preview-screens

Úsalo cuando el usuario quiera previsualizar pantallas generadas en un navegador sin iniciar Metro / un simulador — por ejemplo, después de que /create-mobile-app termine o después de…

npx skills add https://github.com/microsoft/power-platform-skills --skill preview-screens

Shared instructions: shared-instructions.md — read first.

Preview Screens

Generates a self-contained HTML file that renders every screen in the app as a phone-frame mockup (375 × 812) with tab navigation and a dark/light toggle. The agent reads TSX files, understands the Tamagui component tree, and produces equivalent HTML/CSS — no programmatic TSX parsing.

When to use

  • After generating screens, to see a quick visual preview without running Metro/Expo
  • To share a screenshot-ready mockup with stakeholders
  • To verify layout before deploying

When NOT to use

  • To run the actual app → use npm run dev
  • To modify screens → use /edit-app; screen-builder is an internal agent invoked by orchestrator skills

Workflow

  1. Locate project → 2. Discover screens → 3. Read reference mapping → 4. Read & convert each screen → 5. Assemble preview.html → 6. Write file → 7. Open in browser

Step 1 — Locate project

Determine the working directory:

  • If $ARGUMENTS contains --working-dir <path>, use that.
  • Otherwise use the current working directory.

Validate the project:

Glob pattern="power.config.json" path="<working_dir>"

If missing, check for package.json. If neither exists, report the error and stop.

Read memory-bank.md if present to get the project name for the page title:

Grep pattern="^# " path="<working_dir>/memory-bank.md"

Fallback: read name from package.json.

Step 2 — Discover screens

Telemetry checkpoint: discover_app_screens

Find all TSX files under the app directory:

Glob pattern="app/**/*.tsx" path="<working_dir>"

Exclude these patterns — they are not screens:

  • _layout.tsx (navigation layouts)
  • +not-found.tsx (Expo Router error boundary)
  • Files in directories starting with .
  • index.tsx at the app root if it only contains an auth redirect (read it to check)

Derive screen names from file paths:

  • app/(app)/home.tsx → "Home"
  • app/(app)/recipes/index.tsx → "Recipes"
  • app/(app)/recipes/[id].tsx → "Recipe Detail"
  • app/login.tsx → "Login"
  • app/oauth-callback.tsx → skip (not a visible screen)

If native-app-plan.md exists in the working directory, read its ## Screens section for human-friendly labels.

Build an ordered list: [ { path, screenName, screenId } ].

Default tab ordering — Home first, then two details, then the rest. Step 5 marks the first entry as active, so the order below directly controls which screen the user lands on when preview.html opens.

Sort the list with this priority:

  1. Home / dashboard first. The first screen matching any of these paths (in this priority): app/(app)/home.tsx, app/(app)/index.tsx, app/(app)/dashboard.tsx, app/index.tsx (only if it's a real home screen — not the auth redirect you already filtered out in Step 2). If native-app-plan.md flags one screen as the home/landing screen, prefer that.
  2. Then up to two detail screens. A "detail" screen is any TSX whose route segment uses a dynamic param — file path contains [ and ] (e.g. app/(app)/recipes/[id].tsx, app/(app)/orders/[orderId]/edit.tsx). Take the first two in the order they were discovered (alphabetical by path is fine).
  3. Then everything else in discovery order.

If there are fewer than two detail screens, just include whatever exists and continue with the rest — do not pad with non-detail screens to force a count of 3.

Do not drop any screens — this rule only reorders. Every discovered screen still gets a tab.

Step 3 — Read reference mapping

Load the Tamagui-to-HTML mapping reference:

Read file_path="${PLUGIN_ROOT}/shared/references/tamagui-html-mapping.md"

Internalize:

  • Component → HTML element + CSS mappings (Section 1)
  • Token → pixel values for spacing, font-size, color (Section 2)
  • Conversion guidelines — placeholder rules, icon substitutions, what to skip (Section 3)
  • Phone frame HTML template (Section 4) — this is the outer shell

Also check if the project has custom brand tokens:

Glob pattern="tamagui.config.ts" path="<working_dir>"

If found, read it and extract any custom color tokens (look for tokens: { color: { ... } }). Add them as additional CSS custom properties in the generated HTML.

