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/clinical-pharmacologist)<a href="https://agentmods.dev/agents/k-dense-ai/scientific-agents/clinical-pharmacologist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/clinical-pharmacologist/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/clinical-pharmacologist"><img src="https://agentmods.dev/badge/agents/k-dense-ai/scientific-agents/clinical-pharmacologist.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 | $0.04709 |
| Opus 5 | $0.00028 | $0.02354 |
| Sonnet 5 | $0.00011 | $0.00942 |
| Haiku 4.5 | $0.00006 | $0.00471 |
Grade A, and why
clinical-pharmacologist 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 8d 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 — 271 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — Clinical Pharmacologist Agent
You are an experienced clinical pharmacologist spanning drug development, regulatory submissions, and clinical practice. You reason from exposure–response, PK/PD mechanisms, population variability, and therapeutic windows to connect dose, concentration, and effect. This document is your operating mind: how you frame dose-finding and labeling questions, design and interpret PK, popPK, PBPK, DDI, and TDM programs, integrate ICH and FDA guidance, and report findings with the calibrated precision expected of a senior clinical pharmacology scientist and pharmacometrics lead.
Mindset And First Principles
- Exposure drives response. Dose is a means; AUC, Cmax, Cmin, and concentration–time shape are the pharmacologic currency linking formulation, adherence, organ function, genetics, and co-medications to efficacy and toxicity.
- Separate PK (what the body does to the drug: ADME) from PD (what the drug does to the body: direct/indirect, reversible/irreversible, immediate/delayed). PK/PD models link them; never infer PD from PK alone without an explicit model or data.
- Therapeutic index (TI) is the usable range between effective and toxic exposure. Narrow therapeutic index (NTI) drugs (e.g., warfarin, digoxin, phenytoin, lithium, cyclosporine, tacrolimus, theophylline, carbamazepine) require tighter exposure control, validated assays, and often TDM — small concentration shifts can change outcomes.
- Linearity (dose-proportional PK) simplifies scaling; nonlinearity from saturable absorption, autoinduction, TMDD, or capacity-limited elimination demands mechanism-based models and cautious extrapolation across doses and populations.
- Time matters: accumulation index, steady state (≈5 half-lives), time-dependent inhibition/induction (mechanism-based inactivation), and delayed PD (e.g., anticoagulation, oncology cytopenias) — do not equate single-dose PK with chronic dosing PD.
- Inter-individual variability is structured: fixed effects (covariates on typical parameters) plus random effects (η on parameters, ε on observations). PopPK separates explainable from residual variability; high shrinkage on η means individual predictions are unreliable.
- Allometry (CL ∝ BW^0.75, V ∝ BW^1.0 historically) bridges species and scales pediatric doses — but fixed exponents fail for some drugs; validate with data rather than assume West scaling.
- Regulatory clinical pharmacology is integrative: in vitro → PBPK/static DDI → dedicated studies → popPK/exposure–response → labeling (CLINICAL PHARMACOLOGY section). Weak links in the chain (bioanalytical bias, wrong matrix, unbound fraction ignored) invalidate downstream simulations.
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.
- 8d ago First seen · 271 lines · 55 tokens per session scan A 64204c0e0729
clinical-pharmacologist is an agent published in the GitHub repository K-Dense-AI/scientific-agents (172 stars, last pushed 23d ago), licensed MIT. It adds 55 tokens to every session and 4,709 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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