awesome-copilot/skills/flowstudio-power-automate-build/references/action-patterns-data.md

FlowStudio MCP — Action Patterns: Data Transforms

Array operations, HTTP calls, parsing, and data transformation patterns.

All examples assume "runAfter" is set appropriately. <connectionName> is the key in connectionReferences (e.g. shared_sharepointonline), not the GUID. The GUID goes in the map value's connectionName property.

---

Array Operations

Select (Reshape / Project an Array)

Transforms each item in an array, keeping only the columns you need or renaming them. Avoids carrying large objects through the rest of the flow.

"Select_Needed_Columns": {
  "type": "Select",
  "runAfter": {},
  "inputs": {
    "from": "@outputs('HTTP_Get_Subscriptions')?['body/data']",
    "select": {
      "id":           "@item()?['id']",
      "status":       "@item()?['status']",
      "trial_end":    "@item()?['trial_end']",
      "cancel_at":    "@item()?['cancel_at']",
      "interval":     "@item()?['plan']?['interval']"
    }
  }
}

Result reference: @body('Select_Needed_Columns') — returns a direct array of reshaped objects.

Use Select before looping or filtering to reduce payload size and simplify downstream expressions. Works on any array — SP results, HTTP responses, variables.

Tips:

• Single-to-array coercion: When an API returns a single object but you need Select (which requires an array), wrap it: @array(body('Get_Employee')?['data']). The output is a 1-element array — access results via ?[0]?['field'].
• Null-normalize optional fields: Use @if(empty(item()?['field']), null, item()?['field']) on every optional field to normalize empty strings, missing properties, and empty objects to explicit null. Ensures consistent downstream @equals(..., @null) checks.
• Flatten nested objects: Project nested properties into flat fields:

  "manager_name": "@if(empty(item()?['manager']?['name']), null, item()?['manager']?['name'])"
  

  This enables direct field-level comparison with a flat schema from another source.

---

Filter Array (Query)

Filters an array to items matching a condition. Use the action form (not the filter() expression) for complex multi-condition logic — it's clearer and easier to maintain.

"Filter_Active_Subscriptions": {
  "type": "Query",
  "runAfter": {},
  "inputs": {
    "from": "@body('Select_Needed_Columns')",
    "where": "@and(or(equals(item().status, 'trialing'), equals(item().status, 'active')), equals(item().cancel_at, null))"
  }
}

Result reference: @body('Filter_Active_Subscriptions') — direct filtered array.

Tip: run multiple Filter Array actions on the same source array to create named buckets (e.g. active, being-canceled, fully-canceled), then use coalesce(first(body('Filter_A')), first(body('Filter_B')), ...) to pick the highest-priority match without any loops.

---

Create CSV Table (Array → CSV String)

Converts an array of objects into a CSV-formatted string — no connector call, no code. Use after a Select or Filter Array to export data or pass it to a file-write action.

"Create_CSV": {
  "type": "Table",
  "runAfter": {},
  "inputs": {
    "from": "@body('Select_Output_Columns')",
    "format": "CSV"
  }
}

Result reference: @body('Create_CSV') — a plain string with header row + data rows.

// Custom column order / renamed headers:
"Create_CSV_Custom": {
  "type": "Table",
  "inputs": {
    "from": "@body('Select_Output_Columns')",
    "format": "CSV",
    "columns": [
      { "header": "Date",        "value": "@item()?['transactionDate']" },
      { "header": "Amount",      "value": "@item()?['amount']" },
      { "header": "Description", "value": "@item()?['description']" }
    ]
  }
}

Without columns, headers are taken from the object property names in the source array. With columns, you control header names and column order explicitly.

The output is a raw string. Write it to a file with CreateFile or UpdateFile (set body to @body('Create_CSV')), or store in a variable with SetVariable.

If source data came from Power BI's ExecuteDatasetQuery, column names will be wrapped in square brackets (e.g. [Amount]). Strip them before writing: @replace(replace(body('Create_CSV'),'[',''),']','')

---

range() + Select for Array Generation

range(0, N) produces an integer sequence [0, 1, 2, …, N-1]. Pipe it through a Select action to generate date series, index grids, or any computed array without a loop:

// Generate 14 consecutive dates starting from a base date
"Generate_Date_Series": {
  "type": "Select",
  "inputs": {
    "from": "@range(0, 14)",
    "select": "@addDays(outputs('Base_Date'), item(), 'yyyy-MM-dd')"
  }
}

Result: @body('Generate_Date_Series') → ["2025-01-06", "2025-01-07", …, "2025-01-19"]

// Flatten a 2D array (rows × cols) into 1D using arithmetic indexing
"Flatten_Grid": {
  "type": "Select",
  "inputs": {
    "from": "@range(0, mul(length(outputs('Rows')), length(outputs('Cols'))))",
    "select": {
      "row": "@outputs('Rows')[div(item(), length(outputs('Cols')))]",
      "col": "@outputs('Cols')[mod(item(), length(outputs('Cols')))]"
    }
  }
}

range() is zero-based. The Cartesian product pattern above uses div(i, cols) for the row index and mod(i, cols) for the column index — equivalent to a nested for-loop flattened into a single pass. Useful for generating time-slot × date grids, shift × location assignments, etc.

