Qa dashboard

<objective> Build dashboards that drive decisions, not dashboards that display data. The failure mode this prevents: a wall of panels showing "5 failures" with no link to the failures, no target, and no trend — a notification dressed up as a tool that everyone stops opening by week three. This skill delivers audience-specific QA reporting — rich HTML reports (Allure), real-time trend panels with regression alerts (Grafana/InfluxDB), self-hosted AI-assisted aggregation (ReportPortal), and automated stakeholder summaries — each panel mapped to a question someone actually asks and every red indicator drilling down to the failing test. </objective>

Quick Route

Situation	Go to
Already on Grafana for infra metrics	Grafana Dashboards — add test metrics alongside prod
Test runner has a hosted dashboard (Cypress/Playwright)	SaaS-Native Dashboards — least plumbing
Need rich HTML report, no infra to run	Allure Report — generate in CI, publish artifact
Need self-hosted aggregation across frameworks + AI triage	ReportPortal
Need a single view across multiple runners	Grafana or Allure (cross-runner aggregation)
Need a weekly summary / release verdict for stakeholders	Stakeholder Reports

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Discovery Questions

Check .agents/qa-project-context.md first — if it exists, use it and skip anything answered there. Then:

1. What tool do you use for test reporting today? Console output, JUnit XML, HTML reports, or a dedicated platform? Identifies the starting point and how much plumbing is left.
2. Who will look at the dashboard? Developers need failure details and traces; QA leads need trends and flakiness; leadership needs release confidence and defect rates. Each audience needs a different view.
3. What decisions should the dashboard drive? "Is this build safe to release?" "Which tests need fixing?" "Is quality improving sprint over sprint?" Dashboards without a decision context become shelfware.
4. Where do test results live? GitHub Actions artifacts, S3, a database? The storage location determines which dashboard tool is practical.
5. What CI platform? GitHub Actions, GitLab CI, Jenkins? Each has different artifact and reporting integrations.

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Core Principles

1. Dashboards answer questions, they do not display numbers. Every panel must map to a question someone actually asks. "What is the flakiness rate?" is a question. "Total test count" is trivia.

2. Different audiences need different views. A developer debugging a CI failure needs stack traces, screenshots, and traces. A VP needs a single number: "Are we ready to release?" Do not force both through the same dashboard.

3. Real-time for CI, trends for leadership. CI dashboards update on every pipeline run. Leadership dashboards aggregate weekly or per-sprint. Mixing cadences confuses both audiences.

4. Drill-down to action. Every red indicator must link to the specific failing test, the specific flaky test, or the specific coverage gap. A dashboard that shows "5 failures" but does not link to the failures is a notification, not a tool.

5. Automate report generation. Reports that require manual effort (running scripts, copying data, formatting slides) will not survive the first busy sprint. Generate reports from CI pipelines automatically.

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Allure Report

Allure generates rich HTML reports from test results with history, categories, and retries built in. It works with Playwright, Jest, Vitest, pytest, and most frameworks.

Allure 2 vs Allure 3. Two paths, and they are easy to mix up because the framework adapters (allure-playwright, allure-vitest, allure-jest) emit the same Allure 2 result files; only the reader differs:

• Allure 2 path — allure-commandline (2.42.1, Jun 2026). brew install allure installs this, not v3. Commands: allure generate / allure open / allure serve; categories via a categories.json dropped into allure-results/. Stable, frozen, dependency-bump-only now.
• Allure 3 path — allure npm package + allurerc.mjs (3.9.0, May 2026). TypeScript rewrite: plugins, single-file config, real-time allure watch, project-wide quality gates, multi-environment reports, and Allure Service for server-side history. Commands: npx allure run / npx allure generate / npx allure watch; categories move into allurerc.mjs (the dropped-in categories.json is a v2 concept).

Choose Allure 3 for new projects. If you follow only the brew install allure / allure generate commands you are on the v2 path — that is fine and fully supported; just know which one you are running.

