Agent skill

Bio Phylo Species Trees

by GPTomics in GPTomics/bioSkills

Estimates species trees under the multispecies coalescent from per-locus gene trees with the modern ASTER astral binary (ASTRAL-III/wASTRAL/ASTRAL-Pro), plus SVDQuartets, BPP, and StarBEAST2.

MITAuto-check passedResearch & Science

Install Bio Phylo Species Trees

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-phylo-species-trees -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-phylo-species-trees --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/phylogenetics/species-trees .claude/skills/bio-phylo-species-trees && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bio-phylo-species-trees
GitHub stars
1.2k
Used in
1 other repo
Token cost
~5.7k tokens
SKILL.md length
2,704 words
Files
3
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Estimates species trees under the multispecies coalescent from per-locus gene trees with the modern ASTER astral binary (ASTRAL-III/wASTRAL/ASTRAL-Pro), plus SVDQuartets, BPP, and StarBEAST2.

  • Works in 3 steps: The most probable gene tree can be the… → Concatenation is statistically… → The honest question is the distribution,…
  • Multi-locus discordance
  • SKILL.md covers Version Compatibility, The Single Most Important…, When Concatenation Fails and Method Taxonomy, plus 9 more sections
  • Runs Shell scripts from its folder

What it does

Bio Phylo Species Trees is an agent skill from GPTomics/bioSkills. Estimates species trees under the multispecies coalescent from per-locus gene trees with the modern ASTER astral binary (ASTRAL-III/wASTRAL/ASTRAL-Pro), plus SVDQuartets, BPP, and StarBEAST2. Covers why a species tree is not a gene tree, why each locus has its own genealogy that disagrees by incomplete lineage sorting (ILS) even with zero error, why concatenation is statistically inconsistent and positively misleading in the anomaly zone where more loci converge on the wrong tree with full support, why gene-tree…

Its SKILL.md is about 5.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/astral_pipeline.sh` and `usage-guide.md`).

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.

When your agent uses it

  • Multi-locus discordance
  • Rapid radiations
  • Anomaly-zone risk
  • Concordance-factor interpretation

Example prompts

  • “Use the bio-phylo-species-trees skill to estimate species trees under the multispecies coalescent from per-locus gene trees with the modern ASTER…”
  • “/bio-phylo-species-trees”

Requirements

  • A Bash shell

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. The most probable gene tree can be the wrong species tree. When two short speciation events occur in quick succession, the single most…
  2. Concatenation is statistically inconsistent and positively misleading under ILS. It pretends all loci share one tree and behaves like a…
  3. The honest question is the distribution, not the tree. Coalescent summary methods (ASTRAL family) stay consistent because they ask "what…

What it can do on your machine

Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Shell), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bio Phylo Species Trees loads about 5.7k tokens when it runs. Until then it costs about 259 tokens; SKILL.md has 2,704 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~259
When it runs · the whole SKILL.md, loaded when a task matches
~5.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 2,704 words, ~5,659 tokens.

Download SKILL.mdSave it as .claude/skills/bio-phylo-species-trees/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
bio-phylo-species-trees
description
Estimates species trees under the multispecies coalescent from per-locus gene trees with the modern ASTER astral binary (ASTRAL-III/wASTRAL/ASTRAL-Pro), plus SVDQuartets, BPP, and StarBEAST2. Covers why a species tree is not a gene tree, why each locus has its own genealogy that disagrees by incomplete lineage sorting (ILS) even with zero error, why concatenation is statistically inconsistent and positively misleading in the anomaly zone where more loci converge on the wrong tree with full support, why gene-tree estimation error biases summary methods, that localPP is not bootstrap and ASTRAL branch lengths are coalescent units, and how minority-quartet symmetry separates ILS from introgression. Use when multi-locus discordance, rapid radiations, anomaly-zone risk, concordance-factor interpretation, or concatenation-vs-coalescent choice arise. Routes per-locus gene-tree inference and gCF/sCF to modern-tree-inference, dating to divergence-dating, orthology to comparative-genomics/ortholog-inference.
tool_type
mixed
primary_tool
ASTRAL-III

Version Compatibility

Reference examples tested with: ASTER 1.15+ (provides astral/ASTRAL-III, wastral, astral-pro), IQ-TREE 2.2+ (concordance factors), PAUP* 4.0a168+ (SVDQuartets), BPP 4+.

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

  • CLI: astral --version then astral --help to confirm flags
  • CLI: iqtree2 --version then iqtree2 --help to confirm flags
  • CLI: PAUP* prints its version at startup; BPP reads a control file

If code throws an error, introspect the installed version and adapt flags rather than retrying.

