thermal_analysis
community[skill]
Thermal & Heat Transfer Analysis - Analyze thermal system: calculate heat released, convert energy units, compute potential energy, and dynamic viscosity. Use this skill for thermal engineering tasks involving calculate heat released convert energy MeV to J calculate potential energy calculate dynamic viscosity. Combines 4 tools from 1 SCP server(s).
$
/plugin install InnoClawdetails
Thermal & Heat Transfer Analysis
Discipline: Thermal Engineering | Tools Used: 4 | Servers: 1
Description
Analyze thermal system: calculate heat released, convert energy units, compute potential energy, and dynamic viscosity.
Tools Used
calculate_heat_releasedfromserver-22(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamicsconvert_energy_MeV_to_Jfromserver-22(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamicscalculate_potential_energyfromserver-22(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamicscalculate_dynamic_viscosityfromserver-22(streamable-http) -https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamics
Workflow
- Calculate heat released
- Convert energy MeV to Joules
- Calculate potential energy
- Compute dynamic viscosity
Test Case
Input
{
"energy_MeV": 5.0,
"mass": 10.0,
"height": 5.0
}
Expected Steps
- Calculate heat released
- Convert energy MeV to Joules
- Calculate potential energy
- Compute dynamic viscosity
Usage Example
Note: Replace
sk-b04409a1-b32b-4511-9aeb-22980abdc05cwith your own SCP Hub API Key. You can obtain one from the SCP Platform.
import asyncio
import json
from contextlib import AsyncExitStack
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.sse import sse_client
SERVERS = {
"server-22": "https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamics"
}
async def connect(url, stack):
transport = streamablehttp_client(url=url, headers={"SCP-HUB-API-KEY": "sk-b04409a1-b32b-4511-9aeb-22980abdc05c"})
read, write, _ = await stack.enter_async_context(transport)
ctx = ClientSession(read, write)
session = await stack.enter_async_context(ctx)
await session.initialize()
return session
def parse(result):
try:
if hasattr(result, 'content') and result.content:
c = result.content[0]
if hasattr(c, 'text'):
try: return json.loads(c.text)
except: return c.text
return str(result)
except: return str(result)
async def main():
async with AsyncExitStack() as stack:
# Connect to required servers
sessions = {}
sessions["server-22"] = await connect("https://scp.intern-ai.org.cn/api/v1/mcp/22/Thermal_Fluid_Dynamics", stack)
# Execute workflow steps
# Step 1: Calculate heat released
result_1 = await sessions["server-22"].call_tool("calculate_heat_released", arguments={})
data_1 = parse(result_1)
print(f"Step 1 result: {json.dumps(data_1, indent=2, ensure_ascii=False)[:500]}")
# Step 2: Convert energy MeV to Joules
result_2 = await sessions["server-22"].call_tool("convert_energy_MeV_to_J", arguments={})
data_2 = parse(result_2)
print(f"Step 2 result: {json.dumps(data_2, indent=2, ensure_ascii=False)[:500]}")
# Step 3: Calculate potential energy
result_3 = await sessions["server-22"].call_tool("calculate_potential_energy", arguments={})
data_3 = parse(result_3)
print(f"Step 3 result: {json.dumps(data_3, indent=2, ensure_ascii=False)[:500]}")
# Step 4: Compute dynamic viscosity
result_4 = await sessions["server-22"].call_tool("calculate_dynamic_viscosity", arguments={})
data_4 = parse(result_4)
print(f"Step 4 result: {json.dumps(data_4, indent=2, ensure_ascii=False)[:500]}")
# Cleanup
print("Workflow complete!")
if __name__ == "__main__":
asyncio.run(main())
technical
- github
- SpectrAI-Initiative/InnoClaw
- stars
- 374
- license
- Apache-2.0
- contributors
- 16
- last commit
- 2026-04-20T01:27:21Z
- file
- .claude/skills/thermal_analysis/SKILL.md