[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6DM4Y2QD06ASR993CH0024J":3,"skill-same:01M6DM4Y2QD06ASR993CH0024J":62},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":29,"linkOnly":30,"markdownContent":31,"markdownBody":32,"frontMatter":33,"refName":40,"commitSha":41,"skillDirectory":42,"forksCount":43,"repositoryHtmlUrl":44,"repositoryHomepageUrl":6,"ownerHtmlUrl":45,"ownerBlogUrl":6,"ownerDescription":46,"updatedAt":47,"install":48,"resources":61},"01M6DM4Y2QD06ASR993CH0024J","atmospheric-science-calculations",null,"Calculate atmospheric parameters including Coriolis parameter, geostrophic wind, heat index, potential temperature, and dewpoint for meteorology and climate science.","InternScience\u002FDrClaw","InternScience","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F154449623?v=4","01M6DHY1ZF02QASNAVMNPT7NP6","Intern Science","01M6DHY2523FY56Q5572NKAAD0",172,"MIT","https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fscience\u002Fearth\u002Fatmospheric-science-calculations\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28],"science","atmospheric science calculations","index","计算","calculate","2026-11-10 10:12:30",false,"---\nname: atmospheric-science-calculations\ndescription: Calculate atmospheric parameters including Coriolis parameter, geostrophic\n  wind, heat index, potential temperature, and dewpoint for meteorology and climate\n  science.\nlicense: MIT license\nmetadata:\n  skill-author: PJLab\ni18n:\n  zh:\n    description: 计算大气参数。\n---\n\n# Atmospheric Science Calculations\n\n## Usage\n\n### 1. MCP Server Definition\n\n```python\nimport asyncio\nimport json\nfrom mcp.client.streamable_http import streamablehttp_client\nfrom mcp import ClientSession\n\nclass AtmSciClient:\n    \"\"\"Atmospheric Science Tools MCP Client\"\"\"\n\n    def __init__(self, server_url: str, api_key: str):\n        self.server_url = server_url\n        self.api_key = api_key\n        self.session = None\n\n    async def connect(self):\n        print(f\"Connecting to: {self.server_url}\")\n        try:\n            self.transport = streamablehttp_client(\n                url=self.server_url,\n                headers={\"SCP-HUB-API-KEY\": self.api_key}\n            )\n            self.read, self.write, self.get_session_id = await self.transport.__aenter__()\n            self.session_ctx = ClientSession(self.read, self.write)\n            self.session = await self.session_ctx.__aenter__()\n            await self.session.initialize()\n            print(\"✓ connect success\")\n            return True\n        except Exception as e:\n            print(f\"✗ connect failure: {e}\")\n            return False\n\n    async def disconnect(self):\n        try:\n            if self.session:\n                await self.session_ctx.__aexit__(None, None, None)\n            if hasattr(self, 'transport'):\n                await self.transport.__aexit__(None, None, None)\n            print(\"✓ already disconnect\")\n        except Exception as e:\n            print(f\"✗ disconnect error: {e}\")\n\n    def parse_result(self, result):\n        try:\n            if hasattr(result, 'content') and result.content:\n                content = result.content[0]\n                if hasattr(content, 'text'):\n                    return json.loads(content.text)\n            return str(result)\n        except Exception as e:\n            return {\"error\": f\"parse error: {e}\", \"raw\": str(result)}\n```\n\n### 2. Atmospheric Calculations Workflow\n\nCalculate key atmospheric parameters for meteorology, climate science, and weather forecasting applications.