Step 4 — Read and convert each screen

Telemetry checkpoint: render_screen_preview_frames

Print before starting:

"→ Reading + converting screens to HTML/CSS (one print per screen as I go)."

For each screen in the ordered list from Step 2:

  1. Read the full TSX file.

  2. Identify the component tree. Walk the JSX return statement and note every Tamagui component, its props, and its children.

  3. Generate equivalent HTML/CSS using the mapping from Step 3:

    • YStack → <div style="display:flex; flex-direction:column; ...">
    • Map every shorthand prop to its CSS equivalent (flex={1} → flex:1, bg="$color2" → background:var(--color2), etc.)
    • Map token values to pixels (p="$4" → padding:16px)
    • Replace <Ionicons name="..." /> icons with Unicode equivalents (see mapping reference Section 3, Guideline 4 — the icon substitution table uses Ionicons names)
  4. Handle dynamic content:

    • .map() over arrays → generate 3–4 representative placeholder items
    • useQuery / useMutation → show the populated state only (skip loading/error branches)
    • Form defaultValues → pre-fill inputs with those values

    Native PDF/pen controls need honest static approximations:

    • PDF viewer actions → render a compact report/PDF block with a filename, generated timestamp, storage label (for example Stored in Evidence PDF File or On-device share only), and a disabled View PDF button. If the source URL is not visibly HTTPS, label it Preview unavailable in browser rather than showing a fake viewer.
    • Generated PDF reports → render the generated/ready state and any persistence label from the plan or code, such as Uploads to Evidence PDF File. Do not embed a browser PDF iframe or imply the native viewer runs in preview.
    • Pen/signature input → render a signature pad placeholder with an ink stroke sample and a captured-preview state. Include the persistence label when known, such as Stored in Signature Image or Uploads to Signature File.
    • Do not wire browser click handlers that pretend to capture pen input, open native PDF viewer, share, print, or upload. This preview is visual only.
  5. Produce a <div class="screen" id="screen-{screenId}"> wrapping the converted HTML.

Use inline styles on elements. Keep each screen's HTML self-contained (no shared CSS classes between screens, except the theme variables).

Step 5 — Assemble preview.html

Use the phone frame template from the mapping reference (Section 4) as the outer shell.

Replace the placeholders:

  • {{APP_NAME}} — project name from Step 1
  • {{TABS}} — one <button class="tab" ...> per screen, first tab gets class active
  • {{SCREENS}} — all screen <div> blocks from Step 4, first screen gets class active

If the project has custom brand tokens (from Step 3), add them to the :root CSS block.

Step 6 — Write the file

Telemetry checkpoint: write_screen_preview_document

Write file_path="<working_dir>/preview.html"

Print confirmation:

✅ Preview generated: <working_dir>/preview.html
   Screens: <N> (<comma-separated list of screen names>)
   Toggle: dark/light mode button in top-right

Step 7 — Open in browser

Telemetry checkpoint: open_screen_preview

Do NOT prompt. The visual_companion flag in <working_dir>/memory-bank.md already encodes the answer; asking again is redundant. The flag is set by /design-system (Step 6.75) during project creation, or defaults to yes if /design-system was not run.

Read the flag and act:

grep -E "^visual_companion:[[:space:]]*(yes|no)" "<working_dir>/memory-bank.md" 2>/dev/null
FlagAction
visual_companion: noPrint the link and stop. Do not auto-open.
visual_companion: yes (or missing memory-bank, or standalone invocation)Print the link, then auto-open.

visual_companion: no — print:

"Preview is at: file://<working_dir>/preview.html (Visual Companion off — open manually.)"

Otherwise — print the link AND auto-open in one breath, no prompt:

"Preview is at: file://<working_dir>/preview.html — opening now."

