Frontier

Build with AI: Consumption Dashboard

Reproduce the Consumption demo or build a scoped credits dashboard with an AI coding agent and reusable R code.

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Purpose

Reproduce the synthetic seven-page R demo, then customise it or build a narrower credits dashboard from documented Consumption exports without starting over.

Audience

People analytics practitioners and analysts exploring Copilot consumption.

When to use

Choose this journey for the Consumption query, not the general Person Query adoption dashboard. A coding agent can follow the workflow without installing a skill; frequent users can install the optional dashboard skill.

Required inputs

• A local checkout of microsoft/viva-insights-sample-code containing the shared runner at frontier-analytics/starter-kits/copilot-query-dashboard/.
• R and the dependencies listed by the runner; Pandoc for the reference demo.
• Demo: supplied synthetic fixture files only.
• Real: locally extracted activity and people-metadata CSVs, query grain, date window, chosen HR group, mapping evidence and privacy requirements.

Assumptions

The demo fixtures carry synthetic values in the real Consumption schema, so the demo and a governed export describe the same fields. Tokens and delegated task types are not part of that schema and are not present in the demo; do not expect or reconstruct them.

Recommended output

A self-contained HTML report, reproducible config and source, local provenance, validation summary, and explicit supported/excluded analyses. Real data stays outside the repository and is never pasted into the prompt.

Quick prompt (short version)

Help me reproduce or customise the Consumption and Ways of Working dashboard.
Recover choices already supplied, then ask only for missing checkout, demo versus real mode, output folder and intended changes.
Read frontier-analytics/skills/viva-insights-copilot-dashboards/SKILL.md in that checkout as workflow instructions, without requiring skill installation.
Use its Consumption manifest and the shared R runner; do not regenerate the dashboard from scratch.
For real exports, inspect locally, show supported and excluded panels, and get my approval before building.
Do not invent tokens, licensing, coverage, identity mappings or causal conclusions.

Prompt

Build a Consumption dashboard using the local microsoft/viva-insights-sample-code checkout.
Recover any inputs I already supplied. Use prompt mode for the remaining choices:
checkout path, demo or real, audience, input files, output directory, period, grouping,
privacy threshold (at least 10 distinct people), and requested customisations.

Read frontier-analytics/skills/viva-insights-copilot-dashboards/SKILL.md and only
the selected mode plus Consumption manifest. Read the shared query contract it links.
Use frontier-analytics/starter-kits/copilot-query-dashboard/README.md for the config
schema and dashboard.R commands. Record the checkout revision and source fingerprints.

For demo mode, reproduce the reference using copied synthetic fixtures in a new output
workspace. Keep the fixed seed/window and synthetic labels. Do not alter canonical source files.
For real mode, inspect the extracted CSVs locally and return bounded schema summaries,
not rows or identifiers. Verify keys, grain, units and historical metadata joins.
Present supported/excluded panels and mapping evidence; wait for my approval to build.
Build only the supported M365 credits panels in real v1; sessions, Person Query
associations and GitHub panels require separate verified adapters. Do not generate
synthetic replacements, infer licences from activity, or sum policy limits.

Use the existing R code. Check dependencies before requesting installation. Do not read
the generated HTML or whole CSVs into model context. No individual rankings or unsupported
productivity, ROI, quality, burnout or causal claims. Suppress small groups in every output.
After two unsuccessful repairs of the same failure, stop with the specific blocker.
Return the local report, rerun command, config/provenance and limitations. Do not publish.

Adaptation notes

Start with “change the colours and section order” for a presentation-only iteration. Changing dates, grouping or sources requires renewed data and privacy checks. Person Query associations require a separately verified adapter.

Common failure modes

• Assuming credit exports contain tokens: build only supported panels.
• Joining service rows directly to Person Query: aggregate to compatible person-period grain first; a joined real-data adapter is not supplied in v1.
• Rendering in the examples folder: use the isolated runner workspace.
• Calling credits money or value: retain credit units and descriptive claims.

Resources

• Starter kit
• Shared runner
• Optional skill installation
• Public Consumption schema

🔒 Responsible use & data privacy

• Suppress small groups. Apply a minimum group-size threshold to every breakdown, segment, and chart — Viva Insights typically uses 5–10. Raise it for smaller organizations.
• Never expose individuals. Do not print raw UPNs or email addresses; hash, truncate, or report only aggregates. Person query PersonId is already de-identified, but Purview UserId is not.
• Analyze groups, not individuals. These outputs are for understanding cohorts and trends — not for evaluating, ranking, or monitoring named employees.
• Keep data in approved environments. Don't paste sensitive HR data into cloud agents unless your organization's policies allow it; prefer local or enterprise-hosted tools. Validate every agent output before sharing.