Flowstudio-Power-Automate-Build
Flowstudio-Power-Automate-Build是一款code方向的AI技能,核心价值是>-,可用于解决开发者在code领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。
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mkdir -p ./skills/flowstudio-power-automate-build && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/flowstudio-power-automate-build/SKILL.md -o ./skills/flowstudio-power-automate-build/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Build & Deploy Power Automate Flows with FlowStudio MCP
Step-by-step guide for constructing and deploying Power Automate cloud flows
programmatically through the FlowStudio MCP server.
**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
Workflow:
1. Load current build tools.
2. Check for an existing flow.
3. Resolve connection references.
4. Build the definition.
5. Deploy.
6. Verify.
7. Test.
---
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 build patterns —
> things tool schemas cannot tell you. If this document disagrees with
> `tool_search` or a real API response, the API wins.
---
Python Helper
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---
0. Load the Current Build Tools
For a brand-new flow, load the server's `create-flow` bundle. For editing an
existing flow, load `build-flow`. This keeps the agent aligned with the MCP
server's current schema before constructing JSON.
schemas = mcp("tool_search", query="skill:create-flow")
# Includes list_live_environments, list_live_connections,
# describe_live_connector, get_live_dynamic_options, update_live_flow.If you need a tool outside the bundle, load it explicitly:
mcp("tool_search", query="select:get_live_dynamic_properties")---
1. Safety Check: Does the Flow Already Exist?
Always look before you build to avoid duplicates:
results = mcp("list_live_flows",
environmentName=ENV,
mode="owner",
search="My New Flow",
top=20)
# list_live_flows returns { "flows": [...], "mode": "...", ... }
matches = [f for f in results["flows"]
if "My New Flow".lower() in f["displayName"].lower()]
if len(matches) > 0:
# Flow exists — modify rather than create
FLOW_ID = matches[0]["id"] # plain UUID from list_live_flows
print(f"Existing flow: {FLOW_ID}")
defn = mcp("get_live_flow", environmentName=ENV, flowName=FLOW_ID)
else:
print("Flow not found — building from scratch")
FLOW_ID = NoneFor very large environments, `list_live_flows` may return a continuation URL.
Pass it back as `continuationUrl` with the same `mode` to retrieve the next
batch. Use `mode="admin"` only when the user needs all environment flows and
the MCP identity has admin rights.
---
2. Obtain Connection References
Every connector action needs a `connectionName` that points to a key in the
flow's `connectionReferences` map. That key links to an authenticated connection
in the environment.
> **MANDATORY**: You MUST call `list_live_connections` first — do NOT ask the
> user for connection names or GUIDs. The API returns the exact values you need.
> Only prompt the user if the API confirms that required connections are missing.
2a — F
🎯 Best For
- UI designers
- Product designers
- Claude users
- GitHub Copilot users
- Software engineers
💡 Use Cases
- Generating component mockups
- Creating design system tokens
- Code quality improvement
- Best practice enforcement
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Flowstudio-Power-Automate-Build to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Does this work with Figma?
Some design skills integrate with Figma plugins. Check the Works With section for supported tools.
Is Flowstudio-Power-Automate-Build compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for Flowstudio-Power-Automate-Build?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install Flowstudio-Power-Automate-Build?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/flowstudio-power-automate-build/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Skipping usability testing
AI-generated designs should be validated with real users before development.
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.