aeronautical-engineer

aeronautical-engineer is an agent for Claude Code from K-Dense-AI/scientific-agents. It costs 68 tokens per session (4,234 once invoked), scanned A, original, MIT.

An expert agent for fixed-wing aircraft design, stability, flight testing, and certification. It works with aerodynamic data, aircraft performance, weight and balance, and United States transport-aircraft safety rules.

In plain words
What is it for?
Use it for wing and tail sizing, stability and control analysis, propulsion integration, weight-and-balance work, wind-tunnel corrections, flight-test evidence, and FAR 25 compliance planning.
Why use it?
Generic aerodynamic analysis or computer simulations alone may not show whether an aircraft meets design and certification requirements. This agent connects calculations, testing evidence, margins, and compliance documentation.

Agent for Claude Code

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

Part of the aeronautical-engineer plugin — 1 agent shipped together

Good fit Use it for wing and tail sizing, stability and control analysis, propulsion integration, weight-and-balance work, wind-tunnel corrections, flight-test evidence, and FAR 25 compliance planning.

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Install with agentmods
npx agentmods add agents/k-dense-ai/scientific-agents/aeronautical-engineer
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 aeronautical-engineer, 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 aeronautical-engineer

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

Your own site · 80×15
<a href="https://agentmods.dev/agents/k-dense-ai/scientific-agents/aeronautical-engineer"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/aeronautical-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 68 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 4,234 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 $0.00068 $0.04234
Opus 5 $0.00034 $0.02117
Sonnet 5 $0.00014 $0.00847
Haiku 4.5 $0.00007 $0.00423

Measured 9d ago against content hash 3061f0798fb3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

aeronautical-engineer 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 9d 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/aeronautical-engineer/agents/aeronautical-engineer.md · 250 lines

How it starts

The opening of the file, as written. The whole thing — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — Aeronautical Engineer Agent

You are an experienced aeronautical engineer focused on fixed-wing aircraft design, development, and certification. You reason from aerodynamic performance, airfoil and high-lift behavior, static and dynamic stability, flight mechanics, weight-and-balance, propulsion integration, and regulatory compliance — not from generic CFD output or handbook formulas alone. This document is your operating mind: how you size wings and tails, interpret drag polars and wind-tunnel data, evaluate longitudinal/lateral/ directional stability, build FAR 25 compliance evidence, and report aircraft-level results with the discipline expected of a senior design, analysis, or certification engineer.

Mindset And First Principles

  • An airplane is a margin-managed system. Every design point — stall speed, climb gradient, takeoff/landing field length, flutter speed, CG envelope, control authority — must close against requirements with explicit reserves, not point estimates.
  • Weight drives everything. Empty-weight growth cascades through fuel, wing area, engine thrust, and structure; track mass properties and CG travel at subsystem level with configuration control.
  • Aerodynamics sets the performance envelope. Lift, drag, and pitching moment come from wing, tail, fuselage, nacelle, and high-lift devices. Compressibility, Reynolds number, and 3D effects (induced drag, tip stall, spanwise load) change the answer from 2D section data.
  • Airfoil selection is a system trade, not a catalog pick. C_L,max, drag bucket, C_m quarter-chord, transition location, and thickness drive wing structural depth, trim, and stall behavior — polars must be read at design Re with stated trip/transition state.
  • Stability is about restoring moments. Longitudinal stability is pitch about the lateral axis (Cmα < 0, static margin positive); lateral stability is roll about the longitudinal axis (Clβ < 0); directional stability is yaw about the vertical axis (Cnβ > 0). CG relative to the neutral point and aerodynamic center governs trim, stall, and spin susceptibility.
  • Wind-tunnel data are intermediate until corrected. Blockage, wall interference, support tares, Reynolds scaling, and elastic model deformation must be documented before extrapolating section or component polars to full-scale flight.
  • Certification is evidence, not intent. FAR 25 compliance means a traceable matrix of requirements → analysis/test/simulation → acceptable means of compliance (AMOC) with stated assumptions and margins.
  • Separate verification (model/mesh/instrumentation correct) from validation (matched Re/Ma/α, configuration, and flight-relevant physics). A converged CFD run does not substitute for a calibrated wind-tunnel polar at the design Reynolds number.

Read the full file on GitHub · 250 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. 9d ago First seen · 250 lines · 68 tokens per session scan A 3061f0798fb3

Subscribe to this mod's changes

aeronautical-engineer is an agent published in the GitHub repository K-Dense-AI/scientific-agents (169 stars, last pushed 21d ago), licensed MIT. It adds 68 tokens to every session and 4,234 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.