Agent skill

Atmospheric Science Calculations

by InternScience in InternScience/scp

Calculate atmospheric parameters including Coriolis parameter, geostrophic wind, heat index, potential temperature, and dewpoint for meteorology and climate science.

MITAuto-check passedResearch & Science

Install Atmospheric Science Calculations

skills CLI
$ npx skills add InternScience/scp --skill atmospheric-science-calculations -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install InternScience/scp atmospheric-science-calculations --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/InternScience/scp.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/atmospheric-science-calculations .claude/skills/atmospheric-science-calculations && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
atmospheric-science-calculations
GitHub stars
170
Used in
1 other repo
Token cost
~2.1k tokens
SKILL.md length
389 words
Files
1
Skills in repo
73
Repo updated
First seen
Licence
MIT

At a glance

Calculate atmospheric parameters including Coriolis parameter, geostrophic wind, heat index, potential temperature, and dewpoint for meteorology and climate science.

  • Works in 2 steps: MCP Server Definition → Atmospheric Calculations Workflow
  • Tasks that involve Physical and earth sciences
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Atmospheric Science Calculations is an agent skill from InternScience/scp. Calculate atmospheric parameters including Coriolis parameter, geostrophic wind, heat index, potential temperature, and dewpoint for meteorology and climate science.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Physical and earth sciences. The licence is MIT.

When your agent uses it

  • Tasks that involve Physical and earth sciences

Example prompts

  • “/atmospheric-science-calculations”

Requirements

  • Python 3

Workflow steps

2 steps, taken from the step headings in SKILL.md.

  1. MCP Server Definition
  2. Atmospheric Calculations Workflow

What it can do on your machine

Read from SKILL.md and the folder at commit cea5398. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Atmospheric Science Calculations loads about 2.1k tokens when it runs. Until then it costs about 50 tokens; SKILL.md has 389 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~50
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from InternScience/scp at commit cea5398, republished under its MIT licence (© InternScience). 389 words, ~2,115 tokens.

Download SKILL.mdSave it as .claude/skills/atmospheric-science-calculations/SKILL.md (or your agent's skills folder).
name
atmospheric-science-calculations
description
Calculate atmospheric parameters including Coriolis parameter, geostrophic wind, heat index, potential temperature, and dewpoint for meteorology and climate science.
license
MIT license
metadata.skill-author
PJLab

Atmospheric Science Calculations

Usage

1. MCP Server Definition
python
import asyncio
import json
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession

class AtmSciClient:
    """Atmospheric Science Tools 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):
        print(f"Connecting to: {self.server_url}")
        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()
            print("✓ connect success")
            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)
            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. Atmospheric Calculations Workflow

Calculate key atmospheric parameters for meteorology, climate science, and weather forecasting applications.

Workflow Steps:

  1. Calculate Coriolis Parameter - Compute Earth's rotation effect
  2. Calculate Geostrophic Wind - Determine wind from pressure gradients
  3. Calculate Heat Index - Assess human heat stress
  4. Calculate Potential Temperature - Standardize temperature measurements
  5. Calculate Dewpoint - Determine moisture content

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()

print("=== Atmospheric Science Calculations ===\n")

## Step 1: Calculate Coriolis parameter
print("Step 1: Coriolis Parameter")
latitude = 45.0  # degrees
result = await client.session.call_tool(
    "atm_calc_coriolis_parameter",
    arguments={"latitude": latitude}
)
result_data = client.parse_result(result)
print(f"Latitude: {latitude}°")
print(f"Coriolis parameter: {result_data} s⁻¹\n")

## Step 2: Calculate geostrophic wind
print("Step 2: Geostrophic Wind")
result = await client.session.call_tool(
    "atm_calc_geostrophic_wind",
    arguments={
        "pressure_gradient_x": 1.0,  # Pa/m
        "pressure_gradient_y": 0.5,  # Pa/m
        "latitude": latitude,
        "air_density": 1.225         # kg/m³
    }
)
result_data = client.parse_result(result)
print(f"Geostrophic wind (u, v): {result_data} m/s\n")

