Official agent skill

Flowstudio Power Automate Debug

by github in github/awesome-copilot

Debug failing Power Automate cloud flows using the FlowStudio MCP server.

OfficialMITAuto-check passedDevelopment

Install Flowstudio Power Automate Debug

skills CLI
$ npx skills add github/awesome-copilot --skill flowstudio-power-automate-debug -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install github/awesome-copilot flowstudio-power-automate-debug --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/flowstudio-power-automate-debug .claude/skills/flowstudio-power-automate-debug && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
flowstudio-power-automate-debug
GitHub stars
40k
Used in
2 other repos
Token cost
~5k tokens
SKILL.md length
1,302 words
Files
3 (incl. references)
Skills in repo
417
Repo updated
First seen
Licence
MIT

At a glance

Debug failing Power Automate cloud flows using the FlowStudio MCP server.

  • Works in 9 steps: Locate the Flow → Find the Failing Run → Get the Top-Level Error → …
  • Tasks that involve Debugging
  • SKILL.md covers Source of Truth, Python Helper, Step 1 — Locate the Flow and Step 2 — Find the Failing Run, plus 10 more sections
  • Reaches mcp.flowstudio.app; needs MCP_TOKEN

What it does

Flowstudio Power Automate Debug is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. Debug failing Power Automate cloud flows using the FlowStudio MCP server. The Graph API only shows top-level status codes. This skill gives your agent action-level inputs and outputs to find the actual root cause. Load this skill when asked to: debug a flow, investigate a failed run, why is this flow failing, inspect action outputs, find the root cause of a flow error, fix a broken Power Automate flow, diagnose a timeout, trace a DynamicOperationRequestFailure, check connector auth errors, read error details from…

Its SKILL.md is about 5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/common-errors.md` and `references/debug-workflow.md`).

It sits in Development, covering Debugging, MCP servers and Root cause analysis. It works with Power Automate and Model Context Protocol. The repository describes itself as: Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot. The licence is MIT.

When your agent uses it

  • Tasks that involve Debugging
  • Tasks that involve MCP servers
  • Tasks that involve Root cause analysis

Example prompts

  • “/flowstudio-power-automate-debug”

Requirements

  • Python 3
  • A credential in MCP_TOKEN
  • A credential in YOUR_JWT_TOKEN

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. Locate the Flow
  2. Find the Failing Run
  3. Get the Top-Level Error
  4. Inspect the Failing Action's Inputs and Outputs
  5. Read the Flow Definition
  6. Walk Back from the Failure
  7. Pinpoint the Root Cause
  8. Apply the Fix
  9. Verify the Fix

What it can do on your machine

Read from SKILL.md and the folder at commit 727ff2e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • mcp.flowstudio.app

    Also links to:

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • MCP_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Flowstudio Power Automate Debug loads about 5k tokens when it runs, and up to ~8.3k if it reads all its reference files. Until then it costs about 167 tokens; SKILL.md has 1,302 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~167
When it runs · the whole SKILL.md, loaded when a task matches
~5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from github/awesome-copilot at commit 727ff2e, republished under its MIT licence (© github). 1,302 words, ~4,979 tokens.

Download SKILL.mdSave it as .claude/skills/flowstudio-power-automate-debug/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
flowstudio-power-automate-debug
description
Debug failing Power Automate cloud flows using the FlowStudio MCP server. The Graph API only shows top-level status codes. This skill gives your agent action-level inputs and outputs to find the actual root cause. Load this skill when asked to: debug a flow, investigate a failed run, why is this flow failing, inspect action outputs, find the root cause of a flow error, fix a broken Power Automate flow, diagnose a timeout, trace a DynamicOperationRequestFailure, check connector auth errors, read error details from a run, or troubleshoot expression failures. Requires a FlowStudio MCP subscription — see https://mcp.flowstudio.app

Power Automate Debugging with FlowStudio MCP

A step-by-step diagnostic process for investigating failing Power Automate cloud flows through the FlowStudio MCP server.