\n\n**Workflow Steps:**\n\n1. **Calculate Coriolis Parameter** - Compute Earth's rotation effect\n2. **Calculate Geostrophic Wind** - Determine wind from pressure gradients\n3. **Calculate Heat Index** - Assess human heat stress\n4. **Calculate Potential Temperature** - Standardize temperature measurements\n5. **Calculate Dewpoint** - Determine moisture content\n\n**Implementation:**\n\n```python\n## Initialize client\nclient = AtmSciClient(\n    \"https:\u002F\u002Fscp.intern-ai.org.cn\u002Fapi\u002Fv1\u002Fmcp\u002F35\u002FAtmSci-Tool\",\n    \"\u003Cyour-api-key>\"\n)\n\nif not await client.connect():\n    print(\"connection failed\")\n    exit()\n\nprint(\"=== Atmospheric Science Calculations ===\\n\")\n\n## Step 1: Calculate Coriolis parameter\nprint(\"Step 1: Coriolis Parameter\")\nlatitude = 45.0  # degrees\nresult = await client.session.call_tool(\n    \"atm_calc_coriolis_parameter\",\n    arguments={\"latitude\": latitude}\n)\nresult_data = client.parse_result(result)\nprint(f\"Latitude: {latitude}°\")\nprint(f\"Coriolis parameter: {result_data} s⁻¹\\n\")\n\n## Step 2: Calculate geostrophic wind\nprint(\"Step 2: Geostrophic Wind\")\nresult = await client.session.call_tool(\n    \"atm_calc_geostrophic_wind\",\n    arguments={\n        \"pressure_gradient_x\": 1.0,  # Pa\u002Fm\n        \"pressure_gradient_y\": 0.5,  # Pa\u002Fm\n        \"latitude\": latitude,\n        \"air_density\": 1.225         # kg\u002Fm³\n    }\n)\nresult_data = client.parse_result(result)\nprint(f\"Geostrophic wind (u, v): {result_data} m\u002Fs\\n\")\n\n## Step 3: Calculate heat index\nprint(\"Step 3: Heat Index\")\nresult = await client.session.call_tool(\n    \"atm_calc_heat_index\",\n    arguments={\n        \"temperature_f\": 95.0,       # °F\n        \"relative_humidity\": 65.0    # %\n    }\n)\nresult_data = client.parse_result(result)\nprint(f\"Temperature: 95°F, Humidity: 65%\")\nprint(f\"Heat index: {result_data}°F\\n\")\n\n## Step 4: Calculate potential temperature\nprint(\"Step 4: Potential Temperature\")\nresult = await client.session.call_tool(\n    \"atm_calc_potential_temperature\",\n    arguments={\n        \"temperature_k\": 288.15,     # K (15°C)\n        \"pressure_pa\": 85000.0       # Pa (850 hPa)\n    }\n)\nresult_data = client.parse_result(result)\nprint(f\"Temperature: 288.15 K, Pressure: 850 hPa\")\nprint(f\"Potential temperature: {result_data} K\\n\")\n\n## Step 5: Calculate dewpoint\nprint(\"Step 5: Dewpoint Temperature\")\nresult = await client.session.call_tool(\n    \"atm_calc_dewpoint\",\n    arguments={\n        \"temperature_c\": 25.0,       # °C\n        \"relative_humidity\": 60.0    # %\n    }\n)\nresult_data = client.parse_result(result)\nprint(f\"Temperature: 25°C, Humidity: 60%\")\nprint(f\"Dewpoint: {result_data}°C\\n\")\n\n## Step 6: Check for heatwave conditions\nprint(\"Step 6: Heatwave Detection\")\ntemperatures = [32, 34, 35, 36, 35, 34]  # °C over 6 days\nresult = await client.session.call_tool(\n    \"atm_check_heatwave\",\n    arguments={\n        \"temperatures\": temperatures,\n        \"threshold\": 32.0,           # °C\n        \"min_duration\": 3            # days\n    }\n)\nresult_data = client.parse_result(result)\nprint(f\"Temperatures: {temperatures}°C\")\nprint(f\"Heatwave detected: {result_data}\\n\")\n\nawait client.disconnect()\n```\n\n### Tool Descriptions\n\n**AtmSci-Tool Server:**\n\n- `atm_calc_coriolis_parameter`: Calculate Coriolis parameter (f = 2Ω sin φ)\n  - Args: `latitude` (float) - Latitude in degrees\n  - Returns: Coriolis parameter in s⁻¹\n\n- `atm_calc_geostrophic_wind`: Calculate geostrophic wind from pressure gradient\n  - Args: `pressure_gradient_x`, `pressure_gradient_y` (Pa\u002Fm), `latitude` (deg), `air_density` (kg\u002Fm³)\n  - Returns: Wind components (u, v) in m\u002Fs\n\n- `atm_calc_heat_index`: Calculate heat index (apparent temperature)\n  - Args: `temperature_f` (°F), `relative_humidity` (%)\n  - Returns: Heat index in °F\n\n- `atm_calc_potential_temperature`: Calculate potential temperature\n  - Args: `temperature_k` (K), `pressure_pa` (Pa)\n  - Returns: Potential temperature in K\n\n- `atm_calc_dewpoint`: Calculate dewpoint temperature\n  - Args: `temperature_c` (°C), `relative_humidity` (%)\n  - Returns: Dewpoint temperature in °C\n\n- `atm_check_heatwave`: Detect heatwave conditions\n  - Args: `temperatures` (list), `threshold` (°C), `min_duration` (days)\n  - Returns: Boolean indicating heatwave presence\n\n### Input\u002FOutput\n\n**Inputs:**\n- Temperatures in K, °C, or °F (as specified)\n- Pressures in Pa or hPa\n- Relative humidity in %\n- Latitude in degrees\n- Air density in kg\u002Fm³\n\n**Outputs:**\n- Coriolis parameter: s⁻¹\n- Wind speeds: m\u002Fs\n- Temperatures: K, °C, or °F\n- Boolean flags for conditions\n\n### Use Cases\n\n- Weather forecasting and analysis\n- Climate model validation\n- Heat stress assessment for public health\n- Aviation meteorology\n- Agricultural meteorology\n- Renewable energy site assessment\n- Atmospheric research\n\n### Physical Interpretations\n\n**Coriolis Parameter:**\n- Positive in Northern Hemisphere, negative in Southern\n- Zero at equator, maximum at poles\n- Critical for large-scale atmospheric circulation\n\n**Geostrophic Wind:**\n- Theoretical wind resulting from pressure gradient force and Coriolis effect\n- Valid above atmospheric boundary layer\n- Actual winds deviate due to friction and other forces\n\n**Heat Index:**\n- >80°F: Caution (fatigue possible)\n- >90°F: Extreme caution (heat exhaustion possible)\n- >103°F: Danger (heat stroke likely)\n- >125°F: Extreme danger\n\n**Potential Temperature:**\n- Temperature air parcel would have if brought adiabatically to reference pressure (1000 hPa)\n- Conserved for adiabatic processes\n- Used to identify air masses and atmospheric stability\n\n**Dewpoint:**\n- Temperature at which air becomes saturated\n- Higher dewpoint = more moisture\n- Dewpoint > 65°F feels humid\n- Dewpoint depression (T - Td) indicates saturation level\n\n### Additional Atmospheric Tools\n\n- `atm_calc_standard_atmosphere`: Calculate standard atmosphere properties\n- `generate_synthetic_sounding`: Create atmospheric sounding profiles\n- `workflow_storm_diagnosis`: Analyze storm conditions\n- `workflow_wind_site_assessment`: Assess wind energy potential\n- `geo_calc_distance`: Calculate geographic distances\n- `stats_calc_anomaly`: Calculate climate anomalies\n- `stats_calc_rolling_mean`: Compute running averages\n- `stats_linear_trend`: Determine climate trends\n","# Atmospheric Science Calculations\n\n## Usage\n\n### 1. MCP Server Definition\n\n```python\nimport asyncio\nimport json\nfrom mcp.client.streamable_http import streamablehttp_client\nfrom mcp import ClientSession\n\nclass AtmSciClient:\n    \"\"\"Atmospheric Science Tools MCP Client\"\"\"\n\n    def __init__(self, server_url: str, api_key: str):\n        self.server_url = server_url\n        self.api_key = api_key\n        self.session = None\n\n    async def connect(self):\n        print(f\"Connecting to: {self.server_url}\")\n        try:\n            self.transport = streamablehttp_client(\n                url=self.server_url,\n                headers={\"SCP-HUB-API-KEY\": self.api_key}\n            )\n            self.read, self.write, self.get_session_id = await self.transport.__aenter__()\n            self.session_ctx = ClientSession(self.read, self.write)\n            self.session = await self.session_ctx.__aenter__()\n            await self.session.initialize()\n            print(\"✓ connect success\")\n            return True\n        except Exception as e:\n            print(f\"✗ connect failure: {e}\")\n            return False\n\n    async def disconnect(self):\n        try:\n            if self.session:\n                await self.session_ctx.__aexit__(None, None, None)\n            if hasattr(self, 'transport'):\n                await self.transport.__aexit__(None, None, None)\n            print(\"✓ already disconnect\")\n        except Exception as e:\n            print(f\"✗ disconnect error: {e}\")\n\n    def parse_result(self, result):\n        try:\n            if hasattr(result, 'content') and result.content:\n                content = result.content[0]\n                if hasattr(content, 'text'):\n                    return json.loads(content.text)\n            return str(result)\n        except Exception as e:\n            return {\"error\": f\"parse error: {e}\", \"raw\": str(result)}\n```\n\n### 2. Atmospheric Calculations Workflow\n\nCalculate key atmospheric parameters for meteorology, climate science, and weather forecasting applications.