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 scikit-biogit 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/scikit-bio)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/scikit-bio"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/scikit-bio/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/scikit-bio"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/scikit-bio.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Tool Misuse · line 315 Tool defaults are unsafe or overly permissive (e.g. disabled TLS verification, no authentication, world-writable permissions). Unsafe defaults widen the attack surface.Fix: Override unsafe defaults with secure settings (verify=True, auth required, restrictive permissions). Review and harden all tool configurations.
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 | $0.00054 | $0.05022 |
| Opus 5 | $0.00027 | $0.02511 |
| Sonnet 5 | $0.00011 | $0.01004 |
| Haiku 4.5 | $0.00005 | $0.00502 |
Grade A, and why
scikit-bio 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- scikit-bio — 92% identical, 20 lines differ
- scikit-bio — 88% identical, 154 lines differ
- scikit-bio — 88% identical, 154 lines differ
- scikit-bio — 86% identical, 151 lines differ
- scikit-bio — 86% identical, 150 lines differ
- scikit-bio — 86% identical, 151 lines differ
- scikit-bio — 84% identical, 152 lines differ
- scikit-bio — 83% identical, 151 lines differ
How it starts
The opening of the file, as written. The whole thing — 487 lines — stays where its author put it; the contents beside it link to each section on GitHub.
scikit-bio
Overview
scikit-bio is a comprehensive Python library for working with biological data. Apply this skill for bioinformatics analyses spanning sequence manipulation, alignment, phylogenetics, microbial ecology, and multivariate statistics.
When to Use This Skill
This skill should be used when the user:
- Works with biological sequences (DNA, RNA, protein)
- Needs to read/write biological file formats (FASTA, FASTQ, GenBank, Newick, BIOM, etc.)
- Performs sequence alignments or searches for motifs
- Constructs or analyzes phylogenetic trees
- Calculates diversity metrics (alpha/beta diversity, UniFrac distances)
- Performs ordination analysis (PCoA, CCA, RDA)
- Runs statistical tests on biological/ecological data (PERMANOVA, ANOSIM, Mantel)
- Analyzes microbiome or community ecology data
- Works with protein embeddings from language models
- Needs to manipulate biological data tables
Core Capabilities
1. Sequence Manipulation
Work with biological sequences using specialized classes for DNA, RNA, and protein data.
Key operations:
- Read/write sequences from FASTA, FASTQ, GenBank, EMBL formats
- Sequence slicing, concatenation, and searching
- Reverse complement, transcription (DNA→RNA), and translation (RNA→protein)
- Find motifs and patterns using regex
- Calculate distances (Hamming, k-mer based)
- Handle sequence quality scores and metadata
Common patterns:
import skbio
# Read sequences from file
seq = skbio.DNA.read('input.fasta')
# Sequence operations
rc = seq.reverse_complement()
rna = seq.transcribe()
protein = rna.translate()
# Find motifs
motif_positions = seq.find_with_regex('ATG[ACGT]{3}')
# Check for properties
has_degens = seq.has_degenerates()
seq_no_gaps = seq.degap()
Important notes:
- Use
DNA,RNA,Proteinclasses for grammared sequences with validation - Use
Sequenceclass for generic sequences without alphabet restrictions - Quality scores automatically loaded from FASTQ files into positional metadata
- Metadata types: sequence-level (ID, description), positional (per-base), interval (regions/features)
What ships with it
1 file 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.
- 8d ago First seen · 487 lines · 54 tokens per session scan A bc9a14071535
scikit-bio is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,220 stars, last pushed 4d ago), licensed MIT. It adds 54 tokens to every session and 5,022 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…
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.