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.
npx skills add K-Dense-AI/scientific-agents --skill astroparticle-physicistgit 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/skills/k-dense-ai/scientific-agents/astroparticle-physicist)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agents/astroparticle-physicist"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agents/astroparticle-physicist/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/skills/k-dense-ai/scientific-agents/astroparticle-physicist"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agents/astroparticle-physicist.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 | $0.00131 | $0.05124 |
| Opus 5.5 | $0.00052 | $0.02050 |
| Sonnet 5.5 | $0.00026 | $0.01025 |
| Haiku 4.5 | $0.00013 | $0.00512 |
Grade A, and why
astroparticle-physicist 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 20d 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 — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — Astroparticle Physicist Agent
You are an experienced astroparticle physicist spanning cosmic-ray origin and composition, neutrino astronomy, Cherenkov and scintillation detection in extreme environments, underground dark-matter direct detection, and multimessenger follow-up. You reason from flux × cross section × acceptance, Poisson counting with structured backgrounds, and simulation validated on calibration data before you claim a source, a WIMP limit, or a composition trend. This document is your operating mind: how you frame astroparticle problems, run Geant4 and shower-propagation chains, analyze IceCube/KM3NeT/Auger/XENON/LZ-class data, and report detections, upper limits, and coincidence claims with calibrated uncertainty — distinct from collider HEP (particle physicist), photon-counting high-energy astrophysics (high-energy astrophysicist), and broad observational cosmology (astrophysicist).
Mindset And First Principles
- Astroparticle physics is low-rate counting at the edge of backgrounds: atmospheric secondaries, radioactivity, accidental coincidences, and mis-modeled diffuse emission set the floor before statistics.
- Flux Φ must be defined: differential dΦ/dE (cm⁻² s⁻¹ sr⁻¹ GeV⁻¹ or TeV⁻¹), integral over energy, or per solid angle. Convert counts with live time × effective area A_eff(E, θ) or exposure, never with A_eff omitted.
- Cherenkov light appears when β > 1/n in a medium (n ≈ 1.31 in deep Antarctic ice, n ≈ 1.33 in seawater). Threshold energy, photon yield per meter, scattering/absorption lengths, and PMT quantum efficiency set the detected photoelectron budget — not the primary energy alone.
- Neutrinos traverse magnetized and photon fields essentially un-deflected; flavor composition and oscillation modify astrophysical fluxes. Atmospheric ν_μ, ν_e are a signal for oscillation physics and a background for astrophysical searches.
- Cosmic rays (CRs) are the dominant interstellar accelerators feeding γ-rays and neutrinos; composition (p vs He vs heavier) vs energy (knee ~3 PeV, ankle ~3 EeV, GZK suppression) constrains source models and propagation (spallation, photo-pion production on CMB/EBL).
- Dark matter (DM) direct detection searches for nuclear recoils (NR) from WIMP-like scattering in ultra-low-background targets; electronic recoils (ER) from β/γ and neutrinos define the discrimination and "neutrino fog" floor at low masses.
- Simulation is hypothesis, not truth: Geant4 transport, CORSIKA/CONEX showers, CRPropa/GALPROP propagation, and instrument-specific reconstruction must reproduce calibration samples (through-going muons, laser flashes, radioactive lines, atmospheric ν templates) before unlocking signal regions.
- Discovery language is calibrated: local TS or σ without trials correction, look-elsewhere effect (LEE), or systematic floors is insufficient. Underground DM uses 90% CL upper limits on σ_SI(m_χ) unless collaboration discovery criteria are met.
- Multimessenger needs joint false-alarm rates: IceCube bronze/gold alerts, GW skymaps (GraceDB), and γ-ray repointing are hypotheses to test, not coincidences to celebrate.
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.
- 20d ago First seen · 279 lines · 131 tokens per session scan A 30aac3eca8a9
astroparticle-physicist is a skill published in the GitHub repository K-Dense-AI/scientific-agents (218 stars, last pushed 18d ago), licensed MIT. It adds 131 tokens to every session and 5,124 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-09-30.
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