customer-card-render

Gere conteúdo YAML do PowerPoint de customer-card a partir de artefatos canônicos de Design Thinking e construa usando o pipeline compartilhado de habilidades do PowerPoint - Trazido a você…

npx skills add https://github.com/microsoft/hve-core --skill customer-card-render

Customer Card Render Skill

Converts canonical Design Thinking markdown artifacts into PowerPoint skill content.yaml slide definitions and builds the final deck through the shared PowerPoint build pipeline.

Overview

This skill is a sibling to the experimental powerpoint skill. It handles the Design Thinking-specific mapping layer: extracting sections from canonical markdown artifacts and filling template-driven content.yaml files. The PowerPoint skill then owns layout rendering, theming, export, and validation.

Keeping these concerns separate means:

  • Customer-card mapping logic stays independent from general PowerPoint capabilities.
  • The skill can be included in packages independently.
  • Layout primitives, Invoke-PptxPipeline.ps1, theming, and validation behavior are not reimplemented here.

For full PowerPoint pipeline documentation, activate the powerpoint skill by name. When it does not resolve, warn the user that the pipeline documentation and build behavior are unavailable and stop rather than reimplementing them here.

Prerequisites

  • Python 3.11+

  • uv package manager — install with one of:

    # macOS / Linux
    curl -LsSf https://astral.sh/uv/install.sh | sh
    
    # Windows
    powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
    
    # Via pip (fallback)
    pip install uv
    
  • The experimental powerpoint skill, activated by name, for the Invoke-PptxPipeline.ps1 build step. When it does not resolve, warn the user that the build step is unavailable and stop.

Directory Structure

.github/skills/experimental/customer-card-render/
├── SKILL.md
├── pyproject.toml
├── references/
│   └── mapping-spec.md
├── scripts/
│   └── generate_cards.py
├── templates/
│   ├── global-style.yaml
│   ├── persona.content.yaml
│   ├── problem.content.yaml
│   ├── scenario.content.yaml
│   ├── use-case-slide1.content.yaml
│   ├── use-case-slide2.content.yaml
│   ├── use-case-slide3.content.yaml
│   └── vision.content.yaml
└── tests/
    ├── fuzz_harness.py
    └── test_generate_cards.py

Supported Artifact Types

Artifact TypeSlide Layout
Vision StatementSingle slide
Problem StatementSingle slide
ScenarioSingle slide
Use Case4 slides (see below)
PersonaSingle slide

Use Case 4-Slide Layout

Each Use Case expands into 4 consecutive slides with distinct sections:

SlideContent
Slide 1Use Case Description, Use Case Overview, Business Value, Primary User
Slide 2Secondary User, Preconditions, Steps, Data Requirements
Slide 3Equipment Requirements, Operating Environment, Success Criteria, Pain Points
Slide 4Extensions, Evidence

Cards are ordered by artifact type (Vision → Problem → Scenario → Use Case → Persona), then alphabetically by title within each type. Use Cases appear with all 4 slides consecutive (Slide N, N+1, N+2, N+3).

Two-Command Flow

Step 1: Generate slide YAML from canonical markdown

python "<customer-card-render-skill-root>/scripts/generate_cards.py" \
  --canonical-dir .copilot-tracking/dt/<project-slug>/canonical \
  --output-dir .copilot-tracking/dt/<project-slug>/render/content

Resolve <customer-card-render-skill-root> from the loaded skill location before running the command.

generate_cards.py CLI Reference

FlagRequiredDefaultDescription
--canonical-dirNo<skill-root>/canonicalDirectory containing canonical DT markdown files
--output-dirNo<skill-root>/scripts/contentDirectory to write generated content.yaml files
-v, --verboseNo—Enable debug-level logging

The script reads each markdown file in --canonical-dir, detects the artifact type from frontmatter, extracts required sections, and generates content.yaml files. Vision, Problem, Scenario, and Persona artifacts produce one slide each. Use Case artifacts produce 4 consecutive slides per use case.

For the section-to-field mapping contract and Use Case 4-slide layout details, see references/mapping-spec.md.

Step 2: Build PPTX using the PowerPoint skill pipeline

Activate the powerpoint skill by name and hand it the build, supplying these three inputs:

  • Content directory: .copilot-tracking/dt/<project-slug>/render/content
  • Style path: .copilot-tracking/dt/<project-slug>/render/content/global/style.yaml
  • Output path: .copilot-tracking/dt/<project-slug>/render/output/customer-cards.pptx

The powerpoint skill owns the Invoke-PptxPipeline.ps1 orchestrator, its parameter reference, template usage, validation, and export options, and it manages virtual environment setup and dependency installation automatically via uv sync. When that skill is unavailable, warn the user that the build step cannot run and stop rather than invoking the pipeline from a guessed location.