Minimal Playwright reporter wiring — register the adapter as reporter: ["allure-playwright", { outputFolder, environmentInfo }] so every run drops results into allure-results/ with the environment captured:

// playwright.config.ts
reporter: [
  ["list"],
  ["allure-playwright", {
    outputFolder: "allure-results",
    environmentInfo: {
      Environment: process.env.TEST_ENV ?? "local",
      BaseURL: process.env.BASE_URL ?? "http://localhost:3000",
    },
  }],
],

Add per-test metadata (allure.severity / feature / story / tag) to drive grouping, define failure categories to split product bugs from infra breakage, and preserve history/ across CI runs so trends exist at all. Without history an Allure report is a single snapshot — no trends, no intermittent-failure detection. See references/allure.md for the full Playwright/Vitest configs, the v2 categories.json, the v3 allurerc.mjs + allure run runnable path, and the GitHub Actions history-preservation steps.

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Grafana Dashboards

Grafana gives real-time dashboards with alerting. Best when the team already runs Grafana for infrastructure and wants test metrics next to production metrics.

Data pipeline: a post-test CI step parses results (JUnit XML, coverage JSON, timing) and pushes points to a time-series DB (InfluxDB or a Prometheus pushgateway); Grafana queries those. The push script writes two measurements — test_execution (one point per test, tagged by suite/test_name/status/branch/run_id) and test_run_summary (one point per run with pass_rate, total, failed, avg_duration_ms). Tag every point with branch and run_id so panels can filter to main and link back to a specific run.

The script runs under if: always(), so wrap the write loop in try/flush/close — a throw mid-loop otherwise loses every buffered point. See references/grafana.md for the full push-test-metrics.ts (with the flush guard) and the GitHub Actions step.

Recommended panels

Panel	Question	Query shape
Pass Rate Trend (time series)	Is quality improving?	SELECT mean("pass_rate") FROM "test_run_summary" WHERE "branch"='main' GROUP BY time(1d), thresholds at 95% (yellow) / 99% (green)
Release Readiness (stat)	Is main ready to release?	SELECT last("pass_rate") FROM "test_run_summary" WHERE "branch"='main', red <95 / yellow 95–99 / green ≥99 — pair with a coverage stat ≥80
Flakiness Top 10 (table)	Which tests waste the most time?	SELECT "test_name", count("retries") AS retry_count FROM "test_execution" WHERE "retries">0 AND time>now()-14d GROUP BY "test_name" ORDER BY retry_count DESC LIMIT 10
CI Duration Trend	Is the pipeline getting slower?	avg_duration_ms over time with a target line at 600s

Full queries plus Coverage Trend and Duration Distribution panels are in references/grafana.md.

Alerting

Provision alert rules as YAML under provisioning/alerting/ (Grafana 11+). The one the Done When requires: main pass rate drops more than 2 percentage points in a single day. Build it from two queries (mean pass_rate over the last 1d vs the day before) feeding a math expression $yesterday - $today > 2, then route via a Slack contact point (incoming-webhook URL). The full provisioned rule + contact point is in references/grafana.md. Also alert on: pass rate below 95% (10m window), CI duration above 15 min, coverage drop >2% in a week. Dashboards are for investigation; alerts are for detection — a dashboard no one opens catches nothing.

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ReportPortal

Self-hosted test reporting platform with ML-powered failure analysis, cross-framework aggregation, and real-time dashboards.

# Pin to a tagged release (current 26.0.x line: 26.0.3). The `master` branch may not
# match the supported 26.x line.
curl -LO https://raw.githubusercontent.com/reportportal/reportportal/26.0.3/docker-compose.yml
docker compose up -d
# Access at http://localhost:8080 (default: superadmin/erebus)
# ML auto-analysis lives in the `service-auto-analyzer` container — confirm it is running.

As of 26.0.3 ReportPortal also ingests agentic test results (launches carry an AGENTIC vs AUTOMATION execution-type badge) — relevant if part of your suite runs through Claude Code or another agent.

