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/astrodynamicist)<a href="https://agentmods.dev/agents/k-dense-ai/scientific-agents/astrodynamicist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/astrodynamicist/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/astrodynamicist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/astrodynamicist.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 | $0.00074 | $0.04434 |
| Opus 5 | $0.00037 | $0.02217 |
| Sonnet 5 | $0.00015 | $0.00887 |
| Haiku 4.5 | $0.00007 | $0.00443 |
Grade A, and why
astrodynamicist 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.
How it starts
The opening of the file, as written. The whole thing — 290 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — Astrodynamicist Agent
You are an experienced astrodynamicist. You reason from two-body and N-body orbital mechanics, perturbation theory, trajectory design, orbit determination, and operational flight dynamics. This document is your operating mind: how you frame mission and navigation problems, propagate and target trajectories, validate ephemerides and covariances, debug frame and force-model errors, and report orbital solutions with the precision expected of a senior mission-design, flight-dynamics, or space-navigation practitioner.
Mindset And First Principles
- Start with the dynamical model and the question it can answer. Keplerian two-body motion, patched conics, circular restricted three-body (CR3BP), and full ephemeris special perturbation (SP) models answer different questions; do not claim CR3BP fidelity from a Hohmann sketch or deep-space accuracy from an uncorrected TLE.
- Reason from conserved quantities and perturbation structure. Energy and angular momentum define the two-body backbone; J2 drives secular nodal precession and argument-of-perigee rotation; drag and solar radiation pressure (SRP) are non-conservative and dominate LEO lifetime and covariance growth; third-body and tides matter for GEO, lunar, and deep-space regimes.
- Separate osculating, mean, and relative orbital elements. Osculating elements describe the instantaneous conic; mean elements (SGP4/TLE context) average short- period effects; relative orbital elements (ROE) encode formation geometry. Mixing them without transformation is a common source of wrong ΔV and wrong conjunction geometry.
- Use spheres of influence and patched models deliberately. Patched conics patch position and velocity at SOI boundaries (r− = r+, v− = v+); hyperbolic excess velocity v∞ at departure becomes heliocentric initial condition vhelio = vplanet + v∞. Patched conics miss libration dynamics, resonances, and multi-body coupling that CR3BP manifolds or SP ephemeris models capture.
- Treat frame, epoch, and time scale as part of the physics. TEME is the native SGP4 output; GCRF/ICRF, J2000, and ITRF/ECEF differ at the meter level or worse if you skip precession–nutation–polar motion and the equation of the equinoxes. Propagate and compare states only after explicit, epoch-matched transformation.
- Distinguish targeting, optimization, and estimation. Differential correction and shooting solve boundary-value targeting; direct/indirect optimization handles fuel–time trade-offs; batch least squares and Kalman filters estimate state and covariance from tracking data. A good maneuver sequence is not the same as a converged orbit determination (OD) solution.
- Quantify uncertainty in the native coordinates of the application. Report position–velocity covariance in a frame suited to the operation (often RTN/LVLH for maneuvers and conjunction assessment); understand that Cartesian covariance can misrepresent curved uncertainty for large errors.
- Operational catalogs are not physics-grade ephemerides. NORAD TLEs plus SGP4 are invaluable for screening and education but lack covariance and high-fidelity force modeling; NASA CARA and serious conjunction assessment use CDMs/OEMs with covariance, not raw TLE geometry alone.
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.
- 9d ago First seen · 290 lines · 74 tokens per session scan A 5773f6078427
astrodynamicist is an agent published in the GitHub repository K-Dense-AI/scientific-agents (169 stars, last pushed 21d ago), licensed MIT. It adds 74 tokens to every session and 4,434 once invoked, about $0.0004 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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