mirror of
https://github.com/github/awesome-copilot.git
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Update FlowStudio Power Automate skills (#1664)
* feat(flowstudio): align Power Automate skills with MCP server v1.1.6 Foundation skill (flowstudio-power-automate-mcp) rewritten to use the server's new tool_search and list_skills meta-tools (v1.1.5+) for discovery instead of cataloging every tool by hand. Cut from 519 to 295 lines. New "Which Skill to Use When" intent-keyed decision tree points at the four specialized skills. Build/debug/governance/monitoring updated for use-case framing. Tools that genuinely cross tiers (e.g. debug skill borrowing get_store_flow_summary) are correct when the workflow needs them — the split between skills is by use-case intent, not by tool partition. Build skill: new Step 3a Resolving Dynamic Connector Values covers get_live_dynamic_options outer-parameter auto-bridge (v1.1.6+) and the AadGraph user-picker fallback via shared_office365users.SearchUserV2 (replaces broken builtInOperation:AadGraph.GetUsers). Debug skill: Outlook user-picker failure note pointing at the fallback. Monitoring skill description disambiguates from the server's monitor-flow tool bundle (runtime control of a single flow) — this skill is tenant-wide health analytics over the cached store. All 5 skills validate via npm run skill:validate; line endings LF only; codespell clean; auto-regenerated docs/README.skills.md included. * fix(flowstudio): remove deprecated tool references The v1.1.5 MCP server release marked 5 tools [DEPRECATED] but the previous alignment commit missed them. Replacements per server source: - get_live_flow_http_schema → read trigger.inputs.schema from get_live_flow - get_live_flow_trigger_url → read trigger.metadata.callbackUrl from get_live_flow - get_store_flow_trigger_url → get_store_flow.triggerUrl field - get_store_flow_errors → get_store_flow_runs(status=["Failed"]) - set_store_flow_state → set_live_flow_state Touches build, debug, governance, monitoring SKILL.md and the foundation skill's tool-reference.md. Remaining mentions of the deprecated names are intentional — they live in deprecation notices naming the obsolete wrapper alongside its replacement. * Update FlowStudio Power Automate skills * Cover latest FlowStudio MCP actions * Trim FlowStudio Power Automate skills * Number FlowStudio build workflow steps
This commit is contained in:
Catherine Han
GitHub
@@ -132,6 +132,10 @@ Result reference: `@body('Get_SP_Item')?['FieldName']`
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}
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```
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> `PatchItem` can validate required SharePoint columns even when you are not
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> changing those fields. Echo unchanged required fields from the trigger or a
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> prior Get Item action, for example `item/Title`, and use internal field names.
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---
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### SharePoint — File Upsert (Create or Overwrite in Document Library)
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@@ -286,6 +290,10 @@ SharePoint REST API via the `HttpRequest` operation:
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> The `HttpRequest` operation reuses the existing SharePoint connection — no extra
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> authentication needed. Use this when the standard Update Item connector can't
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> reach the target list (different site collection, or you need raw REST control).
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> Keep the connector-specific parameter names exactly as shown:
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> `parameters/method`, `parameters/uri`, `parameters/headers`, and
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> `parameters/body`. The body is a JSON string, and `parameters/uri` is relative
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> to the SharePoint `dataset`.
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---
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@@ -340,6 +348,22 @@ the file; the flow downloads and filters it for before/after comparisons.
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---
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## Excel Online
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### Excel — Run Office Script
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Office Script actions require real workbook and script identifiers at save time.
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Do not deploy placeholder `scriptId` values; `update_live_flow` can fail during
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dynamic operation validation even before a test run exists.
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Use `describe_live_connector` or `get_live_dynamic_options` when available, or
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ask the user for the workbook and script if they are not discoverable. If a real
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`scriptId` still cannot be resolved, ask the user to add the Run script action
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once in the designer, then read the flow definition and preserve the resolved
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parameters.
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---
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## Outlook
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### Outlook — Send Email
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@@ -479,6 +503,20 @@ For 1:1 ("Chat with Flow bot"), use `"location": "Chat with Flow bot"` and set
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---
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## Copilot Studio
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### Copilot Studio — Invoke Agent
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When using the Copilot Studio connector, publish the agent before running the
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flow. Draft/test agents can exist in the studio canvas but still be unavailable
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or stale through the flow connector endpoint.
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If a connector action fails with an unavailable-agent or endpoint-style error,
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publish the agent, wait briefly for propagation, then resubmit the same flow run
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before changing the flow definition.