Playwright integration

// playwright.config.ts
reporter: [
  ["list"],
  ["@reportportal/agent-js-playwright", {
    apiKey: process.env.RP_API_KEY,
    endpoint: process.env.RP_ENDPOINT ?? "http://localhost:8080/api/v1",
    project: "my-project",
    launch: `E2E Tests - ${process.env.CI ? "CI" : "local"}`,
    attributes: [
      { key: "branch", value: process.env.GITHUB_HEAD_REF ?? "local" },
      { key: "build", value: process.env.GITHUB_RUN_ID ?? "dev" },
    ],
  }],
],

Install with npm i -D @reportportal/agent-js-playwright.

Feature	What it does
Auto-analysis	ML failure classification: product bug, test bug, system issue, or to-investigate
Defect type mapping	Custom defect categories with sub-types for your project
Flaky test detection	Tests that flip pass/fail across launches
Merge launches	Combine sharded CI runs into one unified view
Quality gates	Pass/fail criteria per launch (max failures, min pass rate)
Comparison	Side-by-side of two launches to spot regressions

Quality gates are queryable after a run (GET /api/v1/$PROJECT/launch/$LAUNCH_ID/quality-gate) — use the status as a CI gate and fail the pipeline if it is not PASSED.

Allure TestOps (managed alternative)

If self-hosting feels heavy, Allure TestOps (26.2.x line, 2026) is the SaaS path: Allure 3 quality gates, named environments, global attachments, and Allure 3-style flaky detection (flags a test once it shows ≥3 status transitions across its last 10 runs). Its MCP server is in public beta (26.1.1), letting AI agents query launches and quality gates directly — relevant when your QA workflow runs through Claude Code / Cursor.

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SaaS-Native Test Dashboards

If your test runner has a first-class hosted dashboard, prefer it over Allure/Grafana for that runner's native data — less plumbing, more retention, built-in PR comments. Cross-pollinate with Allure/Grafana only for cross-runner aggregation.

Platform	Test runner	Native data + PR comments
Cypress Cloud	Cypress	Test replay, parallelization, flake detection; AI add-on (Auto Heal, Bug Triage)
Currents.dev	Cypress, Playwright	OSS-friendly Cypress Cloud alternative; lower price point
Playwright HTML + --reporter=blob	Playwright	Free, self-hosted; combine shards with merge-reports
Datadog Test Optimization	Any (CI-side)	Flaky Test Management (now with Bits AI auto-fix), TIA, native APM
Allure TestOps	Any	Allure 3 quality gates, named environments, MCP server beta

Combining sharded Playwright runs (free, native). Have each shard emit a blob report, then merge into one HTML report — the no-cost answer to "combine sharded CI runs":

# each shard: npx playwright test --reporter=blob   (uploads blob-report/ as an artifact)
npx playwright merge-reports --reporter html ./blob-reports

Use Allure or Grafana when you need one dashboard across multiple runners, or when a SaaS option's pricing/data-residency does not fit. Otherwise the SaaS-native dashboard is usually the cheapest path to PR-level signal.

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Stakeholder Reports

Weekly QA Summary — automate via a scheduled CI job. Include: pass rate + trend, new vs fixed failures, top 5 flaky tests, coverage delta, avg CI duration. Classify health: STABLE (>= 98%), NEEDS ATTENTION (>= 95%), CRITICAL (< 95%). Post to Slack automatically.

Release Quality Report — generate before each release. Gate on: E2E pass rate >= 99%, unit pass rate 100%, branch coverage >= 80%, zero critical bugs, major bugs <= 2, and the Core Web Vitals budget. Output a READY / NOT READY verdict with a per-gate pass/fail breakdown.

Core Web Vitals "good" thresholds for the perf gate (current as of 2026): LCP < 2500ms, INP < 200ms, CLS < 0.1. Gate on INP, not FID — INP replaced FID as a Core Web Vital on 2024-03-12 and FID was fully retired on 2024-09-09.

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Recommended Dashboard Panels

A practical set covering the most common questions teams ask.

