CodexPluginPowerBI

Use when working with Microsoft Power BI Desktop files or projects, including PBIX/PBIT inventory, DAX and Power Query review, semantic model documentation, Tabular Editor workflows, and local Desktop environment checks.

Use when storing and reusing Power BI planning memory across cycles, including forecast versions, assumptions, actions, overrides, actuals, error causes, segment lessons, and durable learning signals for autonomous planning.

Use when coordinating multiple AI forecasting perspectives for Power BI sales forecasts, including backlog, seasonality, budget, sales skepticism, risk, consensus, dissent, and explainable forecast arbitration.

Use when turning autonomous Power BI planning deviations into managed exceptions, including revenue gaps, risky backlog, unrealistic targets, biased models, sparse segments, owner hints, status, escalation, and closure evidence.

Use when building or running a closed-loop autonomous Power BI planning cycle that refreshes actuals, forecast, gap detection, scenarios, actions, tracking, learning, and the next plan without changing PBIX files directly.

Use when adding causal and counterfactual thinking to Power BI sales forecasts, including working days, holidays, delivery constraints, price changes, product lifecycle, stockouts, campaigns, customer behavior, and best/base/worst case simulations.

Use when constraining Power BI revenue planning by delivery, capacity, stock, supply, margin, cash, payment terms, sales resources, or operational feasibility rather than forecasting unconstrained demand only.

Use when designing or evaluating a Power BI forecast trust market, human override tracking, sales and finance confidence scoring, forecast accountability, and self-learning model-vs-human accuracy loops.

Use when building a Power BI forecast war room or control tower for executive revenue review, forecast risk triage, top deltas, action ownership, gap closure status, confidence monitoring, and daily forecast operating rhythm.

Use when calculating what must happen to reach Power BI revenue, budget, roll, margin, cash, or service targets, including reverse planning, target-gap decomposition, required backlog conversion, and required demand uplift.

Use when scoring whether a Power BI model and forecast pipeline are ready for autonomous planning, including data quality, backlog snapshots, order-to-invoice matching, model accuracy, bias, granularity, constraints, overrides, and action tracking.

Use when designing or running a Power BI revenue digital twin for sales forecasting, target-gap simulation, backlog-to-invoice scenarios, what-if revenue paths, budget attainment, and forecast-to-cash decision support from an open Power BI Desktop model or exported forecast CSVs.

Use when turning Power BI sales forecast gaps into operational rescue actions, including backlog acceleration, customer activation, product substitution, delivery escalation, gap closure ranking, and forecast-to-action recommendations.

Use when Power BI forecast governance should automatically demote biased models, choose safer baselines, trigger data-quality blockers, adjust trust status, and protect planning from low-quality autonomous recommendations.