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---
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## Approvals
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### Split Approval (Create → Wait)
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@@ -337,6 +337,23 @@ walking a time range, polling until a status changes).
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---
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### Agent Retry Loop
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When a flow calls an AI or Copilot-style agent until it reaches a terminal
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outcome, keep the loop state explicit:
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- Initialize variables such as `agentStatus`, `attempt`, and `finalPayload`
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before the `Until`.
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- Inside the loop, call the agent, validate the response, update the status, and
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delay/retry only when the status is non-terminal.
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- Put final dispatch actions such as email, SharePoint update, or Teams post
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after the loop so retries do not duplicate side effects.
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- If the platform rejects a complex `Switch` nested inside `Until`, keep the
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loop body to simple validation and state updates, then route with `Switch`
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after the loop.
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---
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### Async Polling with RequestId Correlation
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When an API starts a long-running job asynchronously (e.g. Power BI dataset refresh,
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@@ -486,6 +503,19 @@ Normalize before compare: @replace(coalesce(outputs('Value'),''),'_',' ')
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Robust non-empty check: @greater(length(trim(coalesce(string(outputs('Val')), ''))), 0)
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```
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### Unsupported / Risky Expression Assumptions
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Power Automate expressions are Workflow Definition Language, not JavaScript.
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These patterns often look plausible but do not deploy or do not behave as agents
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expect:
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| Goal | Avoid | Use instead |
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|---|---|---|
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| Build an object inline | `createObject(...)` | A Compose action with a JSON object literal |
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| Transform an array inline | `select(...)` inside an expression | Data Operations `Select` action |
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| Filter an array inline | `filter(...)` inside an expression | Data Operations `Filter array` action |
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| Find an array item index | `indexOf(array, item)` | Foreach with a counter variable, or build a keyed object map |
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### Newlines in Expressions
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> **`\n` does NOT produce a newline inside Power Automate expressions.** It is
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@@ -142,24 +142,8 @@ without a loop:
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Result: `@body('Generate_Date_Series')` → `["2025-01-06", "2025-01-07", …, "2025-01-19"]`
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```json
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// Flatten a 2D array (rows × cols) into 1D using arithmetic indexing
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"Flatten_Grid": {
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"type": "Select",
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"inputs": {
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"from": "@range(0, mul(length(outputs('Rows')), length(outputs('Cols'))))",
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"select": {
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"row": "@outputs('Rows')[div(item(), length(outputs('Cols')))]",
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"col": "@outputs('Cols')[mod(item(), length(outputs('Cols')))]"
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}
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}
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}
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```
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> `range()` is zero-based. The Cartesian product pattern above uses `div(i, cols)`
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> for the row index and `mod(i, cols)` for the column index — equivalent to a
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> nested for-loop flattened into a single pass. Useful for generating time-slot ×
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> date grids, shift × location assignments, etc.
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For Cartesian products, iterate `range(0, mul(rowCount, colCount))` and derive
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indexes with `div(item(), colCount)` and `mod(item(), colCount)`.
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---
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@@ -184,23 +168,6 @@ dictionary type, build one from an array using Select + join + json:
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Lookup: `@outputs('Assemble_Dictionary')?['myKey']`
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```json
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// Practical example: date → rate-code lookup for business rules
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"Build_Holiday_Rates": {
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"type": "Select",
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"inputs": {
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"from": "@body('Get_Holidays')?['value']",
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"select": "@concat('\"', formatDateTime(item()?['Date'], 'yyyy-MM-dd'), '\":\"', item()?['RateCode'], '\"')"
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}
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},
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"Holiday_Dict": {
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"type": "Compose",
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"inputs": "@json(concat('{', join(body('Build_Holiday_Rates'), ','), '}'))"
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}
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```
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Then inside a loop: `@coalesce(outputs('Holiday_Dict')?[item()?['Date']], 'Standard')`
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> The `json(concat('{', join(...), '}'))` pattern works for string values. For numeric
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> or boolean values, omit the inner escaped quotes around the value portion.
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> Keys must be unique — duplicate keys silently overwrite earlier ones.
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@@ -280,111 +247,20 @@ CSV → database), avoid nested `Apply to each` loops to find changed records.
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Instead, **project flat key arrays** and use `contains()` to perform set operations —
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zero nested loops, and the final loop only touches changed items.
