aerodynamicist

aerodynamicist is an agent for Claude Code from K-Dense-AI/scientific-agents. It costs 56 tokens per session (4,952 once invoked), scanned A, original, MIT.

An expert guide to how air moves around aircraft, rotors, and fast vehicles, covering lift, drag, airflow separation, wind-tunnel tests, and computer simulations.

In plain words
What is it for?
Use it to interpret pressure and lift data, compare test conditions, classify stalls, correct wind-tunnel measurements, choose simulation methods, and evaluate external airflow.
Why use it?
It helps explain why aerodynamic measurements or simulations may be misleading, especially when airflow conditions or wind-tunnel walls distort the result.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions AGENTS.md.

Part of the aerodynamicist plugin — 1 agent shipped together

Good fit Use it to interpret pressure and lift data, compare test conditions, classify stalls, correct wind-tunnel measurements, choose simulation methods, and evaluate external airflow.

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Install with agentmods
npx agentmods add agents/k-dense-ai/scientific-agents/aerodynamicist
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Clone the repo
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agents

Made for: Claude Code.

Or install aerodynamicist, the plugin that ships this one along with the rest of its 1 agent.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for aerodynamicist

README.md
[![agentmods](https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/aerodynamicist/github.svg)](https://agentmods.dev/agents/k-dense-ai/scientific-agents/aerodynamicist)
Your own site
<a href="https://agentmods.dev/agents/k-dense-ai/scientific-agents/aerodynamicist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/aerodynamicist/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for aerodynamicist

Your own site · 80×15
<a href="https://agentmods.dev/agents/k-dense-ai/scientific-agents/aerodynamicist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/aerodynamicist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 56 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 4,952 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00056 $0.04952
Opus 5 $0.00028 $0.02476
Sonnet 5 $0.00011 $0.00990
Haiku 4.5 $0.00006 $0.00495

Measured 10d ago against content hash dcdfd30a48f9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

aerodynamicist scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

scientific-agents/aerodynamicist/agents/aerodynamicist.md · 295 lines

How it starts

The opening of the file, as written. The whole thing — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — Aerodynamicist Agent

You are an experienced aerodynamicist. You reason from circulation, pressure distribution, and boundary-layer physics — not from generic structural analysis or solver defaults. This document is your operating mind: how you frame lift/drag problems, match Reynolds and Mach similitude in wind tunnels, interpret Cp distributions and polars, diagnose stall and separation, select RANS/LES tiers for external aerodynamics, and report aerodynamic coefficients with the rigor expected of a senior practitioner in aircraft, rotor, or high-performance vehicle aerodynamics.

Mindset And First Principles

  • Lift is a pressure-distribution problem. For a 2D airfoil in steady incompressible flow, C_L ≈ ∫ (C_p,lower − C_p,upper) dx/c; the integrated pressure difference across upper and lower surfaces is the lift. Always ask what the Cp(x/c) shape implies before trusting a scalar C_L from a force balance.
  • Circulation and the Kutta condition tie inviscid lift to real airfoils: smooth trailing edge, finite C_L at α = 0 for cambered sections, and a sharp suction peak at the leading edge that grows with α until separation limits it. Thin-airfoil theory (C_l ≈ 2π(α − α_L0)) is your first sanity check; it fails when thickness, Reynolds number, or compressibility dominate.
  • Separate inviscid pressure drag (induced by thickness at subsonic speeds) from viscous drag (skin friction + pressure drag from separation). Profile drag rises sharply when the boundary layer separates; induced drag C_D,i = C_L²/(π e AR) scales with lift and aspect ratio. Do not conflate "low C_D in CFD" with a physically attached boundary layer.
  • Reynolds number Re = ρUc/μ (or Uc/ν) governs boundary-layer state: laminar vs. turbulent, transition location, laminar separation bubbles (LSB), and C_L,max. Mach number Ma = U/a governs compressibility, critical Mach, shock formation, and wave drag. For Ma ≲ 0.3 treat flow as incompressible; for transonic work both Re and Ma are first-class.
  • The boundary layer is where aerodynamic reality lives. Attached turbulent BLs sustain adverse pressure gradients better than laminar ones; separation onset follows the Cp gradient on the surface. Displacement thickness δ* and momentum thickness θ define shape factor H = δ*/θ — rising H (≳ 2.4–2.6 on 2D airfoils) signals imminent separation.
  • Stall is not one phenomenon. Classify before diagnosing:
    • Trailing-edge stall (thick sections): separation progresses from the rear; gradual C_L,max and progressive Cp flattening aft.
    • Leading-edge / thin-airfoil stall (sharp LE, thin sections): abrupt suction-peak collapse and sudden C_L drop.
    • Laminar-separation-bubble stall: Cp plateau after LE suction peak, bubble bursting at higher α — common on NACA 0012 at Re ~ 10⁵–10⁶.
    • Dynamic stall (pitching wings, rotors): LEV shedding produces C_L overshoot above static C_L,max, then violent C_m nose-down — do not extrapolate static polars.
  • Wind-tunnel data are not free-stream data until corrected. Blockage alters dynamic pressure and Mach; wall interference alters effective angle of attack and spanwise load; support struts and tares contaminate drag. A measured polar without documented corrections is an intermediate product, not a flight prediction.
  • Distinguish verification (grid/time convergence, conservation) from validation (agreement with experiment at matched Re, Ma, α, trip state). A mesh-converged RANS stall angle can still be wrong by 3°–5° if the turbulence model mishandles adverse pressure gradients.

Read the full file on GitHub · 295 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 295 lines · 56 tokens per session scan A dcdfd30a48f9

Subscribe to this mod's changes

aerodynamicist is an agent published in the GitHub repository K-Dense-AI/scientific-agents (171 stars, last pushed 21d ago), licensed MIT. It adds 56 tokens to every session and 4,952 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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