## Step 3: Calculate heat index
print("Step 3: Heat Index")
result = await client.session.call_tool(
    "atm_calc_heat_index",
    arguments={
        "temperature_f": 95.0,       # °F
        "relative_humidity": 65.0    # %
    }
)
result_data = client.parse_result(result)
print(f"Temperature: 95°F, Humidity: 65%")
print(f"Heat index: {result_data}°F\n")

## Step 4: Calculate potential temperature
print("Step 4: Potential Temperature")
result = await client.session.call_tool(
    "atm_calc_potential_temperature",
    arguments={
        "temperature_k": 288.15,     # K (15°C)
        "pressure_pa": 85000.0       # Pa (850 hPa)
    }
)
result_data = client.parse_result(result)
print(f"Temperature: 288.15 K, Pressure: 850 hPa")
print(f"Potential temperature: {result_data} K\n")

## Step 5: Calculate dewpoint
print("Step 5: Dewpoint Temperature")
result = await client.session.call_tool(
    "atm_calc_dewpoint",
    arguments={
        "temperature_c": 25.0,       # °C
        "relative_humidity": 60.0    # %
    }
)
result_data = client.parse_result(result)
print(f"Temperature: 25°C, Humidity: 60%")
print(f"Dewpoint: {result_data}°C\n")

## Step 6: Check for heatwave conditions
print("Step 6: Heatwave Detection")
temperatures = [32, 34, 35, 36, 35, 34]  # °C over 6 days
result = await client.session.call_tool(
    "atm_check_heatwave",
    arguments={
        "temperatures": temperatures,
        "threshold": 32.0,           # °C
        "min_duration": 3            # days
    }
)
result_data = client.parse_result(result)
print(f"Temperatures: {temperatures}°C")
print(f"Heatwave detected: {result_data}\n")

await client.disconnect()
Tool Descriptions

AtmSci-Tool Server:

  • atm_calc_coriolis_parameter: Calculate Coriolis parameter (f = 2Ω sin φ)

    • Args: latitude (float) - Latitude in degrees
    • Returns: Coriolis parameter in s⁻¹
  • atm_calc_geostrophic_wind: Calculate geostrophic wind from pressure gradient

    • Args: pressure_gradient_x, pressure_gradient_y (Pa/m), latitude (deg), air_density (kg/m³)
    • Returns: Wind components (u, v) in m/s
  • atm_calc_heat_index: Calculate heat index (apparent temperature)

    • Args: temperature_f (°F), relative_humidity (%)
    • Returns: Heat index in °F
  • atm_calc_potential_temperature: Calculate potential temperature

    • Args: temperature_k (K), pressure_pa (Pa)
    • Returns: Potential temperature in K
  • atm_calc_dewpoint: Calculate dewpoint temperature

    • Args: temperature_c (°C), relative_humidity (%)
    • Returns: Dewpoint temperature in °C
  • atm_check_heatwave: Detect heatwave conditions

    • Args: temperatures (list), threshold (°C), min_duration (days)
    • Returns: Boolean indicating heatwave presence
Input/Output

Inputs:

  • Temperatures in K, °C, or °F (as specified)
  • Pressures in Pa or hPa
  • Relative humidity in %
  • Latitude in degrees
  • Air density in kg/m³

Outputs:

  • Coriolis parameter: s⁻¹
  • Wind speeds: m/s
  • Temperatures: K, °C, or °F
  • Boolean flags for conditions
Use Cases
  • Weather forecasting and analysis
  • Climate model validation
  • Heat stress assessment for public health
  • Aviation meteorology
  • Agricultural meteorology
  • Renewable energy site assessment
  • Atmospheric research
Show full SKILL.md (155 more words)Show less
Physical Interpretations

Coriolis Parameter:

  • Positive in Northern Hemisphere, negative in Southern
  • Zero at equator, maximum at poles
  • Critical for large-scale atmospheric circulation

Geostrophic Wind:

  • Theoretical wind resulting from pressure gradient force and Coriolis effect
  • Valid above atmospheric boundary layer
  • Actual winds deviate due to friction and other forces

