Frontier

Build with AI: Developer Experience Dashboard

Reproduce and customise the developer demo with an AI coding agent, or assess real GitHub query inputs without assuming an export schema.

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Purpose

Reproduce and customise the synthetic developer dashboard with its existing R implementation, and assess real GitHub query inputs without guessing their contract.

Audience

Engineering managers and people analytics practitioners studying working conditions alongside GitHub and Microsoft 365 Copilot use.

When to use

Choose this journey for the Developer Experience reference report, not direct GitHub API analytics or an evaluation of developer productivity.

Required inputs

• A local sample-code checkout containing the shared dashboard runner.
• R and the dependencies listed by the runner; Pandoc for demo rendering.
• Demo: no customer files; the copied helper creates synthetic data.
• Real readiness assessment: approved local CSVs and available documentation for grain, identity, metrics, eligibility and coverage.

Assumptions

The demo uses synthetic values in confirmed export headers. Weekly M365 eligibility uses enabled days; completeness requires independent evidence. GitHub row presence is observation, not provisioning, and GitHub activity coverage is resolved separately from GitHub credit coverage: the credit export is sparse, so sparse credit rows must never void an observed activity week. Breakdown equality and model attribution are synthetic-only, and breakdown category membership is a persistent cohort property rather than a per-day draw. PeopleHistoricalId is opaque: read the activity-file crosswalk instead of reconstructing it from PersonId. M365 and GitHub credits remain separate units. Real GitHub data can be inspected, but v1 has no verified real build adapter.

Recommended output

Demo mode produces a self-contained HTML report, copied source and provenance. Real mode produces an input-readiness assessment and missing-evidence list; it does not promise a complete report.

Quick prompt (short version)

Help me reproduce or customise the Developer Experience and Copilot 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 GitHub manifest and shared R runner; preserve coverage, privacy and synthetic labels.
For real GitHub files, inspect only and explain the missing contract evidence; v1 has no verified real build adapter.
Do not infer productivity, code quality, burnout, licensing or non-use from activity alone.

Prompt

Use the Developer Experience and Copilot reference in my local sample-code checkout.
Recover choices already supplied, then use prompt mode for checkout/output locations,
demo or real, audience, available files, period, grouping, privacy policy and intended changes.

Read frontier-analytics/skills/viva-insights-copilot-dashboards/SKILL.md, its selected mode
and GitHub manifest, and the shared analytical contract. Use the shared runner README
at frontier-analytics/starter-kits/copilot-query-dashboard/ for exact commands/config.

In demo mode, reproduce the copied Rmd/helper in an isolated workspace. Keep the seed,
synthetic window, full developer baseline, eligibility and coverage distinctions.
In real mode, run bounded local inspection. Identify source grain, join keys, units,
coverage, licence evidence and model/language allocation periods. Headers alone do not
verify semantics. Return supported observations and missing evidence for a future adapter.
Do not source the synthetic helper on real inputs or bypass the unsupported-build gate.

For customisation, edit the copied source, not bundled HTML or the canonical example.
Presentation changes should preserve aggregate values. Population or metric changes
require renewed inspection and my approval. No arbitrary duplicate removal, fuzzy
employee joins, individual rankings or converting unresolved coverage to zero.
Keep at least 10 distinct people per published group or a stricter organisational rule.
Do not interpret acceptance as quality, available time as coding, or after-hours as burnout.

Record revision, inputs and configuration. Avoid loading whole CSVs or generated HTML
into model context. After two unsuccessful repairs of one failure, report the blocker.
Return the report or readiness assessment, rerun command and limitations; do not publish.

Adaptation notes

Try changing the panel order or explanatory language on the synthetic demo first. Real-data support needs a source contract and a separately reviewed adapter. Do not rename public GitHub API fields into an assumed Viva contract.

Common failure modes

• Missing activity becomes zero: require independent coverage and eligibility.
• Only Copilot users form the baseline: retain the full developer population.
• Full-window mixes become weekly trends: preserve their actual period.
• Demo runs over customer data: use distinct modes and output workspaces.

Resources

• Starter kit
• Shared runner
• Optional skill installation
• Synthetic schema manifest

🔒 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.