migrate-to-dataverse

作者: microsoft

讀取現有Canvas App的YAML檔案,並將Power FX資料來源呼叫替換為等效的Dataverse資料表呼叫。當使用者想要遷移時使用…

npx skills add https://github.com/microsoft/power-cat-skills --skill migrate-to-dataverse

Migrate Canvas App Data Sources to Dataverse

Read the YAML files of the current Canvas App and replace all Power FX data source calls with Dataverse equivalents for the following requirements:

$ARGUMENTS

CRITICAL: Review Guidance First

Before making any changes, you MUST read and internalize the technical reference document:

  • ${CLAUDE_PLUGIN_ROOT}/references/TechnicalGuide.md — Technical best practices, control selection, validation workflow, formulas, layout strategies

Read this file before planning any edits.

CRITICAL: Sync the Canvas App First

Before reading or editing any YAML files, call the sync_canvas MCP tool to ensure a local copy of the canvas app YAML is present and up to date. This pulls the current app state from the coauthoring session into local .pa.yaml files.

Only proceed after sync_canvas completes successfully.

Migration Workflow

1. Discover Available Data Sources

Call list_data_sources to enumerate all data sources connected to the current authoring session. This is the authoritative list of Dataverse tables (and other connectors) available for mapping.

After the call completes, share a discovery summary with the user:

Discovery complete. Available data sources ([N] total):

NameTypeKey Columns
[Table Name]Dataverse / SharePoint / …[column names]

For each Dataverse table identified, call get_data_source_schema to retrieve the full column list and Power Fx types. This information is required to map source columns to destination columns accurately.

2. Read the YAML Files

Read every .pa.yaml file produced by sync_canvas. For each file:

  • Identify every Power Fx formula that references a non-Dataverse data source (e.g. SharePoint.GetItems, Filter('MyList', …), Patch('MyList', …), LookUp, Collect, ClearCollect, etc.).
  • Note the source table/list name, the columns referenced, and the operation type (Filter, Patch, LookUp, Collect, etc.).

3. Build a Column Mapping Plan

Using the schemas retrieved in step 1, produce a mapping table for every data source call found:

Proposed Mapping Plan

Source ExpressionSource ColumnDataverse TableDataverse ColumnNotes
Filter('Orders List', Status = "Open")Statuscr123_orderscr123_statusType match: Text
……………

Rules for column selection:

  • Prefer an exact name match (case-insensitive).
  • Fall back to a semantic name match (e.g. Title → cr123_name).
  • Flag any column with no clear match as ⚠ needs manual review.
  • Respect Power Fx type compatibility — do not map a Text column to a Choices column without an explicit conversion formula.

Present the plan to the user and ask for approval before making any changes:

Does this mapping plan look correct?

  • Approve and apply changes
  • I'd like to adjust the mapping first

If adjustments are requested, update the mapping plan accordingly and re-present.

4. Apply the Replacements

Once the plan is approved, update every affected .pa.yaml file:

  • Replace each source data-call expression with the equivalent Dataverse Power Fx expression using the approved column mapping.
  • Keep all UI properties, layout, and non-data formulas unchanged.
  • Follow the formula conventions in ${CLAUDE_PLUGIN_ROOT}/references/TechnicalGuide.md (use = prefix, wrap multi-line formulas correctly, etc.).
  • Preserve OnVisible initialization patterns — replace collection sources but keep the collection/variable structure if it exists.

Announce progress for each file:

Updating [filename].pa.yaml ([N] of [Total])…

5. Validate

Call compile_canvas after updating all files. Fix any compilation errors before finishing. Report the result:

  • On success: > Compilation successful — all replacements are valid.
  • On failure: > [N] error(s) found — fixing before finishing. [brief description of each error]

Repeat validate → fix until all files compile clean.

6. Complete

When all files pass validation, present a final summary:

Migration complete.

FileExpressions ReplacedStatus
[filename].pa.yaml[N]Compiled

Source replaced: [original data source names] Target: [Dataverse table names used] Columns requiring manual review: [list any ⚠ flagged columns, or "none"]

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