What is bio-comparative-genomics-ortholog-inference?

A bioinformatics workflow for finding orthologs: genes in different species that came from the same ancestral gene. It groups related genes into families using sequence comparisons and evolutionary trees.

Use it to build ortholog groups, find single-copy genes for evolutionary studies, and prepare gene sets for species-tree or gene-tree analysis.

It helps identify comparable genes across species even when genes were duplicated or lost during evolution.

bio-comparative-genomics-ortholog-inference

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo

git clone --depth 1 https://github.com/GPTomics/bioSkills

agentmods

npx agentmods add skills/gptomics/bioskills/ortholog-inference

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/gptomics/bioskills/ortholog-inference/github.svg)](https://agentmods.dev/skills/gptomics/bioskills/ortholog-inference)

Your own site

<a href="https://agentmods.dev/skills/gptomics/bioskills/ortholog-inference"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/ortholog-inference/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/gptomics/bioskills/ortholog-inference"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/ortholog-inference.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 168 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 9,118 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00168	$0.09118
Opus 5	$0.00084	$0.04559
Sonnet 5	$0.00034	$0.01824
Haiku 4.5	$0.00017	$0.00912

Measured 7d ago against content hash 16ef03d6ec6f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

bio-comparative-genomics-ortholog-inference scanned grade A with 1 finding 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.

The scan reads SKILL.md. This mod also ships 1 executable file (examples/ortholog_analysis.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

wget https://omabrowser.org/standalone/OMA.tgz && tar xf OMA.tgz && cd OMA && ./install.sh

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

bio-comparative-genomics-ortholog-inference — 100% identical, 12 lines differ

comparative-genomics/ortholog-inference/SKILL.md · 446 lines

How it starts

The opening of the file, as written. The whole thing — 446 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Version Compatibility

Reference examples tested with: OrthoFinder 3.0+ (Emms et al 2026 Nat Methods 23:1327), SonicParanoid 2.0.8+ (Cosentino 2024), Broccoli 1.2+ (Derelle 2020), ProteinOrtho 6.3.0+ (Lechner 2011 + recent), OMA standalone 2.6.0+, FastOMA 0.3.5+ (Majidian 2025), eggNOG-mapper 2.1.12+, JustOrthologs 2.0+, DIAMOND 2.1.10+, MMseqs2 17-b804f+, IQ-TREE 2.3.6+, BUSCO 5.7+, Compleasm 0.2.7+, BioPython 1.84+, R 4.4+ for downstream tree-based reconciliation.

Before using code patterns, verify installed versions match. If versions differ:

CLI: orthofinder --help; sonicparanoid --help; oma --help
Python: pip show eggnog-mapper; which fastoma

If code throws Diamond requires N more sequences than provided, KeyError on species tree taxa, STAG branch length 0, or HOG file format mismatch, the OrthoFinder v2 -> v3 file layout changed (Orthogroups/ -> Phylogenetic_Hierarchical_Orthogroups/; rooted gene trees are now per-HOG); update parsing accordingly.

Ortholog Inference

"Find the orthologs of my gene(s) across these species" -> Choose between graph-based (RBH / similarity-clustering: fast, lower recall) and tree-based (gene-tree reconciliation: higher accuracy, slower) frameworks; recognize that "orthology" splits into 1:1, 1:many, many:many, and the practical unit for most pipelines is the HOG (Hierarchical Orthologous Group) -- a maximal cluster of genes descended from a single ancestral gene at a defined taxonomic level (Altenhoff 2013 PLoS ONE 8:e53786). The "ortholog conjecture" (orthologs more functionally similar than paralogs) is supported but weakly (Altenhoff 2012 PLoS Comp Biol 8:e1002514); don't treat 1:1 ortholog labeling as automatic functional equivalence.

CLI: orthofinder -f proteomes/ -t 16 -M msa -- HOG output in v3 layout
CLI: sonicparanoid -i proteomes/ -o output --mode default -- ML predictor + protein language model
CLI: broccoli.py -dir proteomes/ -threads 16 -- direct OG with chimeric handling
CLI: oma standalone HOG inference at every taxonomic level
CLI: proteinortho6.pl --project=run proteomes/*.faa -- graph clustering with optional synteny
CLI: emapper.py -i proteins.faa --output project --cpu 16 -- eggNOG annotation transfer

Read the full file on GitHub · 446 lines

Files

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.

Changes

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 First seen · 446 lines · 168 tokens per session scan A 16ef03d6ec6f

Subscribe to this mod's changes

bio-comparative-genomics-ortholog-inference is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 26d ago), licensed MIT. It adds 168 tokens to every session and 9,118 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

Scientific

instrument-data-to-allotrope

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…

anthropics/knowledge-work-plugins · 123 tokens

matlab

Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 42 tokens

exploratory-data-analysis

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…

K-Dense-AI/scientific-agent-skills · 83 tokens

phylogenetics

Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.

K-Dense-AI/scientific-agent-skills · 68 tokens

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

mapping-to-snomed

Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…

maziyarpanahi/openmed · 205 tokens