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Ladder / Analytics / Data Analytics (role plugin)

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Data Analytics (role plugin)

Metric diagnostics, data quality analysis, KPI design and reporting, dashboards, reports, notebooks, market sizing, business context gathering.

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What it does

The data-analytics plugin from OpenAI's role-specific-plugins repository, packaged on its own. Fourteen skills, with metric-diagnostics and analyze-data-quality the most rigorous; also design-kpis, kpi-reporting, build-dashboard, build-report, visualize-data, jupyter-notebooks, validate-data, product-business-analysis, market-sizing, gather-business-context and create-data-context. Written for Codex, so cross-references use Codex's skill syntax and connector placeholders; the methods read fine in any client. The upstream repository is archived, so this is a snapshot that will not receive upstream updates. MIT. OpenAI has not reviewed or endorsed this listing.

How this listing got here. Packaged from the public repository openai/role-specific-plugins at commit fe5608d, under its MIT license, bundled verbatim. New upstream releases are repackaged after review, never synced from HEAD. If this is your project, you can claim it or have it removed.

Skills in this package 14

  • analyze-data-quality

    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-dashboard

    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-report

    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-data-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-kpis

    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

    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.

  • index

    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.

  • jupyter-notebooks

    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.

Show all 14 skills
  • kpi-reporting

    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.

  • market-sizing

    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.

  • metric-diagnostics

    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.

  • product-business-analysis

    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.

  • validate-data

    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.

  • visualize-data

    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.

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Versions

v0.0.0+fe5608dIndexed from openai/role-specific-plugins at fe5608d · 2026-09-24