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 neurokit2git 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/neurokit2)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/neurokit2"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/neurokit2/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/neurokit2"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/neurokit2.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 | $0.00063 | $0.03503 |
| Opus 5 | $0.00032 | $0.01751 |
| Sonnet 5 | $0.00013 | $0.00701 |
| Haiku 4.5 | $0.00006 | $0.00350 |
Grade A, and why
neurokit2 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 9d 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 — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NeuroKit2
Scope and evidence cutoff
Use this skill for method-aware, reproducible biosignal research with NeuroKit2. The snapshot was checked on 2026-07-23 against:
- stable PyPI 0.2.13, released 2026-03-02;
- Python metadata (
>=3.10; classifiers 3.10–3.14) and wheel dependencies; - GitHub release notes/tags,
NEWS.rst, source at tagv0.2.13; - official API pages/examples (the live site identified itself as
0.2.13.dev214); and - pinned 0.2.13 runtime signatures and synthetic output schemas.
The live documentation can be ahead of the stable wheel. Prefer the pinned runtime for reproducible work and name both versions if consulting development docs.
Boundary
NeuroKit2 is a research and educational toolbox. Do not present its output as:
- a diagnosis, treatment recommendation, patient-monitoring decision, or alarm;
- validation, certification, or regulatory evidence for a medical device; or
- proof that a physiological construct is measured validly in a new sensor, protocol, environment, population, or disease group.
Validate acquisition hardware, electrode/optode placement, units, sampling and clock accuracy, preprocessing, detector/decomposition method, population, task, and outcomes for the intended study. Preserve raw data and an auditable exclusion log. Use deidentified local files only; do not place PHI in prompts, logs, examples, or bundled fixtures.
Reproducible installation
uv pip install "neurokit2==0.2.13"
For optional features, create a uv project, add only the packages actually required at
reviewed exact versions, and commit/review the resulting uv.lock before
uv sync --locked. NeuroKit2 exposes an upstream full extra, but this skill
intentionally does not install that floating transitive set in an automated workflow.
Optional capabilities can require MNE, cvxopt, Plotly, PyEMD, pyRQA, Pillow, OpenCV,
or file readers. Record the resolved environment with the analysis. Provision any MNE
data/template download as an explicit, checksummed study input. Do not install a moving
development branch for a reproducible study.
What ships with it
19 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.
- references/bio_module.md 7.5 KB
- references/complexity.md 7.3 KB
- references/ecg_cardiac.md 6.7 KB
- references/eda.md 6.8 KB
- references/eeg.md 7.1 KB
- references/emg.md 5.6 KB
- references/eog.md 5.2 KB
- references/epochs_events.md 6.9 KB
- references/hrv.md 8.2 KB
- references/ppg.md 6.7 KB
- references/rsp.md 6.7 KB
- references/signal_processing.md 6.7 KB
- scripts/_common.py 19 KB runs code
- scripts/ecg_hrv_pipeline.py 11 KB runs code
- scripts/eda_pipeline.py 9.8 KB runs code
- scripts/generate_synthetic.py 7.4 KB runs code
- scripts/inspect_signal.py 13 KB runs code
- scripts/plan_epochs.py 9.8 KB runs code
- scripts/validate_multimodal.py 12 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.
- 9d ago First seen · 341 lines · 63 tokens per session scan A 0d4307e60864
neurokit2 is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,469 stars, last pushed today), licensed MIT. It adds 63 tokens to every session and 3,503 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.
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 homology, context, and phylogenetic checks. Use when studying lateral gene transfer, virus-host gene exchange, endogenous viral elements, or donor direction.
polars-dovmed
Search PMC Open Access and bioRxiv corpora with polars-dovmed. Use when structured, reproducible literature queries should run through the hosted API or local parquet indexes.
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.
exploratory-data-analysis
Inspect scientific data and generate a Markdown structure-and-quality report. Use when triaging tabular, array, sequence, HDF5, JSON, or raster files before downstream analysis.