We built Autowin by putting models in a loop: discover upgrades → test them → judge them with an assembly of adversarial sub-agents — and we ran that very process on itself, iterating the kit until each piece held up under its own review. The result: from one loosely-defined need, you get back work that has been built, tested, and scored by a panel of judge sub-agents — with closure authority kept outside the model (deterministic checks + you), so an "it's done" is never just taken on faith.
Three reviewer lenses audit an AI agent's or contractor's done report, cross-examine each other, and give an accept/do-not-accept recommendation.
以現有庫存、預留量、確認到貨與自訂每日消耗試算庫存天數；不預測需求或自動下單。 Calculate inventory runway using on-hand stock, reservations, confirmed receipts and your chosen daily consumption; no demand forecasting or automatic ordering.
Upload an Excel model (.xlsx/.xlsm) and get a severity-ranked audit with exact cells: broken references, SUM ranges that stop short, stale totals, formulas that break their pattern, typed numbers among formulas, hidden data, external links and manual calculation. A deterministic offline script does the checks, so results are repeatable. Checks mechanics, not business assumptions.
Run 6 delivery checks on every AI-generated image, catch repeated poses across the set, and get a short list of which images to send back.
Review a diff or pull request for real bugs before merge: four reviewer lenses (one model applying each in turn) check each other and tag findings CONFIRMED, CORROBORATED or DISPUTED.
Academic sanity-check with interactive HTML dashboard. Detects look-ahead bias, over-parameterization, and benchmarks against 10 canonical blueprints via animated SVG gauges and color-coded KPI cards. Parses natural language, code, or CSV uploads. Output auto-adapts (EN/ZH-TW/ZH-CN). Built on De Prado (2018). For students and strategy developers. Not financial advice.