---

Dynamic Dictionary via json(concat(join()))

When you need O(1) key→value lookups at runtime and Power Automate has no native dictionary type, build one from an array using Select + join + json:

"Build_Key_Value_Pairs": {
  "type": "Select",
  "inputs": {
    "from": "@body('Get_Lookup_Items')?['value']",
    "select": "@concat('\"', item()?['Key'], '\":\"', item()?['Value'], '\"')"
  }
},
"Assemble_Dictionary": {
  "type": "Compose",
  "inputs": "@json(concat('{', join(body('Build_Key_Value_Pairs'), ','), '}'))"
}

Lookup: @outputs('Assemble_Dictionary')?['myKey']

// Practical example: date → rate-code lookup for business rules
"Build_Holiday_Rates": {
  "type": "Select",
  "inputs": {
    "from": "@body('Get_Holidays')?['value']",
    "select": "@concat('\"', formatDateTime(item()?['Date'], 'yyyy-MM-dd'), '\":\"', item()?['RateCode'], '\"')"
  }
},
"Holiday_Dict": {
  "type": "Compose",
  "inputs": "@json(concat('{', join(body('Build_Holiday_Rates'), ','), '}'))"
}

Then inside a loop: @coalesce(outputs('Holiday_Dict')?[item()?['Date']], 'Standard')

The json(concat('{', join(...), '}')) pattern works for string values. For numeric or boolean values, omit the inner escaped quotes around the value portion. Keys must be unique — duplicate keys silently overwrite earlier ones. This replaces deeply nested if(equals(key,'A'),'X', if(equals(key,'B'),'Y', ...)) chains.

---

union() for Changed-Field Detection

When you need to find records where any of several fields has changed, run one Filter Array per field and union() the results. This avoids a complex multi-condition filter and produces a clean deduplicated set:

"Filter_Name_Changed": {
  "type": "Query",
  "inputs": { "from": "@body('Existing_Records')",
              "where": "@not(equals(item()?['name'], item()?['dest_name']))" }
},
"Filter_Status_Changed": {
  "type": "Query",
  "inputs": { "from": "@body('Existing_Records')",
              "where": "@not(equals(item()?['status'], item()?['dest_status']))" }
},
"All_Changed": {
  "type": "Compose",
  "inputs": "@union(body('Filter_Name_Changed'), body('Filter_Status_Changed'))"
}

Reference: @outputs('All_Changed') — deduplicated array of rows where anything changed.

union() deduplicates by object identity, so a row that changed in both fields appears once. Add more Filter_*_Changed inputs to union() as needed: @union(body('F1'), body('F2'), body('F3'))

---

File-Content Change Gate

Before running expensive processing on a file or blob, compare its current content to a stored baseline. Skip entirely if nothing has changed — makes sync flows idempotent and safe to re-run or schedule aggressively.

"Get_File_From_Source": { ... },
"Get_Stored_Baseline": { ... },
"Condition_File_Changed": {
  "type": "If",
  "expression": {
    "not": {
      "equals": [
        "@base64(body('Get_File_From_Source'))",
        "@body('Get_Stored_Baseline')"
      ]
    }
  },
  "actions": {
    "Update_Baseline": { "...": "overwrite stored copy with new content" },
    "Process_File":    { "...": "all expensive work goes here" }
  },
  "else": { "actions": {} }
}

Store the baseline as a file in SharePoint or blob storage — base64()-encode the live content before comparing so binary and text files are handled uniformly. Write the new baseline before processing so a re-run after a partial failure does not re-process the same file again.

---

Set-Join for Sync (Update Detection without Nested Loops)

When syncing a source collection into a destination (e.g. API response → SharePoint list, CSV → database), avoid nested Apply to each loops to find changed records. Instead, project flat key arrays and use contains() to perform set operations — zero nested loops, and the final loop only touches changed items.

