Power Automate Flows

Power Automate Flows — Solution-Aware Cloud Flows

This skill handles solution-aware Power Automate cloud flows as first-class Dataverse workflow records (category = 5). It inspects, lints, and reviews live flows, then makes the smallest correct change — patching only the workflow properties that need to move — and resolves HTTP-trigger callback URLs and the environment variables flows depend on. It is code-first and ALM-aware: live edits are for authoring and validation, while cross-environment promotion stays in the solution export/import path with connection references and environment variables kept explicit. It is intentionally generic about environment URL, publisher prefix, solution name, and folder layout — discover those per task.

When to use this

• The user wants to create, update, or analyze a cloud flow, or asks you to "look at", "fix", "enhance", or "troubleshoot" one.
• The user wants a flow's run history — recent runs, whether the last run succeeded or failed, the error on a failed run, or run durations.
• The task asks for a lint pass, a connector governance review, or a hardening review (retry policy, concurrency, pagination, idempotency, error handling, maintainability).
• An HTTP-trigger flow needs its signed callback URL resolved, stored in an environment variable, or smoke-tested.
• A flow reads an environment variable whose value must be inspected or set for the target environment.
• You must guard a critical flow against losing a switch case, branch, or required action during an edit.

Route here from the powerplatform-core orchestrator. For schema, plug-ins, PCF, or solution packaging, use the matching sibling skill instead.

Helpers

The helpers live in the plugin's scripts/ directory at the plugin root, not in this skill folder. Resolve the plugin root and invoke them like:

• python "$CLAUDE_PLUGIN_ROOT/scripts/inspect_flow.py" --spec (Claude Code)
• python "$CODEX_PLUGIN_ROOT/scripts/inspect_flow.py" --spec (also exposed as $PLUGIN_ROOT)
• Installed standalone / unsure: the scripts/ folder sits beside this skill bundle — invoke by its path within the install.

| Helper | Purpose | | --- | --- | | inspect_flow.py | Inspect one flow or list solution-scoped flows; return identifiers, state, connection-reference summary, and definition summary. | | inspect_flow_runs.py | Read-only run history. List a solution flow's recent runs (status, start/end, duration, error) from the Dataverse flowrun table by --name / --workflow-id / --unique-name, with --max-runs and an optional --status filter. Reports whether run-history capture is enabled so an empty list is not mistaken for "no runs". | | lint_flow.py | Detect missing connection references, missing triggers/actions, broken runAfter, hardcoded GUIDs, and hardcoded Dataverse URLs. Works from live Dataverse or a local clientData/definition/JSON file. | | review_flow_connectors.py | Review connector-specific read/write patterns (Dataverse, SharePoint, Outlook action shapes). Same live-or-local sources as the linter. | | review_flow_hardening.py | Hardening checklist for retry policy, concurrency, pagination, idempotency, error handling, and maintainability. Same live-or-local sources. | | create_flow.py | Create a new solution-aware flow from clientData, or from definition plus connectionReferences; add it to the selected solution. New flows start draft/off unless activate is set. | | update_flow.py | Update an existing flow by workflowId, workflowUniqueId, uniqueName, or name. When clientData is supplied, it inspects the live baseline, runs semantic regression checks, updates, then re-inspects the live result. Auto-loads a repo flow-guard contract. | | get_flow_trigger_url.py | Resolve the signed callback URL for an HTTP-trigger flow. Windows-only / optional: depends on Windows PowerShell plus the PowerApps admin module, and may prompt for a separate Power Apps sign-in. Skip gracefully where unavailable and tell the user. | | inspect_environment_variable.py | Inspect an environment variable definition or read the current value that applies in the target environment. | | set_environment_variable_value.py | Create or update the live environmentvariablevalue record for an existing definition. Prefer this over manual maker-portal edits when wiring a flow endpoint or environment-specific value. | | create_environment_variable_definition.py | Create a new solution-aware environmentvariabledefinition (schemaName must carry the publisher prefix; type is string/number/boolean/json/datasource/secret) with optional description, defaultValue, and an initial value. Use when the definition itself does not exist yet; use set_environment_variable_value.py once it does. |

Domain workflow

1. Inspect first. Run inspect_flow.py whenever the user wants to analyze, troubleshoot, or enhance a flow, so you work from the live baseline rather than assumptions.
2. Lint before large edits. Run lint_flow.py to catch missing connection references, broken runAfter, hardcoded GUIDs, and hardcoded Dataverse URLs.
3. Review when quality matters. Run review_flow_hardening.py for retry/concurrency/pagination/idempotency/error-handling concerns and review_flow_connectors.py for connector-specific patterns.
4. Change only what must change. Use update_flow.py and patch only the workflow properties that need to move (typically clientData, plus name/description/owner/state). Prefer create_flow.py only for genuinely new flows. Do not recreate a flow to deploy it.
5. Resolve and wire HTTP triggers. For an HTTP-trigger flow: create/update the flow, resolve the signed URL with get_flow_trigger_url.py, store it with set_environment_variable_value.py, then smoke-test only if the user asked. apply_requirement_spec.py can orchestrate this chain.
6. Promote through solution ALM. Keep the flow in the selected unmanaged solution and promote via solution export/import with connection references and environment variables — not by re-authoring per environment.
7. Verify and debug with run history. Use inspect_flow_runs.py to confirm the flow actually ran and whether it succeeded or failed (and why), rather than asking for a portal screenshot. Dataverse run history covers only solution-aware flows, needs the capture feature enabled (Organization.FlowRunTimeToLiveInSeconds > 0), is retained for a limited window (28 days by default), and is a best-effort mirror — the Power Automate portal is the complete, transactional source.

Safety and decision rules

• Semantic drift is a blocker by default. update_flow.py blocks updates that empty a previously non-empty switch case, remove previously existing branch actions, or violate a repo-owned flow-guard contract. Only pass --allow-semantic-drift when the branch/action removal is genuinely intended and the user accepts it.
• Keep a flow-guard contract for critical flows. update_flow.py auto-loads a guard contract from the repo's flow-guard path or the project-profile flowGuardSpecPath. For critical flows, add a real contract in the repo rather than leaving the safeguard only in chat. Start from references/power-platform.flow-guards.template.json.
• Keep environment-specific values out of the definition. Use connection references and environment variables; do not hardcode URLs or GUIDs into the flow definition.
• Do not delete flows unless the user explicitly requests it. Ask before any publish/import step that affects the environment.
• Mind the dual auth surfaces. Dataverse and the Power Apps admin surface use separate auth stacks, so trigger-URL retrieval can trigger one extra Power Apps sign-in prompt.
• Reads skip the preflight. inspect_flow.py, the lint/review helpers, and inspect_flow_runs.py are read-only — no live-mutation preflight. But treat Dataverse run history as best-effort, not proof: if a run is missing or you need guaranteed-complete history, the Power Automate portal is the source of truth.
• Preflight gate. Run the mandatory live-mutation preflight from the powerplatform-core orchestrator before any create, update, activate, or environment-variable write. Do not restate the full preflight here — invoke it there.

References

• references/power-automate-flows.md — working model, preferred execution path, helper intent, and spec patterns.
• references/queries-and-xml.md — Power Automate Dataverse queries, OData, and FetchXML inside flow actions.
• references/power-platform.flow-guards.template.json — starting template for a repo flow-guard contract protecting critical switch branches.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: satriotsubasa
• Source: satriotsubasa/PowerPlatform-Core
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.