Excel weekly dashboards at scale

**Judgement:** This skill is a strong, focused choice for building repeatable weekly Excel dashboards driven by Power Query and folder-based ingestion. It is best suited to structured operational KPIs where the main challenge is making refresh reliable and low-touch rather than doing complex analytics. **What it does well:** - Provides a full lifecycle plan: ingestion → normalization → validation → pivot dashboard → refresh health checks. - Explicitly targets common Excel pain points (folder ingest, changing columns, ISO week handling, validation of known bad patterns). - Encourages safe, auditable designs with staging/clean queries and a Refresh_Status sheet with row counts and error flags. - Defaults to returning build plans and M-code templates instead of directly editing files, which is appropriate for automation and review. **Key risks / limitations:** - Not suitable for advanced forecasting, valuation, or complex analytics—it is reporting/pipeline focused only. - Not a replacement for full BI stacks (Power BI/Tableau) where richer modeling, security, or distribution are required. - Relies on the user to provide structured exports for DOCX/PDF; extraction quality is explicitly uncertain without them. - Requires the user to clearly define KPIs, week definition (ideally ISO), and stable keys; the skill will pause and ask if these are ambiguous. **Recommended scenarios:** - You have recurring weekly CSV/XLSX exports from a system (e.g., payroll, operations, sales) and want a durable Excel dashboard that keeps working as new weeks and minor schema changes arrive. - You need to retrofit an existing fragile Excel model into a more maintainable, Power Query–driven design with validation and clear error surfacing. - You want a standardized “KPI pack” template that multiple teams can reuse by pointing it at their own folder of exports. **Less suitable scenarios:** - One-off analytical projects where building a robust refresh pipeline isn’t worth the upfront design. - Use cases needing heavy statistical modeling, machine learning, or enterprise BI capabilities beyond Excel’s pivot/reporting layer. - Workflows that depend primarily on web scraping or unstructured document parsing rather than clean exports.