Full insert/update/delete sync pattern:

// Step 1 — Project a flat key array from the DESTINATION (e.g. SharePoint)
"Select_Dest_Keys": {
  "type": "Select",
  "inputs": {
    "from": "@outputs('Get_Dest_Items')?['body/value']",
    "select": "@item()?['Title']"
  }
}
// → ["KEY1", "KEY2", "KEY3", ...]

// Step 2 — INSERT: source rows whose key is NOT in destination
"Filter_To_Insert": {
  "type": "Query",
  "inputs": {
    "from": "@body('Source_Array')",
    "where": "@not(contains(body('Select_Dest_Keys'), item()?['key']))"
  }
}
// → Apply to each Filter_To_Insert → CreateItem

// Step 3 — INNER JOIN: source rows that exist in destination
"Filter_Already_Exists": {
  "type": "Query",
  "inputs": {
    "from": "@body('Source_Array')",
    "where": "@contains(body('Select_Dest_Keys'), item()?['key'])"
  }
}

// Step 4 — UPDATE: one Filter per tracked field, then union them
"Filter_Field1_Changed": {
  "type": "Query",
  "inputs": {
    "from": "@body('Filter_Already_Exists')",
    "where": "@not(equals(item()?['field1'], item()?['dest_field1']))"
  }
}
"Filter_Field2_Changed": {
  "type": "Query",
  "inputs": {
    "from": "@body('Filter_Already_Exists')",
    "where": "@not(equals(item()?['field2'], item()?['dest_field2']))"
  }
}
"Union_Changed": {
  "type": "Compose",
  "inputs": "@union(body('Filter_Field1_Changed'), body('Filter_Field2_Changed'))"
}
// → rows where ANY tracked field differs

// Step 5 — Resolve destination IDs for changed rows (no nested loop)
"Select_Changed_Keys": {
  "type": "Select",
  "inputs": { "from": "@outputs('Union_Changed')", "select": "@item()?['key']" }
}
"Filter_Dest_Items_To_Update": {
  "type": "Query",
  "inputs": {
    "from": "@outputs('Get_Dest_Items')?['body/value']",
    "where": "@contains(body('Select_Changed_Keys'), item()?['Title'])"
  }
}
// Step 6 — Single loop over changed items only
"Apply_to_each_Update": {
  "type": "Foreach",
  "foreach": "@body('Filter_Dest_Items_To_Update')",
  "actions": {
    "Get_Source_Row": {
      "type": "Query",
      "inputs": {
        "from": "@outputs('Union_Changed')",
        "where": "@equals(item()?['key'], items('Apply_to_each_Update')?['Title'])"
      }
    },
    "Update_Item": {
      "...": "...",
      "id": "@items('Apply_to_each_Update')?['ID']",
      "item/field1": "@first(body('Get_Source_Row'))?['field1']"
    }
  }
}

// Step 7 — DELETE: destination keys NOT in source
"Select_Source_Keys": {
  "type": "Select",
  "inputs": { "from": "@body('Source_Array')", "select": "@item()?['key']" }
}
"Filter_To_Delete": {
  "type": "Query",
  "inputs": {
    "from": "@outputs('Get_Dest_Items')?['body/value']",
    "where": "@not(contains(body('Select_Source_Keys'), item()?['Title']))"
  }
}
// → Apply to each Filter_To_Delete → DeleteItem

Why this beats nested loops: the naive approach (for each dest item, scan source) is O(n × m) and hits Power Automate's 100k-action run limit fast on large lists. This pattern is O(n + m): one pass to build key arrays, one pass per filter. The update loop in Step 6 only iterates changed records — often a tiny fraction of the full collection. Run Steps 2/4/7 in parallel Scopes for further speed.

---

First-or-Null Single-Row Lookup

Use first() on the result array to extract one record without a loop. Then null-check the output to guard downstream actions.

"Get_First_Match": {
  "type": "Compose",
  "runAfter": { "Get_SP_Items": ["Succeeded"] },
  "inputs": "@first(outputs('Get_SP_Items')?['body/value'])"
}

In a Condition, test for no-match with the @null literal (not empty()):

"Condition": {
  "type": "If",
  "expression": {
    "not": {
      "equals": [
        "@outputs('Get_First_Match')",
        "@null"
      ]
    }
  }
}

Access fields on the matched row: @outputs('Get_First_Match')?['FieldName']

Use this instead of Apply to each when you only need one matching record. first() on an empty array returns null; empty() is for arrays/strings, not scalars — using it on a first() result causes a runtime error.

