>- Pro+ subscription required. Tenant-wide Power Automate monitoring using the inventory, inactive owners, and compliance/health reports. Use only for aggregated tenant views. For one environment, one flow, run control, or root-cause debugging, use flowstudio-power-automate-mcp, flowstudio-power-automate-debug, or the server monitor-flow bundle. Requires FlowStudio for Teams or MCP Pro+.
npx skills add https://github.com/github/awesome-copilot --skill flowstudio-power-automate-monitoring
Monitor flow health, track failure rates, and inventory tenant assets through
the FlowStudio MCP cached store — fast reads, no PA API rate limits, and
enriched with governance metadata and remediation hints.
> ⚠️ Pro+ subscription required. This skill calls store_* tools that
> only work for FlowStudio for Teams or MCP Pro+ subscribers.
>
> If the user does not have Pro+ access: the first store_* tool call
> will return a 403/404 error. When that happens:
> 1. STOP calling store tools
> 2. Tell the user this feature requires a Pro+ subscription
> 3. Link them to https://mcp.flowstudio.app/pricing
> 4. If their question can be answered with live tools (e.g. "list flows in
> one environment"), offer to use the flowstudio-power-automate-mcp skill instead
>
> Discovery: load tool schemas via tool_search rather than tools/list —
> call with query: "select:list_store_flows,get_store_flow_summary" for the
> common monitoring tools, or load the full set with query: "skill:governance"
> (the server's governance bundle covers most monitoring reads too — this skill
> and flowstudio-power-automate-governance share the underlying tool family). This skill
> covers response shapes, behavioral notes, and workflow patterns — things
> tool_search cannot tell you. If this document disagrees with a real API
> response, the API wins.
Flow Studio scans the Power Automate API daily for each subscriber and caches
the results. There are two levels:
type, and aggregate run statistics (runPeriodTotal, runPeriodFailRate,
etc.). Environments, apps, connections, and makers are also scanned.
monitor: true) additionally get per-run detail:individual run records with status, duration, failed action names, and
remediation hints. This is what populates get_store_flow_runs and
get_store_flow_summary.
Data freshness: Check the scanned field on get_store_flow to see when
a flow was last scanned. If stale, the scanning pipeline may not be running.
Enabling monitoring: Set monitor: true via update_store_flow or the
Flow Studio for Teams app
Designating critical flows: Use update_store_flow with critical=true
on business-critical flows. This enables the governance skill's notification
rule management to auto-configure failure alerts on critical flows.
| Tool | Purpose |
|---|---|
| list_store_flows | List flows with failure rates and monitoring filters |
| get_store_flow | Full cached record: run stats, owners, tier, connections, definition (triggerUrl field included) |
| get_store_flow_summary | Aggregated run stats: success/fail rate, avg/max duration |
| get_store_flow_runs | Per-run history with duration, status, failed actions, remediation (filter status="Failed" for errors-only view) |
| update_store_flow | Set monitor flag, notification rules, tags, governance metadata |
| list_store_environments | All Power Platform environments |
| list_store_connections | All connections |
| list_store_makers | All makers (citizen developers) |
| get_store_maker | Maker detail: flow/app counts, licenses, account status |
| list_store_power_apps | All Power Apps canvas apps |
> For start/stop, use set_live_flow_state from the monitor-flow bundle
> (tool_search query: "select:set_live_flow_state") — the cache resyncs on
> the next scan. The previous set_store_flow_state convenience wrapper is
> deprecated.
| Question | Use Store | Use Live |
|---|---|---|
| How many flows are failing? | list_store_flows | — |
| What's the fail rate over 30 days? | get_store_flow_summary | — |
| Show error history for a flow | get_store_flow_runs (filter status="Failed") | — |
| Who built this flow? | get_store_flow → parse owners | — |
| Read the full flow definition | get_store_flow has it (JSON string) | get_live_flow (structured) |
| Inspect action inputs/outputs from a run | — | get_live_flow_run_action_outputs |
| Resubmit a failed run | — | resubmit_live_flow_run |
> Store tools answer "what happened?" and "how healthy is it?"
