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 agentlas-ai/agentlas-desktop --skill scikit-biogit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/scikit-bio)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/scikit-bio"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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/agentlas-ai/agentlas-desktop/scikit-bio"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/scikit-bio.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 5.5 | $0.00022 | $0.02009 |
| Sonnet 5.5 | $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 1mo 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.
This is a copy
100% identical to scikit-bio — 109 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 1mo ago First seen · 487 lines · 54 tokens per session scan A bc9a14071535
scikit-bio is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed today), licensed Apache-2.0. It adds 54 tokens to every session and 5,022 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to scikit-bio, differing in 109 lines, and is treated as a copy.
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