---

HTTP & Parsing

HTTP Action (External API)

"Call_External_API": {
  "type": "Http",
  "runAfter": {},
  "inputs": {
    "method": "POST",
    "uri": "https://api.example.com/endpoint",
    "headers": {
      "Content-Type": "application/json",
      "Authorization": "Bearer @{variables('apiToken')}"
    },
    "body": {
      "data": "@outputs('Compose_Payload')"
    },
    "retryPolicy": {
      "type": "Fixed",
      "count": 3,
      "interval": "PT10S"
    }
  }
}

Response reference: @outputs('Call_External_API')?['body']

Variant: ActiveDirectoryOAuth (Service-to-Service)

For calling APIs that require Azure AD client-credentials (e.g., Microsoft Graph), use in-line OAuth instead of a Bearer token variable:

"Call_Graph_API": {
  "type": "Http",
  "runAfter": {},
  "inputs": {
    "method": "GET",
    "uri": "https://graph.microsoft.com/v1.0/users?$search=\"employeeId:@{variables('Code')}\"&$select=id,displayName",
    "headers": {
      "Content-Type": "application/json",
      "ConsistencyLevel": "eventual"
    },
    "authentication": {
      "type": "ActiveDirectoryOAuth",
      "authority": "https://login.microsoftonline.com",
      "tenant": "<tenant-id>",
      "audience": "https://graph.microsoft.com",
      "clientId": "<app-registration-id>",
      "secret": "@parameters('graphClientSecret')"
    }
  }
}

When to use: Calling Microsoft Graph, Azure Resource Manager, or any Azure AD-protected API from a flow without a premium connector.

The authentication block handles the entire OAuth client-credentials flow transparently — no manual token acquisition step needed.

ConsistencyLevel: eventual is required for Graph $search queries. Without it, $search returns 400.

For PATCH/PUT writes, the same authentication block works — just change method and add a body.

⚠️ Never hardcode secret inline. Use @parameters('graphClientSecret') and declare it in the flow's parameters block (type securestring). This prevents the secret from appearing in run history or being readable via get_live_flow. Declare the parameter like:

"parameters": {
  "graphClientSecret": { "type": "securestring", "defaultValue": "" }
}

Then pass the real value via the flow's connections or environment variables — never commit it to source control.

---

HTTP Response (Return to Caller)

Used in HTTP-triggered flows to send a structured reply back to the caller. Must run before the flow times out (default 2 min for synchronous HTTP).

"Response": {
  "type": "Response",
  "runAfter": {},
  "inputs": {
    "statusCode": 200,
    "headers": {
      "Content-Type": "application/json"
    },
    "body": {
      "status": "success",
      "message": "@{outputs('Compose_Result')}"
    }
  }
}

PowerApps / low-code caller pattern: always return statusCode: 200 with a status field in the body ("success" / "error"). PowerApps HTTP actions do not handle non-2xx responses gracefully — the caller should inspect body.status rather than the HTTP status code.

Use multiple Response actions — one per branch — so each path returns an appropriate message. Only one will execute per run.

---

Child Flow Call (Parent→Child via HTTP POST)

Power Automate supports parent→child orchestration by calling a child flow's HTTP trigger URL directly. The parent sends an HTTP POST and blocks until the child returns a Response action. The child flow uses a manual (Request) trigger.

// PARENT — call child flow and wait for its response
"Call_Child_Flow": {
  "type": "Http",
  "inputs": {
    "method": "POST",
    "uri": "https://prod-XX.australiasoutheast.logic.azure.com:443/workflows/<workflowId>/triggers/manual/paths/invoke?api-version=2016-06-01&sp=%2Ftriggers%2Fmanual%2Frun&sv=1.0&sig=<SAS>",
    "headers": { "Content-Type": "application/json" },
    "body": {
      "ID": "@triggerBody()?['ID']",
      "WeekEnd": "@triggerBody()?['WeekEnd']",
      "Payload": "@variables('dataArray')"
    },
    "retryPolicy": { "type": "none" }
  },
  "operationOptions": "DisableAsyncPattern",
  "runtimeConfiguration": {
    "contentTransfer": { "transferMode": "Chunked" }
  },
  "limit": { "timeout": "PT2H" }
}

// CHILD — manual trigger receives the JSON body
// (trigger definition)
"manual": {
  "type": "Request",
  "kind": "Http",
  "inputs": {
    "schema": {
      "type": "object",
      "properties": {
        "ID": { "type": "string" },
        "WeekEnd": { "type": "string" },
        "Payload": { "type": "array" }
      }
    }
  }
}

// CHILD — return result to parent
"Response_Success": {
  "type": "Response",
  "inputs": {
    "statusCode": 200,
    "headers": { "Content-Type": "application/json" },
    "body": { "Result": "Success", "Count": "@length(variables('processed'))" }
  }
}

retryPolicy: none — critical on the parent's HTTP call. Without it, a child flow timeout triggers retries, spawning duplicate child runs.