> Live tools answer "what exactly went wrong?" and "fix it now."
> If get_store_flow_runs or get_store_flow_summary return empty results,
> check: (1) is monitor: true on the flow? and (2) is the scanned field
> recent? Use get_store_flow to verify both.
list_store_flowsDirect array. Filters: monitor (bool), rule_notify_onfail (bool),
rule_notify_onmissingdays (bool).
[
{
"id": "Default-<envGuid>.<flowGuid>",
"displayName": "Stripe subscription updated",
"state": "Started",
"triggerType": "Request",
"triggerUrl": "https://...",
"tags": ["#operations", "#sensitive"],
"environmentName": "Default-aaaaaaaa-...",
"monitor": true,
"runPeriodFailRate": 0.012,
"runPeriodTotal": 82,
"createdTime": "2025-06-24T01:20:53Z",
"lastModifiedTime": "2025-06-24T03:51:03Z"
}
]
> id format: Default-<envGuid>.<flowGuid>. Split on first . to get
> environmentName and flowName.
>
> triggerUrl and tags are optional. Some entries are sparse (just id +
> monitor) — skip entries without displayName.
>
> Tags on list_store_flows are auto-extracted from the flow's description
> field (maker hashtags like #operations). Tags written via
> update_store_flow(tags=...) are stored separately and only visible on
> get_store_flow — they do NOT appear in the list response.
get_store_flowFull cached record. Key fields:
| Category | Fields |
|---|---|
| Identity | name, displayName, environmentName, state, triggerType, triggerKind, tier, sharingType |
| Run stats | runPeriodTotal, runPeriodFails, runPeriodSuccess, runPeriodFailRate, runPeriodSuccessRate, runPeriodDurationAverage/Max/Min (milliseconds), runTotal, runFails, runFirst, runLast, runToday |
| Governance | monitor (bool), rule_notify_onfail (bool), rule_notify_onmissingdays (number), rule_notify_email (string), log_notify_onfail (ISO), description, tags |
| Freshness | scanned (ISO), nextScan (ISO) |
| Lifecycle | deleted (bool), deletedTime (ISO) |
| JSON strings | actions, connections, owners, complexity, definition, createdBy, security, triggers, referencedResources, runError — all require json.loads() to parse |
> Duration fields (runPeriodDurationAverage, Max, Min) are in
> milliseconds. Divide by 1000 for seconds.
>
> runError contains the last run error as a JSON string. Parse it:
> json.loads(record["runError"]) — returns {} when no error.
get_store_flow_summaryAggregated stats over a time window (default: last 7 days).
{
"flowKey": "Default-<envGuid>.<flowGuid>",
"windowStart": null,
"windowEnd": null,
"totalRuns": 82,
"successRuns": 81,
"failRuns": 1,
"successRate": 0.988,
"failRate": 0.012,
"averageDurationSeconds": 2.877,
"maxDurationSeconds": 9.433,
"firstFailRunRemediation": null,
"firstFailRunUrl": null
}
> Returns all zeros when no run data exists for this flow in the window.
> Use startTime and endTime (ISO 8601) parameters to change the window.
get_store_flow_runsDirect array of cached run records. Parameters: startTime, endTime,
status (array — pass ["Failed"] for an errors-only view, ["Succeeded"],
or omit for all).
> Returns [] when no run data exists in the window.
Read the triggerUrl field directly from get_store_flow (cached) or
get_live_flow (live). It is null for non-HTTP triggers.
Use set_live_flow_state from the monitor-flow server bundle. The cache
catches up on the next daily scan; if you need cache freshness sooner, call
get_live_flow after the state change to confirm and let the next scan sync.
update_store_flowUpdates governance metadata. Only provided fields are updated (merge).
Returns the full updated record (same shape as get_store_flow).