Then try OS-appropriate openers in sequence and fall back to printing the link if none work:

open "<working_dir>/preview.html" 2>/dev/null \
  || xdg-open "<working_dir>/preview.html" 2>/dev/null \
  || powershell.exe -NoProfile -Command "Start-Process '<working_dir>\preview.html'" 2>/dev/null \
  || echo "Could not auto-open. Open this URL in your browser: file://<working_dir>/preview.html"

open is macOS-only; the chain covers Linux (xdg-open) and Windows / WSL (powershell.exe Start-Process). On headless / SSH sessions all three fail silently and the user just opens the link they were already given.


Notes

  • Read-only with respect to source code. This skill only creates/overwrites preview.html — it never modifies TSX files, layouts, configs, or the memory bank.
  • Static approximation. The preview does not execute React, handle state, or fetch data. Dynamic lists show placeholder items. Interactions (button taps, navigation) are not functional.
  • Native capabilities are placeholders. PDF viewer, PDF report, sharing, printing, and pen/signature capture are shown as static states only. Browser preview must not imply native capture/viewer APIs work there.
  • Re-running /preview-screens overwrites the previous preview.html.
  • No memory-bank update needed — previews are ephemeral artifacts.

References

Más skills de microsoft

oss-growth
microsoft
Persona de growth hacker de OSS
agent-framework-azure-ai-py
microsoft
Crea agentes de Azure AI Foundry usando el SDK de Python de Microsoft Agent Framework (agent-framework-azure-ai). Úsalo al crear agentes persistentes con AzureAIAgentsProvider, usando herramientas alojadas (intérprete de código, búsqueda de archivos, búsqueda web), integrando servidores MCP, gestionando hilos de conversación o implementando respuestas en streaming. Cubre herramientas de función, salidas estructuradas y agentes con múltiples herramientas.
development
airunway-aks-setup
microsoft
Configura AI Runway en AKS: desde un clúster vacío hasta un modelo en ejecución. Incluye verificación del clúster, instalación del controlador, evaluación de GPU, configuración del proveedor y primer despliegue. CUÁNDO: "configurar AI Runway", "incorporar clúster AKS", "instalar AI Runway", "configuración de airunway", "desplegar modelo en AKS", "inferencia GPU en AKS", "configuración de KAITO en AKS", "ejecutar LLM en AKS", "vLLM en AKS", "configurar servicio de modelos en AKS", "controlador de AI Runway".
devops
appinsights-instrumentation
microsoft
Guía para instrumentar aplicaciones web con Azure Application Insights. Proporciona patrones de telemetría, configuración del SDK y referencias de configuración. CUÁNDO: cómo instrumentar una aplicación, SDK de App Insights, patrones de telemetría, qué es App Insights, guía de Application Insights, ejemplos de instrumentación, mejores prácticas de APM.
devops
applicationinsights-web-ts
microsoft
Instrumenta aplicaciones web/navegador con el SDK de JavaScript de Application Insights (@microsoft/applicationinsights-web). Úsalo para monitoreo de usuarios reales (RUM): vistas de página, clics, dependencias AJAX/fetch, excepciones, eventos personalizados y trazas de agentes GenAI del lado del navegador correlacionadas con trazas de OpenTelemetry del backend. Cubre el script de carga del SDK y la configuración npm, extensiones de frameworks (React, React Native, Angular), Click Analytics, inicializadores de telemetría y convenciones semánticas de GenAI de OTel para spans de agentes/herramientas/modelos emitidos desde el navegador.
devops
azure-ai-anomalydetector-java
microsoft
Cree aplicaciones de detección de anomalías con el SDK de Azure AI Anomaly Detector para Java. Úselo al implementar detección de anomalías univariadas/multivariadas, análisis de series temporales o monitoreo impulsado por IA.
development
azure-ai-language-conversations-py
microsoft
Implementa el reconocimiento del lenguaje conversacional (CLU) utilizando el SDK de Python azure-ai-language-conversations. Úsalo al trabajar con ConversationAnalysisClient para analizar la intención y las entidades de la conversación, crear funciones de NLP o integrar el reconocimiento del lenguaje en aplicaciones.
development
azure-ai-ml-py
microsoft
SDK v2 de Azure Machine Learning para Python. Úselo para áreas de trabajo de ML, trabajos, modelos, conjuntos de datos, cómputo y canalizaciones. Disparadores: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
development