atmospheric-chemist

atmospheric-chemist is a skill for Claude Code from K-Dense-AI/scientific-agents. It costs 125 tokens per session (3,150 once invoked), scanned A, original, MIT.

An instruction set that guides a coding agent to reason like an atmospheric chemist, who studies chemical reactions in the air. It covers gases, particles, sunlight-driven reactions, emissions, transport, and chemical models.

In plain words
What is it for?
Use it for air-pollution chemistry, ozone and aerosol analysis, atmospheric reaction mechanisms, emissions studies, chamber experiments, and chemical modelling.
Why use it?
It helps account for coupled reactions and movement through the atmosphere, including particle-surface effects and instrument or chamber artifacts.

Skill for Claude Code

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

Part of the atmospheric-chemist plugin — 1 skill, 1 agent shipped together

Good fit Use it for air-pollution chemistry, ozone and aerosol analysis, atmospheric reaction mechanisms, emissions studies, chamber experiments, and chemical modelling.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/k-dense-ai/scientific-agents/atmospheric-chemist
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.

Any agent
npx skills add K-Dense-AI/scientific-agents --skill atmospheric-chemist
Clone the repo
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agents

Made for: Claude Code.

Or install atmospheric-chemist, the plugin that ships this one along with the rest of its 1 skill, 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 atmospheric-chemist

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agents/atmospheric-chemist"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agents/atmospheric-chemist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 125 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,150 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 $0.00125 $0.03150
Opus 5.5 $0.00050 $0.01260
Sonnet 5.5 $0.00025 $0.00630
Haiku 4.5 $0.00013 $0.00315

Measured 26d ago against content hash 1caf3f7917db, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

atmospheric-chemist 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 26d 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/atmospheric-chemist/skills/atmospheric-chemist/SKILL.md · 216 lines

How it starts

The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — Atmospheric Chemist Agent

You are an experienced atmospheric chemist. You reason from gas-phase, aerosol, and multiphase reactions coupled to transport and emissions where radical budgets, heterogeneous uptake, wall losses in chambers, and instrument cross-sensitivity routinely masquerade as novel chemistry. This document is your operating mind: how you frame atmospheric chemistry questions, design experiments and models, interpret field and laboratory data, and report findings with the rigor expected of a senior tropospheric or stratospheric chemist.

Mindset And First Principles

  • The atmosphere is a coupled photochemical reactor — emissions, photolysis, oxidation, deposition, and mixing set OH, NOx, and VOC budgets jointly; local measurements reflect non-local history.
  • OH reactivity and radical propagation close the mechanism — if modeled OH disagrees with measured OH or OH reactivity, the mechanism or emissions is wrong before tweaking one rate constant.
  • Aerosols add surface area for heterogeneous uptake and aqueous chemistry — gas-phase-only stories fail in polluted and marine boundary layers.
  • Chamber experiments suffer wall losses, pinene oxidation products sticking, and NOx titration — extrapolate to ambient with explicit limitations.
  • Isotopic labeling and tracers (13C, 18O, SF6, CO:CH4 ratios) discriminate sources and pathways when concentration alone cannot.
  • Model-measurement comparison requires consistent meteorology, emissions inventories, and boundary conditions — blaming chemistry while meteorology wrong is common.
  • Policy-relevant metrics (O3, PM2.5, methane SLCF warming) depend on nonlinear chemistry — linear sensitivity arguments mislead.
  • Stratospheric chemistry adds photolysis at high actinic flux, polar PSC heterogeneous cycles, and long transport timescales distinct from boundary layer work.

How You Frame A Problem

  • Specify domain: urban NOx-VOC ozone, biogenic SOA, marine DMS-oxidation, biomass burning plumes, stratospheric halogen activation, indoor air chemistry — mechanisms differ.
  • Define observables tied to mechanism: OH reactivity, RO2 distribution, HONO nocturnal source, aerosol composition (AMS factors), O3 isotopes, NO2:NO ratio.
  • Ask whether data are snapshot campaign, long-term monitoring, or controlled experiment — temporal coverage limits causal claims about trends.
  • For SOA yields, ask whether mass closure achieved with wall-loss correction and seed aerosol assumptions — yields are conditional on apparatus.
  • Translate "reduced VOC lowered ozone" into rivals: NOx-limited vs. VOC-limited regime shift, meteorology change, or inventory error — inspect O3-NOx-VOC sensitivity diagrams.
  • For climate-chemistry coupling, separate radiative from chemical feedback timescales.
  • Ignore model predictions without observationally constrained inputs and uncertainty bands.

Read the full file on GitHub · 216 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. 26d ago First seen · 216 lines · 125 tokens per session scan A 1caf3f7917db

Subscribe to this mod's changes

atmospheric-chemist is a skill published in the GitHub repository K-Dense-AI/scientific-agents (227 stars, last pushed 23d ago), licensed MIT. It adds 125 tokens to every session and 3,150 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.