Settable fields: monitor (bool), rule_notify_onfail (bool),
rule_notify_onmissingdays (number, 0=disabled),
rule_notify_email (comma-separated), description, tags,
businessImpact, businessJustification, businessValue,
ownerTeam, ownerBusinessUnit, supportGroup, supportEmail,
critical (bool), tier, security.
list_store_environmentsDirect array.
[
{
"id": "Default-aaaaaaaa-...",
"displayName": "Flow Studio (default)",
"sku": "Default",
"type": "NotSpecified",
"location": "australia",
"isDefault": true,
"isAdmin": true,
"isManagedEnvironment": false,
"createdTime": "2017-01-18T01:06:46Z"
}
]
> sku values: Default, Production, Developer, Sandbox, Teams.
list_store_connectionsDirect array. Can be very large (1500+ items).
[
{
"id": "<environmentId>.<connectionId>",
"displayName": "[email protected]",
"createdBy": "{\"id\":\"...\",\"displayName\":\"...\",\"email\":\"...\"}",
"environmentName": "...",
"statuses": "[{\"status\":\"Connected\"}]"
}
]
> createdBy and statuses are JSON strings — parse with json.loads().
list_store_makersDirect array.
[
{
"id": "09dbe02f-...",
"displayName": "Sample Maker",
"mail": "[email protected]",
"deleted": false,
"ownerFlowCount": 199,
"ownerAppCount": 209,
"userIsServicePrinciple": false
}
]
> Deleted makers have deleted: true and no displayName/mail fields.
get_store_makerFull maker record. Key fields: displayName, mail, userPrincipalName,
ownerFlowCount, ownerAppCount, accountEnabled, deleted, country,
firstFlow, firstFlowCreatedTime, lastFlowCreatedTime,
firstPowerApp, lastPowerAppCreatedTime,
licenses (JSON string of M365 SKUs).
list_store_power_appsDirect array.
[
{
"id": "<environmentId>.<appId>",
"displayName": "My App",
"environmentName": "...",
"ownerId": "09dbe02f-...",
"ownerName": "Catherine Han",
"appType": "Canvas",
"sharedUsersCount": 0,
"createdTime": "2023-08-18T01:06:22Z",
"lastModifiedTime": "2023-08-18T01:06:22Z",
"lastPublishTime": "2023-08-18T01:06:22Z"
}
]
1. list_store_flows
2. Filter where runPeriodFailRate > 0.1 and runPeriodTotal >= 5
3. Sort by runPeriodFailRate descending
4. For each: get_store_flow for full detail
1. get_store_flow → check scanned (freshness), runPeriodFailRate, runPeriodTotal
2. get_store_flow_summary → aggregated stats with optional time window
3. get_store_flow_runs(status=["Failed"]) → per-run failure detail with remediation hints
4. If deeper diagnosis needed → switch to live tools:
get_live_flow_runs → get_live_flow_run_action_outputs
1. update_store_flow with monitor=true
2. Optionally set rule_notify_onfail=true, rule_notify_email="[email protected]"
3. Run data will appear after the next daily scan
1. list_store_flows
2. Flag flows with runPeriodFailRate > 0.2 and runPeriodTotal >= 3
3. Flag monitored flows with state="Stopped" (may indicate auto-suspension)
4. For critical failures → get_store_flow_runs(status=["Failed"]) for remediation hints
1. list_store_makers
2. Identify deleted accounts still owning flows (deleted=true, ownerFlowCount > 0)
3. get_store_maker for full detail on specific users
1. list_store_environments → environment count, SKUs, locations
2. list_store_flows → flow count by state, trigger type, fail rate
3. list_store_power_apps → app count, owners, sharing
4. list_store_connections → connection count per environment
flowstudio-power-automate-mcp — Foundation skill: connection setup, MCP helper, tool discoveryflowstudio-power-automate-debug — Deep diagnosis with action-level inputs/outputs (live API)flowstudio-power-automate-build — Build and deploy flow definitionsflowstudio-power-automate-governance — Governance metadata, tagging, notification rules, CoE patternsAssess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take github/flowstudio-power-automate-monitoring from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.