Real debugging examples: Expression error in child flow | Data entry, not a flow bug | Null value crashes child flow

Prerequisite: A FlowStudio MCP server must be reachable with a valid JWT. See the flowstudio-power-automate-mcp skill for connection setup. Subscribe at https://mcp.flowstudio.app


Source of Truth

Always call list_skills / tool_search first to confirm available tool names and parameter schemas. Tool names and parameters may change between server versions. This skill covers response shapes, behavioral notes, and diagnostic patterns — things tool schemas cannot tell you. If this document disagrees with tool_search or a real API response, the API wins.


Python Helper

python
import json, urllib.request

MCP_URL   = "https://mcp.flowstudio.app/mcp"
MCP_TOKEN = "<YOUR_JWT_TOKEN>"

def mcp(tool, **kwargs):
    payload = json.dumps({"jsonrpc": "2.0", "id": 1, "method": "tools/call",
                          "params": {"name": tool, "arguments": kwargs}}).encode()
    req = urllib.request.Request(MCP_URL, data=payload,
        headers={"x-api-key": MCP_TOKEN, "Content-Type": "application/json",
                 "User-Agent": "FlowStudio-MCP/1.0"})
    try:
        resp = urllib.request.urlopen(req, timeout=120)
    except urllib.error.HTTPError as e:
        body = e.read().decode("utf-8", errors="replace")
        raise RuntimeError(f"MCP HTTP {e.code}: {body[:200]}") from e
    raw = json.loads(resp.read())
    if "error" in raw:
        raise RuntimeError(f"MCP error: {json.dumps(raw['error'])}")
    return json.loads(raw["result"]["content"][0]["text"])

ENV = "<environment-id>"   # e.g. Default-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx

Step 1 — Locate the Flow

python
result = mcp("list_live_flows", environmentName=ENV)
# Returns a wrapper object: {mode, flows, totalCount, error}
target = next(f for f in result["flows"] if "My Flow Name" in f["displayName"])
FLOW_ID = target["id"]   # plain UUID — use directly as flowName
print(FLOW_ID)

Step 2 — Find the Failing Run

python
runs = mcp("get_live_flow_runs", environmentName=ENV, flowName=FLOW_ID, top=5)
# Returns direct array (newest first):
# [{"name": "08584296068667933411438594643CU15",
#   "status": "Failed",
#   "startTime": "2026-02-25T06:13:38.6910688Z",
#   "endTime": "2026-02-25T06:15:24.1995008Z",
#   "triggerName": "manual",
#   "error": {"code": "ActionFailed", "message": "An action failed..."}},
#  {"name": "...", "status": "Succeeded", "error": null, ...}]

for r in runs:
    print(r["name"], r["status"], r["startTime"])

RUN_ID = next(r["name"] for r in runs if r["status"] == "Failed")

Step 3 — Get the Top-Level Error

CRITICAL: get_live_flow_run_error tells you which action failed. get_live_flow_run_action_outputs tells you why. You must call BOTH. Never stop at the error alone — error codes like ActionFailed, NotSpecified, and InternalServerError are generic wrappers. The actual root cause (wrong field, null value, HTTP 500 body, stack trace) is only visible in the action's inputs and outputs.

python
err = mcp("get_live_flow_run_error",
    environmentName=ENV, flowName=FLOW_ID, runName=RUN_ID)
# Returns:
# {
#   "runName": "08584296068667933411438594643CU15",
#   "failedActions": [
#     {"actionName": "Apply_to_each_prepare_workers", "status": "Failed",
#      "error": {"code": "ActionFailed", "message": "An action failed..."},
#      "startTime": "...", "endTime": "..."},
#     {"actionName": "HTTP_find_AD_User_by_Name", "status": "Failed",
#      "code": "NotSpecified", "startTime": "...", "endTime": "..."}
#   ],
#   "allActions": [
#     {"actionName": "Apply_to_each", "status": "Skipped"},
#     {"actionName": "Compose_WeekEnd", "status": "Succeeded"},
#     ...
#   ]
# }

# failedActions is ordered outer-to-inner. The ROOT cause is the LAST entry:
root = err["failedActions"][-1]
print(f"Root action: {root['actionName']} → code: {root.get('code')}")

# allActions shows every action's status — useful for spotting what was Skipped
# See common-errors.md to decode the error code.