Panel	Question It Answers	Data Source	Audience
Pass/Fail Trend	Is quality improving or degrading?	CI test results over time	Everyone
Flakiness Top 10	Which tests waste the most time?	Tests with retries in last 14 days	Developers, QA
Coverage Heatmap	Where are we blind?	Coverage by module/directory	Developers
Defect Escape Trend	Are bugs reaching production?	Incidents tagged as test escapes	QA leads, Leadership
CI Duration	Is the pipeline getting slower?	Pipeline duration over time	DevOps, Developers
Test Velocity	Tests proportional to features?	New tests added per sprint	QA leads
Failure Categories	Product bugs or test infra?	Categorized failure reasons	QA leads
Release Readiness	Can we ship?	Composite score from all gates	Leadership

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Anti-Patterns

Dashboard with 30 panels. No one reads it. Start with 5–6 panels that answer the most urgent questions; add panels only when someone asks one the dashboard cannot answer.

Metrics without context. "Pass rate: 97%" means nothing without "target: 99%" and "last week: 98.5%." Every metric needs a target and a trend to be actionable.

Manual report generation. If the weekly summary needs someone to SSH in, run queries, and paste into slides, it stops happening by week 3. Automate it into CI.

Same dashboard for developers and leadership. Developers need failure details, stack traces, and repro steps; leadership needs one traffic light. Build separate views.

Reporting test counts as progress. "We added 200 tests" says nothing about quality. Report critical-path coverage, defect escape rate, and mean time to detect regressions instead.

No alerting on regressions. A dashboard no one checks is useless. Alert on pass-rate drops, coverage decreases, and CI-duration increases. Dashboards are for investigation; alerts are for detection.

Allure without history. A single Allure report is a snapshot — no trends, no intermittent-failure detection, no measure of improvement. Always preserve history/ across CI runs (or use Allure 3 / Allure Service for server-side history).

Mixing the Allure 2 and 3 code paths. Following the v3 callout in prose but copying brew install allure + allure generate + a dropped-in categories.json lands you on v2 with v2 categories. Pick one path and use its commands end to end.

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Verification

Prove the report/dashboard actually renders before calling it done. Smallest checks first:

# Allure: a report builds and the trend widget shows >1 run (history preserved)
npx allure generate allure-results --clean -o allure-report && npx allure open allure-report
#   -> Overview page loads; the "Trend" widget shows more than one run.

# Grafana push: the summary point landed in InfluxDB
influx query 'from(bucket:"test-results") |> range(start:-1d) |> filter(fn:(r)=> r._measurement=="test_run_summary")'
#   -> returns at least one row with a pass_rate field for branch=main.

# Grafana alert: the provisioned rule loaded
curl -s http://localhost:3000/api/v1/provisioning/alert-rules -u admin:admin | jq '.[].title'
#   -> includes "Main pass rate dropped >2pp in a day".

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Done When

• Dashboard is published to a known location that returns HTTP 200 for the team (CI artifact URL, Grafana URL, or hosted SaaS link) — no local setup or manual report run required.
• Test execution trends (pass rate, failure count, duration) are visible over at least 2 weeks of historical data.
• Flakiness panel is configured showing the top flaky tests with retry counts over a rolling 14-day window.
• A stakeholder report template is generating automatically — either a weekly summary with a STABLE/NEEDS ATTENTION/CRITICAL health label or a per-release quality report with a READY/NOT READY verdict and per-gate breakdown (one or both is acceptable).
• An alert is configured and routed (Slack or equivalent) that fires when the main-branch pass rate drops by more than 2 percentage points in a single day.

Related Skills

• qa-metrics — Defining quality KPIs, measurement frameworks, and metric interpretation. Go there to decide what to measure; come here to visualize it.
• ci-cd-integration — Pipeline configuration for automated report generation and artifact management.
• ai-bug-triage — AI-powered failure classification that feeds into dashboard failure categories.

Reference Files (in references/)

• allure.md — full Playwright/Vitest adapter configs, per-test metadata, the v2 categories.json, the v3 allurerc.mjs + allure run runnable path, and the CI history-preservation workflow.
• grafana.md — the push-test-metrics.ts script (with flush guard), all panel queries, and the provisioned ">2pp pass-rate drop" alert rule + Slack contact point.