecoden?

Build me a local AI brand visibility auditor for one brand. Requirements:

- Node 22, TypeScript, better-sqlite3, a CLI. Local only, no accounts, no telemetry.
- brand.json holds my brand name, aliases, domain, one competitor, and up to 25
  audit questions (who is X, best tools for Y, X vs competitor, is X legit).
- `audit run` sends every question through OpenAI, Anthropic, Gemini, xAI, and
  DeepSeek APIs. Keys from .env; skip engines whose key is missing and say so in
  the report instead of failing.
- Store one immutable row per run, question, and engine: raw answer, model id,
  latency, error text. Never overwrite a previous run.
- Cap concurrency at 2 per engine and retry twice on 429 and 5xx with backoff.
- A separate scoring pass grades each stored answer 0-10 on six dimensions:
  recognition, knowledge depth, competitive context, sentiment, contextual
  recall, discoverability. Rubric text lives in rubric.md, scores must cite the
  answer sentence that justifies them.
- `audit report` renders a Markdown report: overall score per engine, the
  six-dimension table, competitor mentions, and every flat-out wrong claim the
  models made about the brand, quoted.
- `audit history` prints score per engine across runs from SQLite, so week two
  starts meaning something.
- Out of scope: white-label PDFs, multi-brand management, scheduled monitoring,
  and scraping the consumer web UIs. One brand, run by hand.
- README: setup, per-run cost estimate by engine, and a plain note that API
  answers only approximate what the apps actually show users.

Antworte mir auf Deutsch. Oberfläche, Kommentare und README auf Deutsch, alle Bezeichner wie Tabellen, Spalten, Variablen, Routen und Dateinamen bleiben Englisch.