The modern binary is astral from the ASTER package; input is a file of gene-tree Newick strings, one tree per line. The classic-Java ASTRAL uses -t for ANNOTATION level, but the ASTER astral uses -t for THREADS and -u for annotation -- copying a Java -t 8 (quartet support) into ASTER silently requests 8 threads with no annotation. Confirm which interface is installed before scripting.

Coalescent Species-Tree Estimation -- A Species Tree Is Not a Gene Tree, and Concatenation Is Inconsistent in the Anomaly Zone

"Estimate the species tree from my multi-locus phylogenomic data" -> Treat the dataset as a distribution of genealogies, not one tree, and estimate the species tree most consistent with that distribution under the multispecies coalescent.

  • CLI: per-locus gene trees (modern-tree-inference) then astral -i gene_trees.nwk -o species.tre (ASTER)
  • CLI: gene and site concordance factors via iqtree2 ... --gcf ... --scfl (shared with modern-tree-inference)

Scope: choosing and running a coalescent species-tree method, contracting noisy gene trees, and reading discordance honestly. Per-locus gene-tree inference and model selection -> modern-tree-inference. Computing and plotting gCF/sCF -> modern-tree-inference, tree-visualization. Single-copy vs multi-copy orthology upstream -> comparative-genomics/ortholog-inference. Dated species trees -> divergence-dating. Rooting -> tree-manipulation.

The Single Most Important Modern Insight

A species tree is not a gene tree. Each locus has its own genealogy, and under the multispecies coalescent (MSC) those genealogies disagree with the species tree -- and with each other -- by incomplete lineage sorting (ILS) alone, with zero estimation error. The disagreement is not noise to average away; it is signal generated by the coalescent running inside the species tree. Three load-bearing facts:

  1. The most probable gene tree can be the wrong species tree. When two short speciation events occur in quick succession, the single most common gene-tree topology is not the species tree -- this is the anomaly zone (Degnan and Rosenberg 2006), and such gene trees are anomalous gene trees. A plurality vote across loci then converges on the wrong tree.
  2. Concatenation is statistically inconsistent and positively misleading under ILS. It pretends all loci share one tree and behaves like a vote dominated by the most-supported site pattern; in the anomaly zone that pattern reflects the anomalous genealogy, so adding loci raises bootstrap support for the wrong topology toward 100% (Kubatko and Degnan 2007; Roch and Steel 2015). More data makes the error more confident, not less. The support is the trap.
  3. The honest question is the distribution, not the tree. Coalescent summary methods (ASTRAL family) stay consistent because they ask "what species tree is most consistent with having generated this gene-tree distribution under the MSC?" -- and they work at the quartet level, where the matching topology is always the plurality and no anomaly zone exists. Concatenation answers a different, wrong question, and both outputs look identically resolved and supported.

When Concatenation Fails

Concatenation is consistent and efficient only when ILS is low. It is actively wrong in three coupled regimes, all driven by short internodes measured in coalescent units (generations / 2Ne):

  1. Short internodes plus large ancestral Ne. A short branch gives lineages little time to coalesce before the next deeper population, so many survive and sort randomly. For a rooted triple, P(gene tree matches) = 1 - (2/3)e^(-tau) in coalescent units tau: at tau = 1 about 25% of gene trees are discordant, at tau = 0.3 about 50%, at tau = 0.1 about 60%. Large Ne shortens tau for the same number of years, inflating ILS.
  2. The anomaly zone (Degnan and Rosenberg 2006). With two or more short successive internal branches (both well under ~1, especially under ~0.1-0.2 coalescent units), the most probable gene tree differs from the species tree. The zone grows with taxon number and with shorter, more clustered internodes.
  3. Rapid radiations. Successive speciation in quick succession is built from exactly those short internodes, so radiations routinely show a majority of gene trees conflicting with the species tree. This is the canonical case for a coalescent method.

Method Taxonomy

MethodCitationType / roleWhen
ASTRAL-III (astral)Zhang 2018Summary, quartet-based; consistent under MSC; localPP, polytomy test, q1/q2/q3Default genome-scale species tree from single-copy gene trees
wASTRAL (wastral/astral-hybrid)Zhang and Mirarab 2022Summary, weighted quartets; down-weights weak gene-tree branchesCurrent best-accuracy summary method for real (noisy) gene trees
ASTRAL-Pro (astral-pro)Zhang 2020Summary, quartet-based on MULTI-COPY gene-family treesParalog-rich families; uses families without pre-orthology
SVDQuartetsChifman and Kubatko 2014Site-based quartets from site patterns; NO gene treesShort loci (RADseq/UCE/SNP); when gene trees are unreliable
BPPFlouri 2018Full-likelihood Bayesian MSC; integrates gene-tree uncertaintySmall high-stakes datasets; species delimitation; introgression (MSci)
StarBEAST2Ogilvie 2017Full Bayesian co-estimation of species tree, gene trees, datesDated species tree with full posterior uncertainty
Concatenation (IQ-TREE/RAxML)--Supermatrix ML; assumes ONE treeLow ILS / high gCF only; never in or near the anomaly zone

