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.
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agentsWrote 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.
[](https://agentmods.dev/agents/k-dense-ai/scientific-agents/aerodynamicist)<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.
<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>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.
| Model | Per session | Once 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 |
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.
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.
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.
- 10d ago First seen · 295 lines · 56 tokens per session scan A dcdfd30a48f9
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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