Step 4 — Inspect the Failing Action's Inputs and Outputs

This is the most important step. get_live_flow_run_error only gives you a generic error code. The actual error detail — HTTP status codes, response bodies, stack traces, null values — lives in the action's runtime inputs and outputs. Always inspect the failing action immediately after identifying it.

python
# Get the root failing action's full inputs and outputs
root_action = err["failedActions"][-1]["actionName"]
detail = mcp("get_live_flow_run_action_outputs",
    environmentName=ENV,
    flowName=FLOW_ID,
    runName=RUN_ID,
    actionName=root_action)

if len(detail) > 1:
    print(f"{root_action} returned {len(detail)} repetitions; inspect iteration indexes")
out = detail[0] if detail else {}
print(f"Action: {out.get('actionName')}")
print(f"Status: {out.get('status')}")

# For HTTP actions, the real error is in outputs.body
if isinstance(out.get("outputs"), dict):
    status_code = out["outputs"].get("statusCode")
    body = out["outputs"].get("body", {})
    print(f"HTTP {status_code}")
    print(json.dumps(body, indent=2)[:500])

    # Error bodies are often nested JSON strings — parse them
    if isinstance(body, dict) and "error" in body:
        err_detail = body["error"]
        if isinstance(err_detail, str):
            err_detail = json.loads(err_detail)
        print(f"Error: {err_detail.get('message', err_detail)}")

# For expression errors, the error is in the error field
if out.get("error"):
    print(f"Error: {out['error']}")

# Also check inputs — they show what expression/URL/body was used
if out.get("inputs"):
    print(f"Inputs: {json.dumps(out['inputs'], indent=2)[:500]}")
What the action outputs reveal (that error codes don't)
Error code from get_live_flow_run_errorWhat get_live_flow_run_action_outputs reveals
ActionFailedWhich nested action actually failed and its HTTP response
NotSpecifiedThe HTTP status code + response body with the real error
InternalServerErrorThe server's error message, stack trace, or API error JSON
InvalidTemplateThe exact expression that failed and the null/wrong-type value
BadRequestThe request body that was sent and why the server rejected it
Foreach iterations

When actionName refers to an action inside a foreach, the output tool can return every repetition of that action. Each item may include repetitionIndexes with the loop name and zero-based itemIndex. Use iterationIndex to inspect one iteration after you find the suspicious item:

python
all_reps = mcp("get_live_flow_run_action_outputs",
    environmentName=ENV,
    flowName=FLOW_ID,
    runName=RUN_ID,
    actionName=root_action)

for rep in all_reps[:10]:
    print(rep.get("repetitionIndexes"), rep.get("status"), rep.get("error"))

one_rep = mcp("get_live_flow_run_action_outputs",
    environmentName=ENV,
    flowName=FLOW_ID,
    runName=RUN_ID,
    actionName=root_action,
    iterationIndex=3)
Evidence Compose Bookends

For uncertain connector work, add a Compose_*_Request before the risky action and a Compose_*_Result after it, with the result action allowed on both Succeeded and Failed. This gives future debugging a clean payload snapshot without requiring another deploy. Do not include secrets or long binary payloads in these bookends.

Example: HTTP action returning 500
Error code: "InternalServerError" ← this tells you nothing

Action outputs reveal:
  HTTP 500
  body: {"error": "Cannot read properties of undefined (reading 'toLowerCase')
    at getClientParamsFromConnectionString (storage.js:20)"}
  ← THIS tells you the Azure Function crashed because a connection string is undefined
Example: Expression error on null
Error code: "BadRequest" ← generic

Action outputs reveal:
  inputs: "body('HTTP_GetTokenFromStore')?['token']?['access_token']"
  outputs: ""   ← empty string, the path resolved to null
  ← THIS tells you the response shape changed — token is at body.access_token, not body.token.access_token

Step 5 — Read the Flow Definition

python
defn = mcp("get_live_flow", environmentName=ENV, flowName=FLOW_ID)
actions = defn["properties"]["definition"]["actions"]
print(list(actions.keys()))

Find the failing action in the definition. Inspect its inputs expression to understand what data it expects.


