Debug
agentic-community/mcp-gateway-registry
Debug issues in the MCP Gateway Registry using first-principles thinking.
Debug failing Power Automate cloud flows using the FlowStudio MCP server.
$ npx skills add github/awesome-copilot --skill flowstudio-power-automate-debug -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install github/awesome-copilot flowstudio-power-automate-debug --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "flowstudio-power-automate-debug" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/flowstudio-power-automate-debug into .claude/skills/flowstudio-power-automate-debug/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "flowstudio-power-automate-debug", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/github/awesome-copilot/tree/main/skills/flowstudio-power-automate-debugType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add github/awesome-copilot --skill flowstudio-power-automate-debug -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install github/awesome-copilot flowstudio-power-automate-debug --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/flowstudio-power-automate-debug .agents/skills/flowstudio-power-automate-debug && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "flowstudio-power-automate-debug" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/flowstudio-power-automate-debug into .agents/skills/flowstudio-power-automate-debug/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "flowstudio-power-automate-debug", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add github/awesome-copilot --skill flowstudio-power-automate-debug -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install github/awesome-copilot flowstudio-power-automate-debug --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/flowstudio-power-automate-debug .cursor/skills/flowstudio-power-automate-debug && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "flowstudio-power-automate-debug" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/flowstudio-power-automate-debug into .cursor/skills/flowstudio-power-automate-debug/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "flowstudio-power-automate-debug", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/github/awesome-copilot.git --path skills/flowstudio-power-automate-debug--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add github/awesome-copilot --skill flowstudio-power-automate-debug -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install github/awesome-copilot flowstudio-power-automate-debug --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/flowstudio-power-automate-debug .gemini/skills/flowstudio-power-automate-debug && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "flowstudio-power-automate-debug" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/flowstudio-power-automate-debug into .gemini/skills/flowstudio-power-automate-debug/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "flowstudio-power-automate-debug", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install github/awesome-copilot flowstudio-power-automate-debugInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add github/awesome-copilot --skill flowstudio-power-automate-debug -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/flowstudio-power-automate-debug .github/skills/flowstudio-power-automate-debug && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "flowstudio-power-automate-debug" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/flowstudio-power-automate-debug into .github/skills/flowstudio-power-automate-debug/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "flowstudio-power-automate-debug", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add github/awesome-copilot --skill flowstudio-power-automate-debug -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install github/awesome-copilot flowstudio-power-automate-debug --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/flowstudio-power-automate-debug .opencode/skills/flowstudio-power-automate-debug && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "flowstudio-power-automate-debug" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/flowstudio-power-automate-debug into .opencode/skills/flowstudio-power-automate-debug/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "flowstudio-power-automate-debug", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
flowstudio-power-automate-debugDebug 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. 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.
9 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 727ff2e. It shows what the files ask for, not the result of running them.
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.
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.
Hosts in commands or code, which the agent is likely to contact:
mcp.flowstudio.appAlso links to:
github.comFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
MCP_TOKENFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from github/awesome-copilot at commit 727ff2e, republished under its MIT licence (© github). 1,302 words, ~4,979 tokens.
.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.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
Always call
list_skills/tool_searchfirst 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 withtool_searchor a real API response, the API wins.
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-xxxxxxxxxxxxresult = 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)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")CRITICAL:
get_live_flow_run_errortells you which action failed.get_live_flow_run_action_outputstells you why. You must call BOTH. Never stop at the error alone — error codes likeActionFailed,NotSpecified, andInternalServerErrorare 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.
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.This is the most important step.
get_live_flow_run_erroronly 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.
# 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]}")Error code from get_live_flow_run_error | What get_live_flow_run_action_outputs reveals |
|---|---|
ActionFailed | Which nested action actually failed and its HTTP response |
NotSpecified | The HTTP status code + response body with the real error |
InternalServerError | The server's error message, stack trace, or API error JSON |
InvalidTemplate | The exact expression that failed and the null/wrong-type value |
BadRequest | The request body that was sent and why the server rejected it |
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:
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)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.
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 undefinedError 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_tokendefn = 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.
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:
# 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
actionNameto list top-level actions when you're not sure which action produced the bad data. Once you pick an action inside a foreach, passiterationIndexto avoid pulling every repetition into context.
split on null)If the error mentions InvalidTemplate or a function name:
# 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")Expression triggerBody()?['fieldName'] returns null → fieldName is wrong.
Inspect the trigger output to see the actual field names:
result = mcp("get_live_flow_run_action_outputs", ..., actionName="<trigger-action-name>")
print(json.dumps(result[0].get("outputs"), indent=2)[:500])The error code says InternalServerError or NotSpecified — always inspect
the action outputs to get the actual HTTP status and response body:
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])Look for ConnectionAuthorizationFailed — the connection owner must match the
service account running the flow. Cannot fix via API; fix in PA designer.
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.
For expression/data issues:
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_flowalways returns anerrorkey. A value ofnull(PythonNone) means success.
Use
resubmit_live_flow_runto test ANY flow — not just HTTP triggers.resubmit_live_flow_runreplays 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
resubmitis not available is a brand-new flow that has never run — it has no prior run to replay.