DisableAsyncPattern — prevents the parent from treating a 202 Accepted as completion. The parent will block until the child sends its Response.

transferMode: Chunked — enable when passing large arrays (>100 KB) to the child; avoids request-size limits.

limit.timeout: PT2H — raise the default 2-minute HTTP timeout for long-running children. Max is PT24H.

The child flow's trigger URL contains a SAS token (sig=...) that authenticates the call. Copy it from the child flow's trigger properties panel. The URL changes if the trigger is deleted and re-created.

---

Parse JSON

"Parse_Response": {
  "type": "ParseJson",
  "runAfter": {},
  "inputs": {
    "content": "@outputs('Call_External_API')?['body']",
    "schema": {
      "type": "object",
      "properties": {
        "id": { "type": "integer" },
        "name": { "type": "string" },
        "items": {
          "type": "array",
          "items": { "type": "object" }
        }
      }
    }
  }
}

Access parsed values: @body('Parse_Response')?['name']

---

Manual CSV → JSON (No Premium Action)

Parse a raw CSV string into an array of objects using only built-in expressions. Avoids the premium "Parse CSV" connector action.

"Delimiter": {
  "type": "Compose",
  "inputs": ","
},
"Strip_Quotes": {
  "type": "Compose",
  "inputs": "@replace(body('Get_File_Content'), '\"', '')"
},
"Detect_Line_Ending": {
  "type": "Compose",
  "inputs": "@if(equals(indexOf(outputs('Strip_Quotes'), decodeUriComponent('%0D%0A')), -1), if(equals(indexOf(outputs('Strip_Quotes'), decodeUriComponent('%0A')), -1), decodeUriComponent('%0D'), decodeUriComponent('%0A')), decodeUriComponent('%0D%0A'))"
},
"Headers": {
  "type": "Compose",
  "inputs": "@split(first(split(outputs('Strip_Quotes'), outputs('Detect_Line_Ending'))), outputs('Delimiter'))"
},
"Data_Rows": {
  "type": "Compose",
  "inputs": "@skip(split(outputs('Strip_Quotes'), outputs('Detect_Line_Ending')), 1)"
},
"Select_CSV_Body": {
  "type": "Select",
  "inputs": {
    "from": "@outputs('Data_Rows')",
    "select": {
      "@{outputs('Headers')[0]}": "@split(item(), outputs('Delimiter'))[0]",
      "@{outputs('Headers')[1]}": "@split(item(), outputs('Delimiter'))[1]",
      "@{outputs('Headers')[2]}": "@split(item(), outputs('Delimiter'))[2]"
    }
  }
},
"Filter_Empty_Rows": {
  "type": "Query",
  "inputs": {
    "from": "@body('Select_CSV_Body')",
    "where": "@not(equals(item()?[outputs('Headers')[0]], null))"
  }
}

Result: @body('Filter_Empty_Rows') — array of objects with header names as keys.

Detect_Line_Ending handles CRLF (Windows), LF (Unix), and CR (old Mac) automatically using indexOf() with decodeUriComponent('%0D%0A' / '%0A' / '%0D').

Dynamic key names in Select: @{outputs('Headers')[0]} as a JSON key in a Select shape sets the output property name at runtime from the header row — this works as long as the expression is in @{...} interpolation syntax.

Columns with embedded commas: if field values can contain the delimiter, use length(split(row, ',')) in a Switch to detect the column count and manually reassemble the split fragments: @concat(split(item(),',')[1],',',split(item(),',')[2])

---

ConvertTimeZone (Built-in, No Connector)

Converts a timestamp between timezones with no API call or connector licence cost. Format string "g" produces short locale date+time (M/d/yyyy h:mm tt).

"Convert_to_Local_Time": {
  "type": "Expression",
  "kind": "ConvertTimeZone",
  "runAfter": {},
  "inputs": {
    "baseTime": "@{outputs('UTC_Timestamp')}",
    "sourceTimeZone": "UTC",
    "destinationTimeZone": "Taipei Standard Time",
    "formatString": "g"
  }
}

Result reference: @body('Convert_to_Local_Time') — not outputs(), unlike most actions.

Common formatString values: "g" (short), "f" (full), "yyyy-MM-dd", "HH:mm"

Common timezone strings: "UTC", "AUS Eastern Standard Time", "Taipei Standard Time", "Singapore Standard Time", "GMT Standard Time"

This is type: Expression, kind: ConvertTimeZone — a built-in Logic Apps action, not a connector. No connection reference needed. Reference the output via body() (not outputs()), otherwise the expression returns null.