Step 6 — Walk Back from the Failure

When the failing action's inputs reference upstream actions, inspect those too. Walk backward through the chain until you find the source of the bad data:

python
# Inspect multiple actions leading up to the failure
for action_name in [root_action, "Compose_WeekEnd", "HTTP_Get_Data"]:
    result = mcp("get_live_flow_run_action_outputs",
        environmentName=ENV,
        flowName=FLOW_ID,
        runName=RUN_ID,
        actionName=action_name)
    out = result[0] if result else {}
    print(f"\n--- {action_name} ({out.get('status')}) ---")
    print(f"Inputs:  {json.dumps(out.get('inputs', ''), indent=2)[:300]}")
    print(f"Outputs: {json.dumps(out.get('outputs', ''), indent=2)[:300]}")

⚠️ Output payloads from array-processing actions can be very large. Always slice (e.g. [:500]) before printing.

Tip: Omit actionName to list top-level actions when you're not sure which action produced the bad data. Once you pick an action inside a foreach, pass iterationIndex to avoid pulling every repetition into context.


Step 7 — Pinpoint the Root Cause

Expression Errors (e.g. split on null)

If the error mentions InvalidTemplate or a function name:

  1. Find the action in the definition
  2. Check what upstream action/expression it reads
  3. Inspect that upstream action's output for null / missing fields
python
# Example: action uses split(item()?['Name'], ' ')
# → null Name in the source data
result = mcp("get_live_flow_run_action_outputs", ..., actionName="Compose_Names")
if not result:
    print("No outputs returned for Compose_Names")
    names = []
else:
    names = result[0].get("outputs", {}).get("body") or []
nulls = [x for x in names if x.get("Name") is None]
print(f"{len(nulls)} records with null Name")
Wrong Field Path

Expression triggerBody()?['fieldName'] returns null → fieldName is wrong. Inspect the trigger output to see the actual field names:

python
result = mcp("get_live_flow_run_action_outputs", ..., actionName="<trigger-action-name>")
print(json.dumps(result[0].get("outputs"), indent=2)[:500])
HTTP Actions Returning Errors

The error code says InternalServerError or NotSpecified — always inspect the action outputs to get the actual HTTP status and response body:

python
result = mcp("get_live_flow_run_action_outputs", ..., actionName="HTTP_Get_Data")
out = result[0]
print(f"HTTP {out['outputs']['statusCode']}")
print(json.dumps(out['outputs']['body'], indent=2)[:500])
Connection / Auth Failures

Look for ConnectionAuthorizationFailed — the connection owner must match the service account running the flow. Cannot fix via API; fix in PA designer.

Outlook user-picker failures (DynamicListValuesUndefinedOrInvalid)

Outlook actions like GetEmailsV3 use parameters (mailboxAddress, to, cc, from) whose dropdown is backed by builtInOperation:AadGraph.GetUsers — which is broken at the PA listEnum layer and always returns DynamicListValuesUndefinedOrInvalid. This shows up when an agent rebuilds or modifies an Outlook action via update_live_flow and tries to resolve a user through dynamic options. Don't fix it by retrying AadGraph — switch to shared_office365users.SearchUserV2 instead (returns the same AAD user shape). Use describe_live_connector to confirm whether the affected parameter exposes a structured fallback, then call get_live_dynamic_options against shared_office365users.SearchUserV2 instead of the broken AadGraph operation. For dynamic field schemas rather than dropdown options, use get_live_dynamic_properties with the metadata returned by describe_live_connector.


Step 8 — Apply the Fix

For expression/data issues:

python
defn = mcp("get_live_flow", environmentName=ENV, flowName=FLOW_ID)
acts = defn["properties"]["definition"]["actions"]

# Example: fix split on potentially-null Name
acts["Compose_Names"]["inputs"] = \
    "@coalesce(item()?['Name'], 'Unknown')"

conn_refs = defn["properties"]["connectionReferences"]
result = mcp("update_live_flow",
    environmentName=ENV,
    flowName=FLOW_ID,
    definition=defn["properties"]["definition"],
    connectionReferences=conn_refs)

print(result.get("error"))  # None = success

⚠️ update_live_flow always returns an error key. A value of null (Python None) means success.