Heat Index:

  • 80°F: Caution (fatigue possible)

  • 90°F: Extreme caution (heat exhaustion possible)

  • 103°F: Danger (heat stroke likely)

  • 125°F: Extreme danger

Potential Temperature:

  • Temperature air parcel would have if brought adiabatically to reference pressure (1000 hPa)
  • Conserved for adiabatic processes
  • Used to identify air masses and atmospheric stability

Dewpoint:

  • Temperature at which air becomes saturated
  • Higher dewpoint = more moisture
  • Dewpoint > 65°F feels humid
  • Dewpoint depression (T - Td) indicates saturation level
Additional Atmospheric Tools
  • atm_calc_standard_atmosphere: Calculate standard atmosphere properties
  • generate_synthetic_sounding: Create atmospheric sounding profiles
  • workflow_storm_diagnosis: Analyze storm conditions
  • workflow_wind_site_assessment: Assess wind energy potential
  • geo_calc_distance: Calculate geographic distances
  • stats_calc_anomaly: Calculate climate anomalies
  • stats_calc_rolling_mean: Compute running averages
  • stats_linear_trend: Determine climate trends

© InternScience, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/atmospheric-science-calculations of InternScience/scp.

Open the folder on GitHubat commit cea5398

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in InternScience/scp, which our catalogue first saw on October 7, 2026.

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Questions about Atmospheric Science Calculations

What does Atmospheric Science Calculations do?

Calculate atmospheric parameters including Coriolis parameter, geostrophic wind, heat index, potential temperature, and dewpoint for meteorology and climate science. Atmospheric Science Calculations is an agent skill from InternScience/scp. Calculate atmospheric parameters including Coriolis parameter, geostrophic wind, heat index, potential temperature, and dewpoint for meteorology and climate science.

When should I use Atmospheric Science Calculations?

Atmospheric Science Calculations fits situations like: tasks that involve Physical and earth sciences.

How do I install Atmospheric Science Calculations in Claude Code?

Run `npx skills add InternScience/scp --skill atmospheric-science-calculations -a claude-code`. Or copy the skill folder (skills/atmospheric-science-calculations in InternScience/scp) into .claude/skills/atmospheric-science-calculations in your project. Claude Code loads it when a task matches its description.

How do I install Atmospheric Science Calculations in Codex?

Run `npx skills add InternScience/scp --skill atmospheric-science-calculations -a codex`. Or copy the skill folder (skills/atmospheric-science-calculations in InternScience/scp) into .agents/skills/atmospheric-science-calculations in your project. Codex loads it when a task matches its description.

Can I use Atmospheric Science Calculations in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add InternScience/scp --skill atmospheric-science-calculations -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/atmospheric-science-calculations, .gemini/skills/atmospheric-science-calculations, .github/skills/atmospheric-science-calculations and .opencode/skills/atmospheric-science-calculations in your project.

What does Atmospheric Science Calculations need to run?

SKILL.md names no scripts, command-line tools or credentials: Atmospheric Science Calculations is instructions for the agent only. Our summary lists: Python 3.

Does Atmospheric Science Calculations access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Atmospheric Science Calculations safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Atmospheric Science Calculations use?

Atmospheric Science Calculations is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Atmospheric Science Calculations use?

About 2.1k tokens (SKILL.md is roughly 8.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Atmospheric Science Calculations?

Skills that share tags, products or a category with Atmospheric Science Calculations: Astropy (zLanqing/codex-claude-academic-skills, 4.7k stars), Pymatgen (zLanqing/codex-claude-academic-skills, 4.7k stars), Cantera Ignition Delay (K-Dense-AI/scientific-agent-skills, 48k stars) and Weather (trpc-group/trpc-agent-go, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Atmospheric Science Calculations?

InternScience (a GitHub organization) maintains it in InternScience/scp, which has 170 GitHub stars. The repository holds 73 skills in this directory. The repository was last updated on June 3, 2026.

Source: InternScience/scp on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.