\n\n**Workflow Steps:**\n\n1. **Calculate Coriolis Parameter** - Compute Earth's rotation effect\n2. **Calculate Geostrophic Wind** - Determine wind from pressure gradients\n3. **Calculate Heat Index** - Assess human heat stress\n4. **Calculate Potential Temperature** - Standardize temperature measurements\n5. **Calculate Dewpoint** - Determine moisture content\n\n**Implementation:**\n\n```python\n## Initialize client\nclient = AtmSciClient(\n    \"https:\u002F\u002Fscp.intern-ai.org.cn\u002Fapi\u002Fv1\u002Fmcp\u002F35\u002FAtmSci-Tool\",\n    \"\u003Cyour-api-key>\"\n)\n\nif not await client.connect():\n    print(\"connection failed\")\n    exit()\n\nprint(\"=== Atmospheric Science Calculations ===\\n\")\n\n## Step 1: Calculate Coriolis parameter\nprint(\"Step 1: Coriolis Parameter\")\nlatitude = 45.0  # degrees\nresult = await client.session.call_tool(\n    \"atm_calc_coriolis_parameter\",\n    arguments={\"latitude\": latitude}\n)\nresult_data = client.parse_result(result)\nprint(f\"Latitude: {latitude}°\")\nprint(f\"Coriolis parameter: {result_data} s⁻¹\\n\")\n\n## Step 2: Calculate geostrophic wind\nprint(\"Step 2: Geostrophic Wind\")\nresult = await client.session.call_tool(\n    \"atm_calc_geostrophic_wind\",\n    arguments={\n        \"pressure_gradient_x\": 1.0,  # Pa\u002Fm\n        \"pressure_gradient_y\": 0.5,  # Pa\u002Fm\n        \"latitude\": latitude,\n        \"air_density\": 1.225         # kg\u002Fm³\n    }\n)\nresult_data = client.parse_result(result)\nprint(f\"Geostrophic wind (u, v): {result_data} m\u002Fs\\n\")\n\n## Step 3: Calculate heat index\nprint(\"Step 3: Heat Index\")\nresult = await client.session.call_tool(\n    \"atm_calc_heat_index\",\n    arguments={\n        \"temperature_f\": 95.0,       # °F\n        \"relative_humidity\": 65.0    # %\n    }\n)\nresult_data = client.parse_result(result)\nprint(f\"Temperature: 95°F, Humidity: 65%\")\nprint(f\"Heat index: {result_data}°F\\n\")\n\n## Step 4: Calculate potential temperature\nprint(\"Step 4: Potential Temperature\")\nresult = await client.session.call_tool(\n    \"atm_calc_potential_temperature\",\n    arguments={\n        \"temperature_k\": 288.15,     # K (15°C)\n        \"pressure_pa\": 85000.0       # Pa (850 hPa)\n    }\n)\nresult_data = client.parse_result(result)\nprint(f\"Temperature: 288.15 K, Pressure: 850 hPa\")\nprint(f\"Potential temperature: {result_data} K\\n\")\n\n## Step 5: Calculate dewpoint\nprint(\"Step 5: Dewpoint Temperature\")\nresult = await client.session.call_tool(\n    \"atm_calc_dewpoint\",\n    arguments={\n        \"temperature_c\": 25.0,       # °C\n        \"relative_humidity\": 60.0    # %\n    }\n)\nresult_data = client.parse_result(result)\nprint(f\"Temperature: 25°C, Humidity: 60%\")\nprint(f\"Dewpoint: {result_data}°C\\n\")\n\n## Step 6: Check for heatwave conditions\nprint(\"Step 6: Heatwave Detection\")\ntemperatures = [32, 34, 35, 36, 35, 34]  # °C over 6 days\nresult = await client.session.call_tool(\n    \"atm_check_heatwave\",\n    arguments={\n        \"temperatures\": temperatures,\n        \"threshold\": 32.0,           # °C\n        \"min_duration\": 3            # days\n    }\n)\nresult_data = client.parse_result(result)\nprint(f\"Temperatures: {temperatures}°C\")\nprint(f\"Heatwave detected: {result_data}\\n\")\n\nawait client.disconnect()\n```\n\n### Tool Descriptions\n\n**AtmSci-Tool Server:**\n\n- `atm_calc_coriolis_parameter`: Calculate Coriolis parameter (f = 2Ω sin φ)\n  - Args: `latitude` (float) - Latitude in degrees\n  - Returns: Coriolis parameter in s⁻¹\n\n- `atm_calc_geostrophic_wind`: Calculate geostrophic wind from pressure gradient\n  - Args: `pressure_gradient_x`, `pressure_gradient_y` (Pa\u002Fm), `latitude` (deg), `air_density` (kg\u002Fm³)\n  - Returns: Wind components (u, v) in m\u002Fs\n\n- `atm_calc_heat_index`: Calculate heat index (apparent temperature)\n  - Args: `temperature_f` (°F), `relative_humidity` (%)\n  - Returns: Heat index in °F\n\n- `atm_calc_potential_temperature`: Calculate potential temperature\n  - Args: `temperature_k` (K), `pressure_pa` (Pa)\n  - Returns: Potential temperature in K\n\n- `atm_calc_dewpoint`: Calculate dewpoint temperature\n  - Args: `temperature_c` (°C), `relative_humidity` (%)\n  - Returns: Dewpoint temperature in °C\n\n- `atm_check_heatwave`: Detect heatwave conditions\n  - Args: `temperatures` (list), `threshold` (°C), `min_duration` (days)\n  - Returns: Boolean indicating heatwave presence\n\n### Input\u002FOutput\n\n**Inputs:**\n- Temperatures in K, °C, or °F (as specified)\n- Pressures in Pa or hPa\n- Relative humidity in %\n- Latitude in degrees\n- Air density in kg\u002Fm³\n\n**Outputs:**\n- Coriolis parameter: s⁻¹\n- Wind speeds: m\u002Fs\n- Temperatures: K, °C, or °F\n- Boolean flags for conditions\n\n### Use Cases\n\n- Weather forecasting and analysis\n- Climate model validation\n- Heat stress assessment for public health\n- Aviation meteorology\n- Agricultural meteorology\n- Renewable energy site assessment\n- Atmospheric research\n\n### Physical Interpretations\n\n**Coriolis Parameter:**\n- Positive in Northern Hemisphere, negative in Southern\n- Zero at equator, maximum at poles\n- Critical for large-scale atmospheric circulation\n\n**Geostrophic Wind:**\n- Theoretical wind resulting from pressure gradient force and Coriolis effect\n- Valid above atmospheric boundary layer\n- Actual winds deviate due to friction and other forces\n\n**Heat Index:**\n- >80°F: Caution (fatigue possible)\n- >90°F: Extreme caution (heat exhaustion possible)\n- >103°F: Danger (heat stroke likely)\n- >125°F: Extreme danger\n\n**Potential Temperature:**\n- Temperature air parcel would have if brought adiabatically to reference pressure (1000 hPa)\n- Conserved for adiabatic processes\n- Used to identify air masses and atmospheric stability\n\n**Dewpoint:**\n- Temperature at which air becomes saturated\n- Higher dewpoint = more moisture\n- Dewpoint > 65°F feels humid\n- Dewpoint depression (T - Td) indicates saturation level\n\n### Additional Atmospheric Tools\n\n- `atm_calc_standard_atmosphere`: Calculate standard atmosphere properties\n- `generate_synthetic_sounding`: Create atmospheric sounding profiles\n- `workflow_storm_diagnosis`: Analyze storm conditions\n- `workflow_wind_site_assessment`: Assess wind energy potential\n- `geo_calc_distance`: Calculate geographic distances\n- `stats_calc_anomaly`: Calculate climate anomalies\n- `stats_calc_rolling_mean`: Compute running averages\n- `stats_linear_trend`: Determine climate trends\n",{"i18n":34,"name":5,"license":37,"metadata":38,"description":7},{"zh":35},{"description":36},"计算大气参数。","MIT license",{"skill-author":39},"PJLab","main","4f96df0d9df0eae24a992afabe08edec5fc575f0","drclaw\u002Flocal_skill_hub\u002Fscience\u002Fearth\u002Fatmospheric-science-calculations",10,"https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw","https:\u002F\u002Fgithub.com\u002FInternScience","Shanghai Artificial Intelligence Laboratory","2026-03-20 17:06:10",{"source":8,"skillName":5,"commands":49,"prompt":59,"zipAvailable":60},[50,53,56],{"tool":51,"command":52},"npx","npx skills add InternScience\u002FDrClaw --skill atmospheric-science-calculations",{"tool":54,"command":55},"bunx","bunx skills add InternScience\u002FDrClaw --skill atmospheric-science-calculations",{"tool":57,"command":58},"pnpm","pnpm dlx skills add InternScience\u002FDrClaw --skill atmospheric-science-calculations","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FInternScience\u002FDrClaw\u002Fblob\u002Fmain\u002Fdrclaw\u002Flocal_skill_hub\u002Fscience\u002Fearth\u002Fatmospheric-science-calculations\u002FSKILL.md",true,[],[63,98,121,137,155,170],{"skillNo":64,"name":65,"nameZh":6,"description":66,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":67,"categories":68,"keywords":77,"publishedAt":29},"01M6DMQESTSBW8VJW03QWHMT85","scientific-critical-thinking","Evaluate scientific claims and evidence quality. 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Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. 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