customer-card-render

作者: microsoft

從設計思考的典型產出生成客戶卡片 PowerPoint 內容 YAML,並透過共用的 PowerPoint 技能管道進行建置——由…提供

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

來自 microsoft 的更多技能

oss-growth
microsoft
開源增長駭客角色
agent-framework-azure-ai-py
microsoft
使用Microsoft Agent Framework Python SDK(agent-framework-azure-ai)构建Azure AI Foundry代理。适用于使用AzureAIAgentsProvider创建持久化代理、使用托管工具(代码解释器、文件搜索、网络搜索)、集成MCP服务器、管理对话线程或实现流式响应。涵盖函数工具、结构化输出和多工具代理。
development
airunway-aks-setup
microsoft
在AKS上設定AI Runway——從裸叢集到執行模型。涵蓋叢集驗證、控制器安裝、GPU評估、供應商設定及首次部署。時機:「設定AI Runway」、「上線AKS叢集」、「安裝AI Runway」、「airunway設定」、「部署模型至AKS」、「在AKS上進行GPU推論」、「在AKS上設定KAITO」、「在AKS上執行LLM」、「在AKS上使用vLLM」、「在AKS上設定模型服務」、「AI Runway控制器」。
devops
appinsights-instrumentation
microsoft
使用Azure Application Insights檢測Web應用程式的指南。提供遙測模式、SDK設定與組態參考。適用時機:如何檢測應用程式、App Insights SDK、遙測模式、什麼是App Insights、Application Insights指南、檢測範例、APM最佳實踐。
devops
applicationinsights-web-ts
microsoft
使用Application Insights JavaScript SDK(@microsoft/applicationinsights-web)為瀏覽器/Web應用程式進行檢測。適用於真實使用者監控(RUM)——頁面檢視、點擊、AJAX/fetch依賴、例外、自訂事件,以及與後端OpenTelemetry追蹤關聯的瀏覽器端GenAI代理追蹤。涵蓋SDK載入器指令碼與npm設定、框架擴充(React、React Native、Angular)、點擊分析、遙測初始化器,以及從瀏覽器發出的代理/工具/模型span的OTel GenAI語意慣例。
devops
azure-ai-anomalydetector-java
microsoft
使用適用於 Java 的 Azure AI 異常偵測器 SDK 建置異常偵測應用程式。在實作單變量/多變量異常偵測、時間序列分析或 AI 驅動監控時使用。
development
azure-ai-language-conversations-py
microsoft
使用 azure-ai-language-conversations Python SDK 實作對話語言理解(CLU)。當使用 ConversationAnalysisClient 分析對話意圖與實體、建置 NLP 功能,或將語言理解整合至應用程式時使用。
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python。用於機器學習工作區、作業、模型、資料集、計算資源與管線。 觸發詞:「azure-ai-ml」、「MLClient」、「workspace」、「model registry」、「training jobs」、「datasets」。
development