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**Full insert/update/delete sync pattern:**
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**Insert/update/delete sync recipe:**
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```json
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// Step 1 — Project a flat key array from the DESTINATION (e.g. SharePoint)
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"Select_Dest_Keys": {
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"type": "Select",
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"inputs": {
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"from": "@outputs('Get_Dest_Items')?['body/value']",
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"select": "@item()?['Title']"
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}
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}
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// → ["KEY1", "KEY2", "KEY3", ...]
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1. `Select_Dest_Keys` from destination rows.
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2. `Filter_To_Insert`: source rows whose key is not in destination keys.
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3. `Filter_Already_Exists`: source rows whose key is in destination keys.
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4. For each compared field, run `Filter_<Field>_Changed`; combine them with
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`union()` into `Union_Changed`.
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5. `Select_Changed_Keys` from `Union_Changed`, then filter destination rows to
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only those keys before updating.
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6. `Select_Source_Keys`, then `Filter_To_Delete` destination rows whose key is
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not in source keys.
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// Step 2 — INSERT: source rows whose key is NOT in destination
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"Filter_To_Insert": {
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"type": "Query",
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"inputs": {
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"from": "@body('Source_Array')",
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"where": "@not(contains(body('Select_Dest_Keys'), item()?['key']))"
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}
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}
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// → Apply to each Filter_To_Insert → CreateItem
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// Step 3 — INNER JOIN: source rows that exist in destination
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"Filter_Already_Exists": {
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"type": "Query",
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"inputs": {
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"from": "@body('Source_Array')",
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"where": "@contains(body('Select_Dest_Keys'), item()?['key'])"
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}
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}
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// Step 4 — UPDATE: one Filter per tracked field, then union them
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"Filter_Field1_Changed": {
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"type": "Query",
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"inputs": {
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"from": "@body('Filter_Already_Exists')",
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"where": "@not(equals(item()?['field1'], item()?['dest_field1']))"
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}
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}
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"Filter_Field2_Changed": {
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"type": "Query",
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"inputs": {
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"from": "@body('Filter_Already_Exists')",
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"where": "@not(equals(item()?['field2'], item()?['dest_field2']))"
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}
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}
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"Union_Changed": {
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"type": "Compose",
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"inputs": "@union(body('Filter_Field1_Changed'), body('Filter_Field2_Changed'))"
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}
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// → rows where ANY tracked field differs
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// Step 5 — Resolve destination IDs for changed rows (no nested loop)
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"Select_Changed_Keys": {
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"type": "Select",
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"inputs": { "from": "@outputs('Union_Changed')", "select": "@item()?['key']" }
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}
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"Filter_Dest_Items_To_Update": {
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"type": "Query",
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"inputs": {
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"from": "@outputs('Get_Dest_Items')?['body/value']",
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"where": "@contains(body('Select_Changed_Keys'), item()?['Title'])"
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}
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}
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// Step 6 — Single loop over changed items only
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"Apply_to_each_Update": {
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"type": "Foreach",
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"foreach": "@body('Filter_Dest_Items_To_Update')",
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"actions": {
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"Get_Source_Row": {
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"type": "Query",
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"inputs": {
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"from": "@outputs('Union_Changed')",
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"where": "@equals(item()?['key'], items('Apply_to_each_Update')?['Title'])"
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}
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},
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"Update_Item": {
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"...": "...",
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"id": "@items('Apply_to_each_Update')?['ID']",
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"item/field1": "@first(body('Get_Source_Row'))?['field1']"
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}
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}
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}
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// Step 7 — DELETE: destination keys NOT in source
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"Select_Source_Keys": {
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"type": "Select",
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"inputs": { "from": "@body('Source_Array')", "select": "@item()?['key']" }
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}
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"Filter_To_Delete": {
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"type": "Query",
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"inputs": {
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"from": "@outputs('Get_Dest_Items')?['body/value']",
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"where": "@not(contains(body('Select_Source_Keys'), item()?['Title']))"
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}
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}
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// → Apply to each Filter_To_Delete → DeleteItem
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```
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> **Why this beats nested loops**: the naive approach (for each dest item, scan source)
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> is O(n × m) and hits Power Automate's 100k-action run limit fast on large lists.
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> This pattern is O(n + m): one pass to build key arrays, one pass per filter.
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> The update loop in Step 6 only iterates *changed* records — often a tiny fraction
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> of the full collection. Run Steps 2/4/7 in **parallel Scopes** for further speed.
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This changes O(n x m) nested loops to O(n + m) set operations and helps avoid
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Power Automate's 100k-action run limit.
