Power BI Desktop

Plugin powerbi-desktop in rweisssieker-xp/CodexPluginPowerBI. Commands read from the repo's manifest — 12 days ago.

Skills (16)

• powerbi-autonomous-exception-management

  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.

• powerbi-autonomous-forecast-agents

  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.

• powerbi-autonomous-planning-loop

  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.

• powerbi-causal-counterfactual-forecasting

  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.

• powerbi-constraint-aware-planning

  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.

• powerbi-desktop

  Use when building Power BI models, trusting KPIs, changing PBIP safely, releasing, operating Fabric, or governing decisions.

• powerbi-fabric-nextgen-usps

  Use when a Power BI or Fabric team needs FinOps, Copilot answer regression, Direct Lake readiness, data-product SLOs, capacity change proof, or executive decision traces.

• powerbi-forecast-trust-market

  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.

• powerbi-forecast-war-room

  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.

• powerbi-goal-seeking-planning

  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.

• powerbi-model-wizard

  Use when starting a new Power BI model from a business goal, source list, star schema, KPI plan, security plan, and report-page plan.

• powerbi-planning-memory

  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.

• powerbi-planning-readiness-score

  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.

• powerbi-revenue-digital-twin

  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.

• powerbi-revenue-rescue-mode

  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.

• powerbi-self-healing-forecast-governance

  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.

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