Show full SKILL.md (530 more words)Show less

Step 9 — Verify the Fix

Use resubmit_live_flow_run to test ANY flow — not just HTTP triggers. resubmit_live_flow_run replays a previous run using its original trigger payload. This works for every trigger type: Recurrence, SharePoint "When an item is created", connector webhooks, Button triggers, and HTTP triggers. You do NOT need to ask the user to manually trigger the flow or wait for the next scheduled run.

The only case where resubmit is not available is a brand-new flow that has never run — it has no prior run to replay.

python
# Resubmit the failed run — works for ANY trigger type
resubmit = mcp("resubmit_live_flow_run",
    environmentName=ENV, flowName=FLOW_ID, runName=RUN_ID)
print(resubmit)   # {"resubmitted": true, "triggerName": "..."}

# Wait ~30 s then check
import time; time.sleep(30)
new_runs = mcp("get_live_flow_runs", environmentName=ENV, flowName=FLOW_ID, top=3)
print(new_runs[0]["status"])   # Succeeded = done
When to use resubmit vs trigger
ScenarioUseWhy
Testing a fix on any flowresubmit_live_flow_runReplays the exact trigger payload that caused the failure — best way to verify
Recurrence / scheduled flowtrigger_live_flow (no body)Runs it now, like the portal's "Run flow" button; resubmit replays a past run's data
SharePoint / connector triggerresubmit_live_flow_runCannot be triggered without creating a real SP item
HTTP, Button, or PowerApps trigger with custom test payloadtrigger_live_flowWhen you need to send different data than the original run
Brand-new flow, never runtrigger_live_flowNo prior run exists to resubmit
Testing HTTP, Button, and PowerApps flows with custom payloads

For flows with a Request trigger (HTTP request, manual Button, or PowerApps), use trigger_live_flow when you need to send a different payload than the original run. Pass trigger inputs as body for every kind:

python
# First inspect what the trigger expects — read directly from the flow definition
defn = mcp("get_live_flow", environmentName=ENV, flowName=FLOW_ID)
triggers = defn["properties"]["definition"]["triggers"]
manual = next(iter(triggers.values()))   # usually the only trigger on HTTP flows
request_schema = manual.get("inputs", {}).get("schema")
print("Expected body schema:", request_schema)

# Response schemas live on Response action(s) in the actions block
for name, act in defn["properties"]["definition"]["actions"].items():
    if act.get("type") == "Response":
        print(f"Response {name}:", act.get("inputs", {}).get("schema"))

# Trigger with a test payload
result = mcp("trigger_live_flow",
    environmentName=ENV,
    flowName=FLOW_ID,
    body={"name": "Test User", "value": 42})
print(f"Status: {result['responseStatus']}, Body: {result.get('responseBody')}")
print(f"Kind: {result['triggerKind']}, via: {result['invocation']}, run: {result.get('runName')}")
if result.get("warning"):
    print(result["warning"])   # required trigger inputs you left out

trigger_live_flow handles AAD-authenticated triggers automatically. Works for Request triggers (HTTP request, Button, PowerApps) and for scheduled (Recurrence) flows, which it runs immediately — with no body, since a scheduled trigger takes no inputs (a body is refused). Automated connector triggers only fire from their source event.

Power Automate does not enforce a trigger's required inputs. If you leave one out the run still starts, with that input null, and the result carries a warning naming the missing keys. Cancel the run and call again with the full body if that matters.

runName is only returned for Button and PowerApps runs. For HTTP triggers find the run with get_live_flow_runs.

Over a browser-extension key, Button and PowerApps triggers run only with an empty body. The tool says so and lists the ways round it: resubmit a past run, default the inputs inside the flow with coalesce(triggerBody()?['x'], 'value'), or use a standard API key.


