用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/InternScience/DrClaw --skill wind-site-assessment命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
Use the ACPX CLI through DrClaw's existing exec/long_exec tools to run Codex in the current project workspace.
Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.
Convert a user style request into concrete rewrite constraints and apply that style during de-flavoring. Use when the user specifies a target tone, audience, or writing persona.
基于 SOC 职业分类
正在显示 SKILL.md
| name | wind-site-assessment |
| description | Assess wind energy potential and perform site analysis using atmospheric science calculations. |
| license | MIT license |
| metadata | {"skill-author":"PJLab"} |
| i18n | {"zh":{"description":"评估风能潜力与场地分析。"}} |
import asyncio
import json
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.read, self.write, self.get_session_id = await self.transport.__aenter__()
self.session_ctx = ClientSession(self.read, self.write)
self.session = await self.session_ctx.__aenter__()
await self.session.initialize()
return True
except Exception as e:
print(f"✗ connect failure: {e}")
return False
async def disconnect(self):
try:
if self.session:
await self.session_ctx.__aexit__(None, None, None)
if hasattr(self, 'transport'):
await self.transport.__aexit__(None, None, None)
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)}
Evaluate wind energy potential at a specific location.
Implementation:
## 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()