# 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| Scenario | Use | Why |
|---|---|---|
| Testing a fix on any flow | resubmit_live_flow_run | Replays the exact trigger payload that caused the failure — best way to verify |
| Recurrence / scheduled flow | trigger_live_flow (no body) | Runs it now, like the portal's "Run flow" button; resubmit replays a past run's data |
| SharePoint / connector trigger | resubmit_live_flow_run | Cannot be triggered without creating a real SP item |
| HTTP, Button, or PowerApps trigger with custom test payload | trigger_live_flow | When you need to send different data than the original run |
| Brand-new flow, never run | trigger_live_flow | No prior run exists to resubmit |
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:
# 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_flowhandles AAD-authenticated triggers automatically. Works forRequesttriggers (HTTP request, Button, PowerApps) and for scheduled (Recurrence) flows, which it runs immediately — with nobody, 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
requiredinputs. If you leave one out the run still starts, with that input null, and the result carries awarningnaming the missing keys. Cancel the run and call again with the full body if that matters.
runNameis only returned for Button and PowerApps runs. For HTTP triggers find the run withget_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.
| Symptom | First Tool | Then ALWAYS Call | What to Look For |
|---|---|---|---|
| Flow shows as Failed | get_live_flow_run_error | get_live_flow_run_action_outputs on the failing action | HTTP status + response body in outputs |
Error code is generic (ActionFailed, NotSpecified) | — | get_live_flow_run_action_outputs | The outputs.body contains the real error message, stack trace, or API error |
| HTTP action returns 500 | — | get_live_flow_run_action_outputs | outputs.statusCode + outputs.body with server error detail |
| Expression crash | — | get_live_flow_run_action_outputs on prior action | null / wrong-type fields in output body |
| Flow never starts | get_live_flow | — | check properties.state = "Started" |
| Action returns wrong data | get_live_flow_run_action_outputs | — | actual output body vs expected |
| Fix applied but still fails | get_live_flow_runs after resubmit | — | new run status field |
Rule: never diagnose from error codes alone.
get_live_flow_run_erroridentifies the failing action.get_live_flow_run_action_outputsreveals the actual cause. Always call both.
flowstudio-power-automate-mcp — Foundation skill: connection setup, MCP helper, tool discoveryflowstudio-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
SKILL.md and 2 other files (references) in skills/flowstudio-power-automate-debug of github/awesome-copilot.
Open the folder on GitHubat commit 727ff2e
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.
Flowstudio Power Automate Debug next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Flowstudio Power Automate Debug this skillgithub/awesome-copilot | 40k | 2 repos | ~5k | Automated safety check: Pass | MIT | |
| Debugagentic-community/mcp-gateway-registry | 962 | — | ~1.8k | Automated safety check: Notes | Apache-2.0 | |
| QA Find Bugs MCPbex-co/beancount-io | 294 | — | ~3k | Automated safety check: Pass | MIT | |
| QA Find Bugs Mobilebex-co/beancount-io | 294 | — | ~2.2k | Automated safety check: Notes | MIT | |
| Octocode Code Researchbgauryy/octocode | 946 | — | ~1.5k | Automated safety check: Pass | MIT | |
| Misakanet Failure MemoryIkalus1988/MisakaNet | 524 | — | ~1.9k | Automated safety check: Pass | Apache-2.0 |
agentic-community/mcp-gateway-registry
Debug issues in the MCP Gateway Registry using first-principles thinking.
bex-co/beancount-io
Hunt bugs in the Beancount.io remote MCP server by driving the real POST /api-gateway/mcp endpoint with JSON-RPC and real MCP clients, checking transport, discovery, credential boundaries, tool and…
bex-co/beancount-io
Hunt bugs in the running Beancount mobile app using local Expo MCP on the iPhone 17e simulator, sign in with QAEMAIL and QAPASSWORD, reproduce failures, trace root causes, and deduplicate findings.
bgauryy/octocode
Researches code with evidence: traces callers, imports and cross-repo links, diagnoses failures and reports findings with exact file and line references and a confidence label.
Ikalus1988/MisakaNet
Search and record failure-recovery lessons from real engineering sessions; submit and verify debugging lessons across the MisakaNet network.
drhelius/Gearcoleco
Debug and trace ColecoVision and Super Game Module games using the Gearcoleco emulator MCP server.
github/awesome-copilot
Maps an unfamiliar codebase into seven evidence-backed documents in docs/codebase/, using a scan script and templates, for onboarding or architecture write-ups.
github/awesome-copilot
Designs Azure infrastructure from a natural-language description, or diagrams an existing resource group, then refines the design through conversation and deploys it with Bicep.
github/awesome-copilot
Generates, edits and validates draw.io files with correct mxGraph XML, covering flowcharts, architecture, sequence, ER and UML class diagrams.
github/awesome-copilot
Cleans raw credit data and screens variables before loan modeling, dropping unstable, noisy or redundant features and writing an Excel report of every step.
github/awesome-copilot
Builds a warm, browser-based daily focus board the user updates by talking to their agent, with Eisenhower priorities, a brain-dump box and kind not-today carryover.
github/awesome-copilot
End-to-end skill for building, testing, linting, versioning, and publishing a production-grade Python library to PyPI.
Works with
Categories
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.
Flowstudio Power Automate Debug fits situations like: tasks that involve Debugging; tasks that involve MCP servers; tasks that involve Root cause analysis.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.