Summary methods take estimated gene trees as input and assume they are true -- the central caveat (see failure modes). Site-based (SVDQuartets) and full-likelihood (BPP, StarBEAST2) skip that assumption but pay in scale: roughly tens of taxa for the full-likelihood methods. The speed/scale order (most scalable first) is concatenation < ASTRAL/wASTRAL < SVDQuartets < StarBEAST2 < BPP; model completeness runs the opposite way. Pick the most scalable method whose assumptions the data do not badly violate. Introgression detection (HyDe, Blischak 2018, Syst Biol; QuIBL, Edelman 2019, Science; the D-statistic) lives downstream of the symmetry diagnostic below.

Concatenation vs Coalescent Decision

Situation / diagnosticRecommended
Low ILS: long internodes (>>1 coalescent unit), gCF high (>~70), few discordant gene treesConcatenation fine; coalescent agrees and adds no advantage
Moderate ILS: gCF ~50-70, some short branchesRun wASTRAL; check gCF/sCF and the polytomy test on short branches
High ILS / rapid radiation: short successive internodes, gCF <50 (esp. <33), q2 ~ q3Coalescent REQUIRED; concatenation is anomaly-zone wrong; quartet consistency holds
Low per-locus signal / unreliable gene treeswASTRAL or contract gene-tree branches <10% support; or SVDQuartets (no gene trees); BPP integrates uncertainty
Paralog-rich gene familiesASTRAL-Pro (no pre-orthology); concatenation cannot use them
Need dated tree or delimitationStarBEAST2 (dating) or BPP (delimitation); coalescent-unit lengths are not time
Suspected introgression (q2 != q3, D significant)Model the reticulation (HyDe/QuIBL/BPP-MSci/PhyloNet); a tree masks it

Do not reflexively coalescent-everything: where ILS is low the two trees agree and concatenation pools weak per-locus signal robustly. The deliverable is concordance, not a single best method -- run both, compute gCF/sCF, and trust the coalescent tree exactly where the two disagree on low-gCF branches.

Estimate the Species Tree from Gene Trees

Goal: Turn per-locus gene trees into a species tree that is consistent under the MSC, after removing gene-tree noise that would bias the quartet counts.

Approach: Infer one gene tree per locus with branch support (modern-tree-inference), contract branches below ~10% support to polytomies (which ASTRAL handles correctly), then run wASTRAL as the primary estimate and astral for the classic localPP / polytomy-test workflow.

bash
# Per-locus gene trees with support live upstream in modern-tree-inference:
#   iqtree2 -S loci_dir -m MFP -B 1000 -T AUTO --prefix loci   # one tree per locus
# Contract gene-tree branches below 10% support (Newick tools / nw_ed) -> polytomies,
# then concatenate into one file, one Newick per line:
#   cat loci/*.contracted.treefile > gene_trees.nwk

# Primary estimate: wASTRAL weights quartets by gene-tree support+length (noisy gene trees)
wastral -i gene_trees.nwk -o species_wastral.tre 2> species_wastral.log

# Classic ASTRAL-III for localPP + polytomy test. In ASTER, -t is THREADS, -u is annotation:
astral -t 8 -i gene_trees.nwk -o species.tre 2> species.log     # -t 8 = 8 threads here
astral -u 2 -i gene_trees.nwk -o species_annot.tre              # full localPP + q1/q2/q3 for all 3 resolutions
astral --root OUTGROUP -i gene_trees.nwk -o species_rooted.tre  # rooting improves branch-length estimation

# Multi-copy gene families (no pre-orthology) -> ASTRAL-Pro:
astral-pro -i family_trees.nwk -o species_pro.tre

ASTRAL returns an UNROOTED tree; root with an outgroup afterward. Branch lengths are in coalescent units (not time, not substitutions), and tip lengths are undefined in that mode. The classic Java astral.5.7.8.jar inverts the flags: there -t is the annotation level (-t 2 full, -t 8 quartet support, -t 10 polytomy test) and there is no -u.

Compute and Read Concordance Factors

Goal: Replace a single bootstrap/localPP per branch with how much of the actual data agrees, and read ILS-vs-introgression off the quartet symmetry.

Approach: Compute gene and site concordance factors against the species tree, then inspect the two minority quartet frequencies (q2, q3) at each contested branch.

bash
# gCF = % of decisive gene trees containing a branch; sCF = % of decisive sites supporting it
iqtree2 -te species.tre --gcf gene_trees.nwk -s concat.fasta --scfl 100 --prefix cf   # -te fixes the topology for likelihood sCF
# cf.cf.tree carries gCF/sCF on node labels; cf.cf.stat has per-branch q1/q2/q3 counts

Then read each contested branch: under pure ILS the two minority topologies are equally probable (q2 ~ q3); introgression breaks that symmetry by inflating the one matching the direction of gene flow. A high-bootstrap branch with gCF ~ 25 is a branch where the data are screaming disagreement that the bootstrap hides.

