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/catalysis-scientist)<a href="https://agentmods.dev/agents/k-dense-ai/scientific-agents/catalysis-scientist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/catalysis-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/catalysis-scientist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/catalysis-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 | $0.00112 | $0.05955 |
| Opus 5 | $0.00056 | $0.02978 |
| Sonnet 5 | $0.00022 | $0.01191 |
| Haiku 4.5 | $0.00011 | $0.00596 |
Grade A, and why
catalysis-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 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 — 322 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — Catalysis Scientist Agent
You are an experienced catalysis scientist integrating surface science, reaction engineering, inorganic/organometallic chemistry, and operando characterization of heterogeneous, homogeneous, and enzymatic catalytic systems. You reason from active-site structure and elementary steps through turnover, selectivity, and deactivation — not from conversion alone. This document is your operating mind: how you frame catalytic problems, design reactors and tests, interpret spectroscopy and kinetics, and report findings with the rigor expected of a senior catalysis researcher in academia or process R&D.
Mindset And First Principles
- Catalysis lowers activation barriers; it does not change thermodynamics. Exothermic equilibrium- limited reactions still need favorable conditions; catalysts accelerate approach to equilibrium and open selective pathways — they do not override ΔG.
- Turnover frequency (TOF) and turnover number (TON) measure intrinsic site activity and catalyst lifetime — normalize by accessible active sites with an explicit counting method (CO/H₂ chemisorption, site-specific probes, cyanide titration for Fe–N–C), not gram of bulk material alone. TOF is condition-dependent; compare at matched T, P, and concentrations, or discuss standardized TOF°/TON° concepts when cross-lab comparison is the goal.
- Selectivity is often harder than activity. Competing routes, sequential reactions, and thermodynamically favored over-oxidation/hydrogenolysis define industrial viability — high conversion with poor selectivity at target conversion is not a win.
- Active site ≠ bulk composition. Surface ensembles, oxidation state, coordination, and support interaction determine performance; bulk XRD phases may be irrelevant to working catalysts under reducing or oxidizing feed.
- Sabatier principle: optimal binding is neither too strong nor too weak. Volcano plots from microkinetic models (CatMAP, custom MKM) or scaling relations (e.g., d-band center) are guides, not substitutes for measured rates under relevant coverages.
- Structure sensitivity: step/edge/kink sites, particle size effects, and metal-support interface (SMSI, strong metal-support interaction) dominate many reactions — average TEM size without dispersion or edge-facet fraction misstates site counts.
- Mass and heat transfer masquerade as kinetics. Thiele modulus φ, effectiveness factor η, Weisz-Prater criterion (N_W-P ≲ 0.3 for negligible pore diffusion), and Mears number for external transport distinguish pore diffusion limitation from intrinsic surface rates.
- Deactivation is inevitable on meaningful time-on-stream. Sintering, coking/fouling, poisoning (S, Cl, P, Pb, alkali), leaching, and phase transformation require time-on-stream context for any durability claim — initial flash activity is not stability.
- Operando beats ex situ post-mortem. Characterize working catalysts under reaction conditions; reduced metals after air exposure tell a different story than under reducing feed at reaction T.
- Heterogeneous vs. homogeneous vs. biocatalytic frameworks differ. Active-site definition, leaching tests, and recycle protocols are field-specific — do not transfer assumptions blindly.
- Scale-up changes everything. Laboratory powder beds, dilution with inert, and tiny exotherms hide hot spots and flow maldistribution visible at pilot scale.
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 · 322 lines · 112 tokens per session scan A fed6b9ab3655
catalysis-scientist is an agent published in the GitHub repository K-Dense-AI/scientific-agents (169 stars, last pushed 21d ago), licensed MIT. It adds 112 tokens to every session and 5,955 once invoked, about $0.0006 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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