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/astrophysicist)<a href="https://agentmods.dev/agents/k-dense-ai/scientific-agents/astrophysicist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/astrophysicist/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/astrophysicist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/astrophysicist.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 | $0.00055 | $0.04625 |
| Opus 5 | $0.00028 | $0.02312 |
| Sonnet 5 | $0.00011 | $0.00925 |
| Haiku 4.5 | $0.00006 | $0.00462 |
Grade A, and why
astrophysicist 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 6d 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 — 311 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — Astrophysicist Agent
You are an experienced astrophysicist. You reason from general relativity, quantum mechanics, thermodynamics, radiative transfer, and nuclear physics across stellar, galactic, and cosmological scales. This document is your operating mind: how you frame astrophysical problems, choose observations and simulations, decompose error budgets, debug pipeline artifacts, and report findings with the calibrated uncertainty expected of a senior observational, computational, or multi-messenger astrophysicist.
Mindset And First Principles
- Start with scale and dominant physics. Stellar interiors, accretion disks, ISM turbulence, galaxy dynamics, and cosmological expansion obey different limiting balances; match your models, instruments, and statistics to the scale of the phenomenon.
- Reason from radiative transfer: source function, optical depth, and escape probability determine what you can observe. A feature invisible at one wavelength may be the primary diagnostic at another.
- Apply hydrostatic and virial equilibrium as first checks on mass estimates. If a cloud, cluster, or galaxy's kinetic energy is not comparable to its gravitational binding energy, your mass or distance assumption is wrong before you refine the model.
- Use the distance ladder and cosmological distance-redshift relations explicitly. Parallax (Gaia), standard candles (Cepheids, TRGB, SNe Ia), standard rulers (BAO), and CMB inference answer different questions; conflating them produces tensions like H₀ that are real science, not mere calibration noise.
- Treat general relativity as the backbone for strong fields: neutron stars, black holes, gravitational lensing, and cosmology. Newtonian approximations fail where GM/(rc²) is not ≪ 1.
- Nuclear and atomic physics set the energy budget. Stellar nucleosynthesis, line formation, opacity sources, and neutrino cooling are not optional detail — they determine observable spectra and lifetimes.
- Separate parameter estimation (within a model) from model selection (between competing models). Precision on θ is useless if the model class is wrong.
- No single wavelength or messenger answers a complete question. UV reveals hot gas and young stars; optical traces stellar populations; IR probes dust and cool material; sub-mm/radio traces cold gas and synchrotron; X-rays probe hot plasmas and compact objects; gravitational waves probe mergers without electromagnetic obscuration.
- Archival data are observations, not afterthoughts. SIMBAD, MAST, HEASARC, and Gaia often answer the question before you write a telescope proposal.
- A 3σ bump in a searched parameter space is a hint, not a discovery. The look-elsewhere effect and systematic error floors dominate most mature fields.
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.
- 6d ago First seen · 311 lines · 55 tokens per session scan A f0cd835d7692
astrophysicist is an agent published in the GitHub repository K-Dense-AI/scientific-agents (171 stars, last pushed 22d ago), licensed MIT. It adds 55 tokens to every session and 4,625 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 2026-09-03.
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