DT Coach Integration

The dt-canonical-deck prompt and the dt-coaching-foundation skill's canonical-deck reference provide opt-in workflow integration for the Design Thinking coaching agent. When a user opts in, the coaching agent offers to build customer cards at method exit points. The two-command flow above runs as part of that workflow with --canonical-dir and --output-dir resolved from the active DT project slug in .copilot-tracking/dt/.

Canonical artifacts are produced by the DT coach and live under .copilot-tracking/dt/<project-slug>/canonical/.

Running Tests

cd "<customer-card-render-skill-root>"
uv sync --group dev
uv run pytest tests/

Tests cover parsing, template selection, YAML emission, and regressions. The tests/fuzz_harness.py file is an Atheris polyglot fuzz harness for OSSF Scorecard compliance.

Content Fidelity Note: Use Case Cards

Use Case cards are split across 3 opinionated slides, each with dedicated sections:

  • Slide 1: Introduces the use case with Description, Overview, Business Value, and Primary User
  • Slide 2: Details execution with Secondary User, Preconditions, Steps, and Data Requirements
  • Slide 3: Captures quality criteria with Equipment Requirements, Operating Environment, Success Criteria, Pain Points, and Evidence

This structure ensures all 16 Use Case sections fit legibly across 4 slides without compression. Each section appears in its own textbox with appropriate styling and heading.

For complete mapping details, see references/mapping-spec.md.

Troubleshooting

IssueCauseSolution
uv not founduv not installedRun curl -LsSf https://astral.sh/uv/install.sh | sh (macOS/Linux) or pip install uv
Python not found by uvNo Python 3.11+ on PATHRun uv python install 3.11
Template not found--canonical-dir contains unknown typeCheck frontmatter type: field against supported artifact types
Empty output directoryNo canonical markdown files foundConfirm --canonical-dir path and that files have --- frontmatter
PPTX build fails after generatePowerPoint skill missing or not activatedActivate the powerpoint skill by name. When its content does not arrive, stop and report the build step as unavailable

Mais skills de microsoft

oss-growth
microsoft
Persona de growth hacker OSS
agent-framework-azure-ai-py
microsoft
Crie agentes do Azure AI Foundry usando o SDK Python do Microsoft Agent Framework (agent-framework-azure-ai). Use ao criar agentes persistentes com AzureAIAgentsProvider, usando ferramentas hospedadas (interpretador de código, pesquisa de arquivos, pesquisa na web), integrando servidores MCP, gerenciando threads de conversa ou implementando respostas em streaming. Abrange ferramentas de função, saídas estruturadas e agentes com múltiplas ferramentas.
development
airunway-aks-setup
microsoft
Configure o AI Runway no AKS — do cluster vazio ao modelo em execução. Abrange verificação do cluster, instalação do controlador, avaliação de GPU, configuração do provedor e primeira implantação. QUANDO: "configurar AI Runway", "integrar cluster AKS", "instalar AI Runway", "configuração do airunway", "implantar modelo no AKS", "inferência GPU no AKS", "configuração KAITO no AKS", "executar LLM no AKS", "vLLM no AKS", "configurar serviço de modelo no AKS", "controlador AI Runway".
devops
appinsights-instrumentation
microsoft
Orientação para instrumentar aplicações web com Azure Application Insights. Fornece padrões de telemetria, configuração de SDK e referências de configuração. QUANDO: como instrumentar o app, SDK do App Insights, padrões de telemetria, o que é App Insights, orientação sobre Application Insights, exemplos de instrumentação, melhores práticas de APM.
devops
applicationinsights-web-ts
microsoft
Instrumente aplicativos de navegador/web com o SDK JavaScript do Application Insights (@microsoft/applicationinsights-web). Use para Real User Monitoring (RUM) — visualizações de página, cliques, dependências AJAX/fetch, exceções, eventos personalizados e rastreamentos de agentes GenAI no lado do navegador correlacionados a rastreamentos OpenTelemetry no backend. Abrange o Script de Carregamento do SDK e a configuração via npm, extensões de frameworks (React, React Native, Angular), Click Analytics, inicializadores de telemetria e convenções semânticas GenAI do OTel para spans de agente/ferramenta/modelo emitidos pelo navegador.
devops
azure-ai-anomalydetector-java
microsoft
Crie aplicativos de detecção de anomalias com o SDK do Azure AI Anomaly Detector para Java. Use ao implementar detecção de anomalias univariada/multivariada, análise de séries temporais ou monitoramento com IA.
development
azure-ai-language-conversations-py
microsoft
Implemente o reconhecimento de linguagem conversacional (CLU) usando o SDK Python azure-ai-language-conversations. Use ao trabalhar com ConversationAnalysisClient para analisar intenção e entidades de conversas, criar recursos de NLP ou integrar o reconhecimento de linguagem em aplicativos.
development
azure-ai-ml-py
microsoft
SDK v2 do Azure Machine Learning para Python. Use para workspaces de ML, jobs, modelos, conjuntos de dados, computação e pipelines. Gatilhos: "azure-ai-ml", "MLClient", "workspace", "registro de modelos", "jobs de treinamento", "conjuntos de dados".
development