Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
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-agent-skills --skill pkpd-modelinggit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skillsWrote 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-agent-skills/pkpd-modeling)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/pkpd-modeling"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pkpd-modeling/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-agent-skills/pkpd-modeling"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pkpd-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00273 | $0.04235 |
| Opus 5.5 | $0.00109 | $0.01694 |
| Sonnet 5.5 | $0.00055 | $0.00847 |
| Haiku 4.5 | $0.00027 | $0.00424 |
Grade A, and why
pkpd-modeling 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 25d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- pkpd-modeling — 100% identical, 442 lines differ
- pkpd-modeling — 94% identical, 449 lines differ
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.
Pharmacokinetic and Pharmacodynamic Modelling
When to use
Use for concentration-time analysis, structural and population PK workflows, exposure-response, regimen simulation, bioequivalence planning, DDI screening, and research TDM calculations. Version 2.0 rejects unsupported replicate/scaled BE and oral/infusion closed-form summaries; model-target dose and lowest-HED output labels replace clinical recommendation labels. This skill computes exploratory quantities and documents assumptions; it does not establish clinical safety, recommend a patient dose, or certify a regulatory submission.
Fix the question and conventions first
- Record the analyte (free/total drug, metabolite or complex), matrix, dose history, actual times, units, population, and assay/LLOQ. With dose in mg, volume in L and time in h, concentrations are mg/L and CL is L/h. Convert before fitting; mg/L equals micrograms/mL, not ng/mL.
- Pre-specify the exposure metric: AUC(0-t), AUC(0-inf), and steady-state AUC(0-tau) differ. State BLQ handling, missing-data rules, terminal selection and observed/predicted Clast.
- Separate structural, residual-variability and covariate models. Convergence and a small residual sum of squares do not establish identifiability. Inspect sensitivity rank, local uncertainty, correlations, plausible bounds, multiple starts and profile likelihoods when needed.
- Match validation to intended use. Analytical recovery and synthetic tests verify numerical behavior; they do not validate a model in a patient population or establish clinical evidence.
Environment and output
The bundled scripts were exercised with NumPy 2.5.3 and SciPy 1.18.1 on Python 3.13. Use an isolated environment; no network, credentials or proprietary engine is needed at runtime.
cd skills/pkpd-modeling/scripts
All scripts accept --format table|tsv|json. Table/TSV data go to stdout and notes/findings to
stderr; JSON includes all four components on stdout and uses null for unavailable diagnostics.
Exit 0 means no findings, 1 means findings, and 2 means invalid input. Dataset validation defaults
to failing on errors; --strict also fails on warnings. Inspect findings even when exit 0.
What ships with it
27 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/nca-reporting-checklist.md 3.5 KB
- assets/popk-analysis-plan.md 5.0 KB
- references/antimicrobial-and-tdm.md 3.1 KB
- references/bioequivalence.md 3.2 KB
- references/dataset-standards.md 3.3 KB
- references/ddi-and-qt.md 3.0 KB
- references/nca-conventions.md 3.2 KB
- references/pbpk.md 3.5 KB
- references/pd-and-exposure-response.md 3.2 KB
- references/population-pk.md 3.5 KB
- references/regulatory-guidance.md 3.7 KB
- references/software-ecosystem.md 3.9 KB
- references/source-ledger.md 4.2 KB
- references/special-populations.md 3.0 KB
- references/structural-models.md 4.2 KB
- references/tmdd-and-biologics.md 3.3 KB
- scripts/_common.py 12 KB runs code
- scripts/_models.py 27 KB runs code
- scripts/allometry_and_fih.py 16 KB runs code
- scripts/bioequivalence.py 18 KB runs code
- scripts/check_popk_dataset.py 18 KB runs code
- scripts/ddi_static.py 18 KB runs code
- scripts/exposure_response.py 16 KB runs code
- scripts/fit_compartmental.py 22 KB runs code
- scripts/nca.py 24 KB runs code
- scripts/simulate_regimen.py 17 KB runs code
- scripts/tdm_bayes.py 13 KB runs code
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.
- 25d ago Changed · -124 lines fc58aed600d0
- 26d ago Changed · +4 lines 0e074d056b9a
- 1mo ago First seen · 399 lines · 273 tokens per session scan A 5b555b3139dd
pkpd-modeling is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (54,777 stars, last pushed yesterday), licensed MIT. It adds 273 tokens to every session and 4,235 once invoked, about $0.0011 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 2026-09-03.
Other skills, from other repositories
discovery-toolbox
A routed repertoire of 90 scientific thinking operators for biological research agents - visual reasoning, detectability and information budgets, search reframing, causal identification, competing explanations, observation and selection processes, pipeline artifact diagnosis, effort allocation, and confirmation…
discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…
bio-interdomain-hgt
Detect and polarize interdomain horizontal gene transfer with reciprocal hits, genomic context, and gene trees. Use for virus-host gene exchange, endogenous viral elements, or donor direction.
scholion
Second-opinion support for individuals and clinicians, one person's data at a time — genome, laboratory history, prescriptions, wearables. It reads them locally and states what the data cannot support. No data is baked into the skill: everyone supplies their own. Use it when you need to check a physician's…
csag-extraction
Extract a Conditional Scientific Argumentation Graph and grounded Q&A from a manuscript. Use when representing assertions, contexts, evidence links, and inference steps in machine-readable form.
notebooks
Author, run and convert marimo or Jupyter notebooks. Use when delivering an analysis notebook executed end to end with figures embedded.