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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.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
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 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.
| name | powerbi-autonomous-forecast-agents |
| description | 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 this skill when the user wants an agent council rather than one blended forecast. The goal is a structured debate that exposes why the forecast is high, low, risky, or trustworthy.
ai-forecast-summary.csv, ai-forecast-detail.csv, ai-forecast-top-deltas.csv, and ai-forecast-model-quality.csv when available.forecast_monthgraincustomerproductagent_nameagent_forecastagent_confidenceevidencerisk_flagrecommended_actionarbitrated_decisionadvisory_only.