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---
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@@ -649,14 +525,8 @@ Parse a raw CSV string into an array of objects using only built-in expressions.
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Avoids the premium "Parse CSV" connector action.
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```json
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"Delimiter": {
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"type": "Compose",
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"inputs": ","
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},
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"Strip_Quotes": {
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"type": "Compose",
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"inputs": "@replace(body('Get_File_Content'), '\"', '')"
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},
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"Delimiter": { "type": "Compose", "inputs": "," },
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"Strip_Quotes": { "type": "Compose", "inputs": "@replace(body('Get_File_Content'), '\"', '')" },
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"Detect_Line_Ending": {
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"type": "Compose",
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"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'))"
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@@ -665,10 +535,7 @@ Avoids the premium "Parse CSV" connector action.
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"type": "Compose",
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"inputs": "@split(first(split(outputs('Strip_Quotes'), outputs('Detect_Line_Ending'))), outputs('Delimiter'))"
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},
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"Data_Rows": {
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"type": "Compose",
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"inputs": "@skip(split(outputs('Strip_Quotes'), outputs('Detect_Line_Ending')), 1)"
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},
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"Data_Rows": { "type": "Compose", "inputs": "@skip(split(outputs('Strip_Quotes'), outputs('Detect_Line_Ending')), 1)" },
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"Select_CSV_Body": {
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"type": "Select",
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"inputs": {
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@@ -691,16 +558,9 @@ Avoids the premium "Parse CSV" connector action.
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Result: `@body('Filter_Empty_Rows')` — array of objects with header names as keys.
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> **`Detect_Line_Ending`** handles CRLF (Windows), LF (Unix), and CR (old Mac) automatically
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> using `indexOf()` with `decodeUriComponent('%0D%0A' / '%0A' / '%0D')`.
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>
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> **Dynamic key names in `Select`**: `@{outputs('Headers')[0]}` as a JSON key in a
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> `Select` shape sets the output property name at runtime from the header row —
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> this works as long as the expression is in `@{...}` interpolation syntax.
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>
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> **Columns with embedded commas**: if field values can contain the delimiter,
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> use `length(split(row, ','))` in a Switch to detect the column count and manually
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> reassemble the split fragments: `@concat(split(item(),',')[1],',',split(item(),',')[2])`
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Notes: `Detect_Line_Ending` handles CRLF/LF/CR. Dynamic keys in `Select` require
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`@{...}` interpolation. This simple pattern does not safely parse quoted fields
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with embedded delimiters; for those, use a dedicated parser or custom action.
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---
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@@ -59,6 +59,15 @@ Beyond the required `type`, `runAfter`, and `inputs`, actions can include:
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| `runtimeConfiguration` | Pagination, concurrency, secure data, chunked transfer |
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| `operationOptions` | `"Sequential"` for Foreach, `"DisableAsyncPattern"` for HTTP |
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| `limit` | Timeout override (e.g. `{"timeout": "PT2H"}`) |
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| `metadata` | Designer metadata such as `operationMetadataId` |
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#### Designer Metadata
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For existing connector actions, preserve `metadata.operationMetadataId` when you
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edit the definition. For new connector actions or Skills/HTTP response actions,
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add a stable GUID and keep it stable across updates. Do not regenerate these IDs
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on every deploy; the designer and some run-only surfaces use them to keep action
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identity consistent.
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#### `runtimeConfiguration` Variants
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@@ -93,6 +93,40 @@ Access any field dynamically: `@triggerBody()?['anyField']`
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---
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## Manual (Copilot Studio Skills)
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Use the Skills trigger when the flow is meant to be called by a Copilot Studio
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agent tool. Keep the trigger schema explicit so the agent receives predictable
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input names and types.
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```json
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"manual": {
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"type": "Request",
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"kind": "Skills",
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"inputs": {
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"schema": {
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"type": "object",
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"properties": {
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"itemId": { "type": "string" },
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"notes": { "type": "string" }
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},
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"required": ["itemId"]
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}
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},
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"metadata": {
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"operationMetadataId": "<stable-guid>"
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}
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}
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```
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After deploying a production Skills-triggered flow, call
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`add_live_flow_to_solution` with the target `solutionId`; Copilot Studio agent
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tool discovery expects the flow to be solution-aware. For MCP-driven testing,
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use a temporary HTTP twin with the same actions and payload shape, then restore
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the Skills trigger after the actions are verified.
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---
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## Automated (SharePoint Item Created)
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```json
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Reference in New Issue
Block a user