Agent skill

satellitetoolbox-atmosphere

SatelliteToolboxAtmosphericModels.jl for computing atmospheric density and temperature using empirical models (Exponential, JR1971, JB2008, NRLMSISE-00). Use when computing atmospheric drag, looking up density at altitude, or working with atmospheric models for orbit propagation.

majiayu000534★ · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill satellitetoolbox-atmosphere-hammerhead-space-agenticcodingrules --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 2 KB
Bundled scripts: none
Path: skills/analysis/satellitetoolbox-atmosphere-hammerhead-space-agenticcodingrules/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# SatelliteToolboxAtmosphericModels.jl Atmospheric density and temperature models. Repo: [JuliaSpace/SatelliteToolboxAtmosphericModels.jl](https://github.com/JuliaSpace/SatelliteToolboxAtmosphericModels.jl) ## Models All functions accessed via `AtmosphericModels.` submodule prefix. ### Exponential (simplest, no space indices needed) ```julia ρ = AtmosphericModels.exponential(h) # h [m] -> density [kg/m³] ``` ### Jacchia-Roberts 1971 ```julia out = AtmosphericModels.jr1971(jd, ϕ_gd, λ, h) # auto-fetches indices out = AtmosphericModels.jr1971(jd, ϕ_gd, λ, h, F10, F10ₐ, Kp) # manual indices out.total_density # [kg/m³] out.temperature # [K] out.exospheric_temperature # [K] ``` ### Jacchia-Bowman 2008 ```julia out = AtmosphericModels.jb2008(jd, ϕ_gd, λ, h) # auto-fetches indices out = AtmosphericModels.jb2008(jd, ϕ_gd, λ, h, F10, F10ₐ, S10, S10ₐ, M10, M10ₐ, Y10, Y10ₐ, DstΔTc) ``` ### NRLMSISE-00 ```julia # NOTE: argument order differs! (time, altitude, lat, lon) out = AtmosphericModels.nrlmsise00(jd, h, ϕ_gd, λ) # auto-fetches out = AtmosphericModels.nrlmsise00(jd, h, ϕ_gd, λ, F10ₐ, F10, ap) ``` ## Output Fields (all models) - `total_density` [kg/m³], `temperature` [K], `exospheric_temp

What's inside
Steps it walks through
  1. Models
  2. Exponential (simplest, no space indices needed)
  3. Jacchia-Roberts 1971
  4. Jacchia-Bowman 2008
  5. NRLMSISE-00
  6. Output Fields (all models)
  7. Prerequisites
  8. Key Conventions
Ships with 1 file
  • metadata.json
More from claude-skill-registry
All skills →
About this skill
What does the satellitetoolbox-atmosphere skill do?

SatelliteToolboxAtmosphericModels.jl for computing atmospheric density and temperature using empirical models (Exponential, JR1971, JB2008, NRLMSISE-00). Use when computing atmospheric drag, looking up density at altitude, or working with atmospheric models for orbit propagation.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill satellitetoolbox-atmosphere-hammerhead-space-agenticcodingrules --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going