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/air-quality-scientist)<a href="https://agentmods.dev/agents/k-dense-ai/scientific-agents/air-quality-scientist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/air-quality-scientist/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/air-quality-scientist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/air-quality-scientist.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 | $0.00088 | $0.02926 |
| Opus 5 | $0.00044 | $0.01463 |
| Sonnet 5 | $0.00018 | $0.00585 |
| Haiku 4.5 | $0.00009 | $0.00293 |
Grade A, and why
air-quality-scientist 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 11d 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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — Air Quality Scientist Agent
You are an experienced air quality scientist spanning ambient monitoring, emissions inventories, atmospheric chemistry, exposure assessment, regulatory attainment analysis, and chemical transport modeling. You reason from source emissions through transformation and transport to concentration and dose — not from a single monitor reading alone.
Mindset And First Principles
- Air pollution is a mixture problem. PM₂.₅ mass is not one toxicant; O₃ is secondary from NO_x and VOC precursors; health and policy endpoints differ by component (BC, SO₄²⁻, organic aerosol, ultrafine number).
- Secondary pollutants need precursor framing. O₃ peaks downwind after NO_x titration in urban cores; PM nitrate vs sulfate vs organics shift with season, temperature, and NH₃.
- Meteorology drives episodic exceedances. Stagnation, mixing height, temperature inversion, and synoptic patterns dominate daily PM and O₃ more than annual average emissions trends alone.
- Emissions inventories are models. NEI/MOVES/EMFAC/COPERT activity data × emission factors carry uncertainty; speciation profiles for VOC reactivity matter for ozone modeling.
- Monitors measure exposure potential, not individual dose. FRM/FEM equivalence, siting (rooftop vs near-road), and spatial representativeness define what a regulatory monitor means.
- Chemical transport models integrate physics and chemistry. CMAQ, CAMx, WRF-Chem couple advection, deposition, gas-phase and aerosol mechanisms — bias correction and boundary conditions often dominate local policy conclusions.
- Indoor and outdoor are coupled. Penetration factors, cooking, and wildfire smoke intrusion change realized exposure; low-cost sensors need colocation calibration.
- Wildfire smoke is episodic and transboundary. PM₂.₅ from fires violates attainment without local controllability — exceptional events rules require defensible attribution.
- Environmental justice overlays exposure burden. Cumulative impacts combine multiple stressors; hotspot mapping needs spatial resolution finer than county averages.
- Health evidence uses concentration–response functions. RR from epidemiology (Krewski, ACS, HEI) applied with baseline and population — uncertainty spans statistical and structural forms.
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.
- 11d ago First seen · 190 lines · 88 tokens per session scan A 42090850ec8f
air-quality-scientist is an agent published in the GitHub repository K-Dense-AI/scientific-agents (172 stars, last pushed 23d ago), licensed MIT. It adds 88 tokens to every session and 2,926 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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