Quick-Reference Diagnostic Decision Tree

SymptomFirst ToolThen ALWAYS CallWhat to Look For
Flow shows as Failedget_live_flow_run_errorget_live_flow_run_action_outputs on the failing actionHTTP status + response body in outputs
Error code is generic (ActionFailed, NotSpecified)—get_live_flow_run_action_outputsThe outputs.body contains the real error message, stack trace, or API error
HTTP action returns 500—get_live_flow_run_action_outputsoutputs.statusCode + outputs.body with server error detail
Expression crash—get_live_flow_run_action_outputs on prior actionnull / wrong-type fields in output body
Flow never startsget_live_flow—check properties.state = "Started"
Action returns wrong dataget_live_flow_run_action_outputs—actual output body vs expected
Fix applied but still failsget_live_flow_runs after resubmit—new run status field

Rule: never diagnose from error codes alone. get_live_flow_run_error identifies the failing action. get_live_flow_run_action_outputs reveals the actual cause. Always call both.


Reference Files

  • flowstudio-power-automate-mcp — Foundation skill: connection setup, MCP helper, tool discovery
  • flowstudio-power-automate-build — Build and deploy new flows

© github, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in skills/flowstudio-power-automate-debug of github/awesome-copilot.

  • SKILL.md
  • references/common-errors.md
  • references/debug-workflow.md

Open the folder on GitHubat commit 727ff2e

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in github/awesome-copilot, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Questions about Flowstudio Power Automate Debug

What does Flowstudio Power Automate Debug do?

Debug failing Power Automate cloud flows using the FlowStudio MCP server. Flowstudio Power Automate Debug is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. Debug failing Power Automate cloud flows using the FlowStudio MCP server.

When should I use Flowstudio Power Automate Debug?

Flowstudio Power Automate Debug fits situations like: tasks that involve Debugging; tasks that involve MCP servers; tasks that involve Root cause analysis.

How do I install Flowstudio Power Automate Debug in Claude Code?

Run `npx skills add github/awesome-copilot --skill flowstudio-power-automate-debug -a claude-code`. Or copy the skill folder (skills/flowstudio-power-automate-debug in github/awesome-copilot) into .claude/skills/flowstudio-power-automate-debug in your project. Claude Code loads it when a task matches its description.

How do I install Flowstudio Power Automate Debug in Codex?

Run `npx skills add github/awesome-copilot --skill flowstudio-power-automate-debug -a codex`. Or copy the skill folder (skills/flowstudio-power-automate-debug in github/awesome-copilot) into .agents/skills/flowstudio-power-automate-debug in your project. Codex loads it when a task matches its description.

Can I use Flowstudio Power Automate Debug in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add github/awesome-copilot --skill flowstudio-power-automate-debug -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/flowstudio-power-automate-debug, .gemini/skills/flowstudio-power-automate-debug, .github/skills/flowstudio-power-automate-debug and .opencode/skills/flowstudio-power-automate-debug in your project.

What does Flowstudio Power Automate Debug need to run?

Going by SKILL.md and its folder, Flowstudio Power Automate Debug needs credentials named MCP_TOKEN. Our summary lists: Python 3; A credential in MCP_TOKEN; A credential in YOUR_JWT_TOKEN.

Does Flowstudio Power Automate Debug access the network?

SKILL.md names 2 domains. In commands or code: mcp.flowstudio.app; the agent is likely to contact it when it follows the instructions. As links in the text: github.com. This is read from the text; nothing was executed.

Is Flowstudio Power Automate Debug safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Flowstudio Power Automate Debug use?

Flowstudio Power Automate Debug is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Flowstudio Power Automate Debug use?

About 5k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.3k tokens, read only when the agent opens those files.

What are the alternatives to Flowstudio Power Automate Debug?

Skills that share tags, products or a category with Flowstudio Power Automate Debug: Debug (agentic-community/mcp-gateway-registry, 962 stars), QA Find Bugs MCP (bex-co/beancount-io, 294 stars), QA Find Bugs Mobile (bex-co/beancount-io, 294 stars) and Octocode Code Research (bgauryy/octocode, 946 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Flowstudio Power Automate Debug?

github (a GitHub organization, an official publisher) maintains it in github/awesome-copilot, which has 39,748 GitHub stars. The repository holds 417 skills in this directory. The repository was last updated on October 7, 2026.

Source: github/awesome-copilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.