wind-site-assessment
Assess wind energy potential and perform site analysis using atmospheric science calculations.
Best use case
wind-site-assessment is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Assess wind energy potential and perform site analysis using atmospheric science calculations.
Teams using wind-site-assessment should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/wind-site-assessment/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How wind-site-assessment Compares
| Feature / Agent | wind-site-assessment | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Assess wind energy potential and perform site analysis using atmospheric science calculations.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Wind Site Assessment
## Usage
### 1. MCP Server Definition
```python
import asyncio
import json
from contextlib import AsyncExitStack
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
class AtmSciClient:
"""AtmSci-Tool MCP Client"""
def __init__(self, server_url: str, api_key: str):
self.server_url = server_url
self.api_key = api_key
self.session = None
async def connect(self):
try:
self.transport = streamablehttp_client(
url=self.server_url,
headers={"SCP-HUB-API-KEY": self.api_key}
)
self._stack = AsyncExitStack()
await self._stack.__aenter__()
self.read, self.write, self.get_session_id = await self._stack.enter_async_context(self.transport)
self.session_ctx = ClientSession(self.read, self.write)
self.session = await self._stack.enter_async_context(self.session_ctx)
await self.session.initialize()
return True
except Exception as e:
print(f"✗ connect failure: {e}")
return False
async def disconnect(self):
"""Disconnect from server"""
try:
if hasattr(self, '_stack'):
await self._stack.aclose()
print("✓ already disconnect")
except Exception as e:
print(f"✗ disconnect error: {e}")
def parse_result(self, result):
try:
if hasattr(result, 'content') and result.content:
content = result.content[0]
if hasattr(content, 'text'):
return json.loads(content.text)
return str(result)
except Exception as e:
return {"error": f"parse error: {e}", "raw": str(result)}
```
### 2. Wind Site Assessment Workflow
Evaluate wind energy potential at a specific location.
**Implementation:**
```python
## Initialize client
client = AtmSciClient(
"https://scp.intern-ai.org.cn/api/v1/mcp/35/AtmSci-Tool",
"<your-api-key>"
)
if not await client.connect():
print("connection failed")
exit()
## Input: Wind measurements
wind_speeds = [6.5, 7.2, 8.1, 5.9, 9.3] # m/s at hub height
hub_height = 80 # meters
air_density = 1.225 # kg/m³
## Calculate wind power and assess site viability
# Note: Use appropriate atmospheric science tools
result = await client.session.call_tool(
"wind_power_assessment",
arguments={
"wind_speeds": wind_speeds,
"hub_height": hub_height,
"air_density": air_density
}
)
assessment = client.parse_result(result)
print(f"Average wind speed: {assessment['avg_speed']:.2f} m/s")
print(f"Wind power density: {assessment['power_density']:.2f} W/m²")
print(f"Site classification: {assessment['classification']}")
await client.disconnect()
```
### Use Cases
- Wind farm site selection
- Renewable energy assessment
- Atmospheric boundary layer studies
- Wind resource mappingRelated Skills
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