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 phylogeneticsgit 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/phylogenetics)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/phylogenetics"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/phylogenetics/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/phylogenetics"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/phylogenetics.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 | $0.00071 | $0.02278 |
| Opus 5.5 | $0.00028 | $0.00911 |
| Sonnet 5.5 | $0.00014 | $0.00456 |
| Haiku 4.5 | $0.00007 | $0.00228 |
Grade A, and why
phylogenetics 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 24d 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
8 near-identical copies found in the catalogue:
- phylogenetics — 100% identical, 488 lines differ
- phylogenetics — 100% identical, 492 lines differ
- phylogenetics — 100% identical, 490 lines differ
- phylogenetics — 100% identical, 490 lines differ
- phylogenetics — 98% identical, 489 lines differ
- phylogenetics — 98% identical, 489 lines differ
- phylogenetics — 98% identical, 489 lines differ
- phylogenetics — 98% identical, 489 lines differ
How it starts
The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Phylogenetics
When to use
Use this workflow for an alignment of homologous loci or proteins, a gene tree, branch support, and an interpretable tree figure. Multi-locus partitioning, concordance factors, ancestral states and dating are covered in IQ-TREE reference.
A gene tree does not establish a species tree, transmission direction, or horizontal transfer by itself. Sampling dates alone do not justify a molecular clock.
Versions and installation
Reviewed against current official documentation on 2026-10-01. The executed
smoke workflow used MAFFT 7.526, IQ-TREE 3.1.4, FastTree 2.2.0 and
ETE3 3.1.3 on Python 3.12. ETE3 remains deliberate for this helper; ETE4 is a
separate API and is not a drop-in replacement. ETE3 imports cgi, removed in
Python 3.13. The native executables are not Python packages.
Illustrative installation commands; verify executable versions after installation:
conda create -n phylo -c conda-forge -c bioconda mafft iqtree fasttree trimal
conda activate phylo
uv venv --python 3.12 .venv-phylo
uv pip install --python .venv-phylo/bin/python ete3==3.1.3 numpy six
# Optional rendering backend; it can require a working Qt platform plugin.
uv pip install --python .venv-phylo/bin/python PyQt5
The helper defaults to iqtree3 and FastTree; use --iqtree-bin iqtree2 for an
existing IQ-TREE 2 installation, or --fasttree-bin fasttree if that is the
installed name. Version 2 compatibility is source-checked, not runtime-tested
in this refresh. All analyses are local; this skill invokes no HTTP API or remote
inference endpoint.
Workflow
1. Establish the biological input
- Select homologs with appropriate taxon coverage; investigate paralogs, contamination and recombination before assuming one history describes all sites.
- Use unique, whitespace-free FASTA IDs and keep a separate sample metadata table. The helper rejects empty/all-missing records and Newick punctuation in IDs.
- Specify
--type ntor--type aa. The helper sends-st DNAor-st AAto IQ-TREE and selects nucleotide/protein FastTree flags explicitly. - Coding DNA needs codon-aware alignment if using a codon model. Ordinary MAFFT nucleotide alignment does not preserve reading frames automatically.
- Keep the original sequences, accession versions, filters, alignment and exact commands. Inspect alignment lengths, gaps, composition and taxon identities.
What ships with it
2 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.
- 24d ago Changed · -232 lines · +3 tokens per session eef2354a8fe6
- 25d ago Changed ef3c739ccdac
- 1mo ago First seen · 410 lines · 68 tokens per session scan A f05162b902c3
phylogenetics is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (54,587 stars, last pushed 7d ago), licensed MIT. It adds 71 tokens to every session and 2,278 once invoked, about $0.0003 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.
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.
bio-annotation
Annotate proteins with functions, domains, and homology-based taxonomy. Use when assigning functional or taxonomic labels to proteins from genomes, contigs, or MAGs.