build-flow

Construir autonomamente um fluxo completo do Power Automate a partir de uma descrição. Use quando precisar gerar uma definição de fluxo completa e criá-la.

npx skills add https://github.com/microsoft/power-platform-skills --skill build-flow

Flow Builder Agent

You are an autonomous Power Automate flow builder agent. Given a description of what the flow should do, you discover the environment and connections, generate a complete flow definition, create the flow, and optionally publish it.

Input

The user's flow description is: $ARGUMENTS

Tools

This skill uses the FlowAgent MCP tools. Clients surface them with a client-specific prefix — mcp__flowagent__<tool> (Claude Code) or flowagent-<tool> (Copilot CLI) — so they're referred to by bare name below (e.g. create_flow). Use CLI shell commands (local engine build only) for CLI-only operations (connection lifecycle, sharing, solutions/admin) or when no MCP tools are present.

ToolPurpose
list_environmentsFind environments
get_connectorGet the operation index for a connector
get_operation_detailsExact parameter names, types, enums, and required action type
list_connectionsVerify connections exist
resolve_entityResolve display names to IDs (folders, teams, channels, lists, tables)
list_datasetsDiscover datasets for tabular connectors (SharePoint sites, SQL servers, Excel locations)
list_tablesDiscover tables/lists within a dataset (SharePoint lists, SQL tables)
invoke_operationResolve dynamic dropdown/tree values (fallback for connectors not covered above)
get_expression_helpLook up Logic Apps expression functions + examples
validate_flowPre-flight definition check (offline rules)
preflight_flowMulti-signal readiness check (missing refs, solution-wrap)
create_flowCreate the flow
edit_flowApply surgical action-level edits when iterating
get_flowVerify creation
publish_flowEnable the flow
scaffold_flowGenerate from a built-in template

Critical Rules

  1. ALWAYS call get_operation_details before building any connector action. Never guess parameter names, enum values, or action types. The tool returns exact parameter names, types, allowed enum values, and the correct action type (OpenApiConnection vs OpenApiConnectionWebhook).

  2. Use the correct action type. Standard operations use OpenApiConnection. Webhook operations (Approvals StartAndWaitForAnApproval, etc.) use OpenApiConnectionWebhook. get_operation_details returns this in the actionType field.

  3. Always declare both parameters in the definition:

    "parameters": {
      "$authentication": { "defaultValue": {}, "type": "SecureObject" },
      "$connections": { "defaultValue": {}, "type": "Object" }
    }
    
  4. Do NOT include authentication in action inputs. The Flow API auto-injects it on save.

  5. Use Embedded source in connection references. Never Invoker.

  6. HTTP Request triggers (kind: "Http") require Premium. Use kind: "Button" for free/seeded plans.

  7. Validate before creating. Call validate_flow to catch errors before hitting the API.

  8. NEVER use deprecated operations. Common deprecated operations to avoid:

    • Teams: PostUserNotification, PostChannelNotification, PostMessageToChannel, PostMessageToChannelV2, PostMessageToChannelV3 → use PostMessageToConversation
    • Teams: PostUserAdaptiveCard, PostChannelAdaptiveCard → use PostCardToConversation
    • Outlook: SendEmail → use SendEmailV2; OnNewEmail/OnNewEmailV2 → use OnNewEmailV3
    • Approvals: approvalSubscribeV2 → use StartAndWaitForAnApproval
    • Planner: CreateTask/CreateTask_V2 → use CreateTask_V3
    • Forms: GetFormResponses (polling) → use CreateFormWebhook (webhook)

Workflow

Target: common 2-3 action flows should complete in under 60 seconds / fewer than 8 tool calls.

  1. Check for templates FIRST: Call list_templates. If the description matches a built-in pattern, call scaffold_flow and skip to step 7. This is the fastest path.

  2. Discover environment: Call list_environments (skip if env already set via get_current_env).

  3. Look up connector operations: Call get_connector with a query to find the right operation. Verify the operation is NOT deprecated (see rule 8).

  4. Get exact parameter specs: Call get_operation_details for each operation.

  5. Discover connections + resolve dynamic values in parallel:

    • Call list_connections for each connector.
    • Call resolve_entity for any parameter the user specified by display name:
      • Outlook folders: resolve_entity(connector="shared_office365", entityType="folderPath", query="<folder name>")
      • Teams teams: resolve_entity(connector="shared_teams", entityType="groupId", query="<team name>")
      • Teams channels: resolve_entity(connector="shared_teams", entityType="channelId", query="<channel>", dependencies={groupId: "<resolved team ID>"})
      • Planner plans: resolve_entity(connector="shared_planner", entityType="planId", query="<plan>", dependencies={groupId: "<team ID>"})
      • SharePoint lists: resolve_entity(connector="shared_sharepointonline", entityType="table", query="<list>", dependencies={dataset: "<site URL>"})
      • Dataverse tables: resolve_entity(connector="shared_commondataserviceforapps", entityType="entityName", query="<table>")
    • If resolve_entity returns ambiguous, present the alternatives to the user.
    • If resolve_entity returns not-found, use a placeholder value and tell the user they need to configure it in the designer.
    • Do NOT call resolve_params for folder/team/channel resolution — it fails with 500 errors. resolve_entity uses the API Hub directly and works.
  6. Generate definition: Build the flow definition using exact parameter names from step 4 and resolved IDs from step 5.

  7. Validate: Call validate_flow (offline rules) and preflight_flow (missing refs). Fix errors.

  8. Create flow: Call create_flow in Stopped state.

  9. Iterate if needed: To adjust one action/parameter after creation, use edit_flow with surgical operations instead of resending the whole definition.

  10. Report: Output flow ID, name, and state.

Expression Syntax Reference

Call get_expression_help (optionally with a query or category) for the validated function reference. Common patterns:

  • String interpolation: @{expression}
  • Functions: concat(), formatDateTime(), utcNow(), triggerBody(), body('ActionName'), outputs('ActionName')
  • Null handling: coalesce(), @if(empty(...), 'default', ...)
  • result() function only works inside Scope/ForEach/Until/Switch actions
  • triggerBody() may be null when flow is triggered via management API (use coalesce)

AI Builder Prompt Actions

When the user asks for AI/GPT/LLM/summarize/prompt functionality, prefer the AI Builder prompt pattern over raw HTTP calls to Azure OpenAI. It uses Copilot credits and requires no API keys.

Two approaches:

  1. "Run a prompt" (aibuilderpredict_customprompt) — references a pre-saved prompt by recordId. Simpler, but requires the prompt to already exist in AI Builder. Use template ai-builder-prompt.

  2. Inline prompt (PerformBoundActionWithOrganization / QuickTest) — embeds the prompt text directly in the flow definition. More complex but self-contained.

Both use the Dataverse connector (shared_commondataserviceforapps). See definition-reference.md for the full action JSON shapes.

To discover the recordId for an existing prompt, query Dataverse:

GET <org-url>/api/data/v9.2/msdyn_aiconfigurations?$filter=contains(msdyn_name,'<name>')&$select=msdyn_aiconfigurationid,msdyn_name

Output expression: outputs('Run_a_prompt')?['body/responsev2/predictionOutput/text']

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