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 etetoolkitgit 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/etetoolkit)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/etetoolkit"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/etetoolkit/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/etetoolkit"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/etetoolkit.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 | $0.00091 | $0.03096 |
| Opus 5 | $0.00046 | $0.01548 |
| Sonnet 5 | $0.00018 | $0.00619 |
| Haiku 4.5 | $0.00009 | $0.00310 |
Grade A, and why
etetoolkit 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 7d 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
1 near-identical copy found in the catalogue:
- etetoolkit — 100% identical, 19 lines differ
How it starts
The opening of the file, as written. The whole thing — 345 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ETE Toolkit 4
Scope
Use ETE 4 to work with an existing tree:
- Read Newick/Nexus, then inspect, annotate, transform, root, prune, and write Newick trees
- Compare topologies and calculate phylogenetic distances
- Find repeated subtree topologies with
TreePattern - Analyze gene trees with
PhyloTree - Query local NCBI or GTDB taxonomy databases
- Explore large trees interactively with SmartView
- Render PNG with SmartView or PNG/PDF/SVG with the optional Qt treeview
ETE does not replace sequence alignment or phylogenetic inference software. For raw sequences, first use MAFFT or another aligner and IQ-TREE 2, FastTree, or another inference tool; then load the resulting tree into ETE.
Current Target
This skill targets ETE 4.4.0, released September 3, 2025 and verified as the current PyPI release on July 23, 2026.
Use https://etetoolkit.github.io/ete/ for ETE 4 documentation. The
etetoolkit.org/docs/latest pages are legacy ETE 3 documentation despite the
URL name.
Do not silently translate these examples back to ETE 3:
- Package and import:
ete4, notete3 - File input: pass an open file object; use strings for Newick text and do not rely on path-string heuristics retained in ETE 4.4.0
- Newick selection:
parser=, notformat= - Node metadata:
props,add_prop(), andadd_props() - Iteration:
leaves(),descendants(), and related methods return iterators - Predicates:
node.is_leafandnode.is_rootare properties, not methods - Node lookup:
tree["name"], nottree & "name"
For porting older code, load
references/migration-ete3-to-ete4.md.
Installation
Install the pinned base package:
uv pip install "ete4==4.4.0"
Add only the visualization extra required by the workflow:
# SmartView static PNG screenshots
uv pip install "ete4[render-sm]==4.4.0"
# Legacy Qt renderer for PNG, PDF, and SVG
uv pip install "ete4[treeview]==4.4.0"
Confirm the active environment:
What ships with it
7 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.
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.
- 7d ago Changed · +17 lines ad1649e47ef9
- 11d ago First seen · 328 lines · 91 tokens per session scan A e9637685939b
etetoolkit is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,220 stars, last pushed 3d ago), licensed MIT. It adds 91 tokens to every session and 3,096 once invoked, about $0.0005 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.
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…
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.
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.
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.