data-analytics

Assess whether structured data, query results, dashboards, or analytical evidence are trustworthy enough to use. Use when the task is to check data quality, reconcile conflicting sources or metric definitions, or decide whether evidence is safe to cite.

Build source-backed dashboards for monitoring performance, exploring drivers, or acting on product and business metrics. Use when the task needs a dashboard, scorecard, or monitoring view with clear metrics, filters, source definitions, and QA.

Build polished analytical reports for executive, product, business, or technical audiences. Use when the task needs a durable answer-first narrative with evidence-backed findings, visuals or tables, caveats, and source context.

Create, update, inspect, or repair Data Analytics semantic layers. Use when the user asks to save data context or create a semantic layer that future Data Analytics work can inspect and cite.

Design KPI frameworks, metric definitions, targets, guardrails, and measurement plans for product or business decisions. Use when success metrics, drivers, guardrails, targets, or the measurement approach need to be defined or improved.

Gather business context from connected or provided sources so downstream analysis starts with the right framing. Use when an analytical question depends on missing context, such as what a metric means, what changed recently, or which sources should be checked. If the same prompt asks for diagnosis, recommendation, or a deliverable, gather context first and continue to the focused skill.

Route Data Analytics plugin-level requests and broad analytics work to the right focused workflow. Use when Data Analytics is at-mentioned, or for analytics requests involving data, metrics, dashboards, reports, charts, notebooks, spreadsheets, KPIs, market sizing, or semantic layers.

Create, edit, or validate reproducible SQL or Python notebooks. Use for notebooks, SQL/Python scratchpads, reproducible exploration, audit trails, or runnable companions where the analysis should be reviewable or rerunnable.

Prepare KPI readouts, scorecards, WBR/MBR/QBR updates, and executive summaries from quantitative business or product metrics; use when the task is to report status, compare against targets, explain validated drivers, and state operating implications.

Estimate market, segment, or opportunity size with transparent assumptions and uncertainty. Use for TAM/SAM/SOM, sizing scenarios, or comparing the scale of possible opportunities.

Diagnose why a metric changed or differs from expectation. Use when the task is to identify likely drivers of a metric movement, anomaly, gap, or discrepancy.

Analyze product or business data to support a decision or recommendation. Use when a decision depends on metric-backed evidence, such as choosing a direction, prioritizing an opportunity, evaluating a change, segmenting users, sizing tradeoffs, or deciding what to do next.

Publish a validated Data Analytics report or dashboard artifact through Sites. Use automatically for durable artifacts in any positively identified Work Mode when the full Sites lifecycle is callable, or in ChatGPT Desktop outside Work Mode only after an explicit Sites request or acceptance of the optional post-handoff sharing offer.

Validate whether an analysis is accurate, well-supported, and ready to share or use for a decision. Use when reviewing methodology, calculations, comparisons, visuals, caveats, or conclusions.

Design, build, revise, or QA quantitative charts and figures. Use when an analytical answer needs visual judgment, whether for an inline answer, report, dashboard, notebook, or artifact.