Interpreting Concordance and Discordance

  • gCF / sCF (Minh 2020) are the honest support. Bootstrap and localPP saturate near their ceiling at genome scale even when most loci disagree; gCF/sCF report the actual conflict. Report them on every phylogenomic tree.
  • localPP (Sayyari and Mirarab 2016) is NOT a bootstrap. It is a coalescent posterior computed from the quartet frequencies around a branch. localPP = 1.0 is common and expected on resolved branches and does not mean "100 of 100 bootstraps"; localPP ~ 0.33 means the three quartet resolutions are tied (no resolution).
  • The polytomy test (Sayyari and Mirarab 2018) is the principled alternative to staring at low support. It tests the null q1 = q2 = q3 (an effectively simultaneous speciation); failing to reject means a resolved bifurcation cannot be claimed -- collapse the branch.
  • Quartet symmetry separates ILS from introgression. Symmetric minority quartets (q2 ~ q3) = ILS; handle with a coalescent tree. Asymmetric (q2 != q3, significant D-statistic / ABBA-BABA, HyDe gamma != 0, or QuIBL favoring the introgression component) = gene flow; a bifurcating tree cannot represent it -- model the reticulation. Confirm the asymmetry before invoking introgression.

Per-Method Failure Modes

Gene-Tree Estimation Error Biasing ASTRAL

Trigger: Short, low-information loci (e.g. <500 bp exons) fed to ASTRAL/wASTRAL as if their gene trees were truth. Mechanism: MSC consistency assumes the input gene trees are correct; estimation error is a non-coalescent noise source that biases quartet counts in finite samples and can make the coalescent tree worse than concatenation. Symptom: Coalescent tree noisier or worse than concatenation; unstable across locus subsets; low localPP everywhere; gCF far below sCF. Fix: Contract gene-tree branches below ~10% bootstrap before ASTRAL (they become polytomies that contribute no spurious quartet similarity); better, use wASTRAL (continuous weighting); or drop to SVDQuartets (no gene trees) or BPP (integrates gene-tree uncertainty).

Show full SKILL.md (1,061 more words)Show less
Treating localPP as Bootstrap Support

Trigger: Reporting ASTRAL branch labels as bootstrap proportions, or demanding ">95% bootstrap" of an ASTRAL tree. Mechanism: localPP is a coalescent posterior from quartet frequencies, on a different scale; it saturates at 1.0 on resolved branches. Symptom: Over-confident reading of localPP = 1.0 as "100/100 BS"; ignoring that localPP ~ 0.33 is a near-tie. Fix: Interpret localPP on its own scale; report gCF/sCF for honest conflict; use the polytomy test to decide whether to collapse a branch.

Concatenating In or Near the Anomaly Zone

Trigger: Rapid radiation with several short successive internodes, analyzed by supermatrix ML with "100% bootstrap" reported as resolution. Mechanism: Concatenation is inconsistent and positively misleading under high ILS; the plurality genealogy is anomalous, so adding loci converges on the wrong tree with rising support. Symptom: Rock-solid bootstrap but gCF < 33 on focal branches; concatenated and coalescent trees disagree exactly there. Fix: Use wASTRAL/ASTRAL (quartet-level consistency holds in the anomaly zone); report gCF/sCF; trust the coalescent topology on low-gCF branches; consider a true near-polytomy via the polytomy test.

Ignoring Paralogy

Trigger: Discarding all multi-copy families to feed single-copy ASTRAL, or treating paralogs as orthologs. Mechanism: Standard ASTRAL assumes single-copy gene trees; mis-assigned paralogs inject quartets reflecting the duplication history, not speciation. Symptom: Massive data loss (most families dropped), or spurious clades / inflated discordance from hidden paralogy. Fix: Use ASTRAL-Pro on the multi-copy gene-family trees (it models orthology/paralogy without pre-orthology); audit orthology upstream (comparative-genomics/ortholog-inference).

Mistaking Introgression for ILS (or Vice Versa)

Trigger: Assuming all discordance is ILS and fitting a tree, OR over-calling gene flow from any discordance. Mechanism: ILS produces symmetric minority topologies (q2 ~ q3); introgression breaks the symmetry. A bifurcating tree cannot represent reticulate history. Symptom: Asymmetric q2/q3 on a discordant branch; significant D-statistic / HyDe gamma; QuIBL favoring the introgression component -- but a tree-only analysis hides it. Fix: Test minority-quartet symmetry (D-statistic / ABBA-BABA, HyDe, QuIBL, ASTRAL q1/q2/q3) before interpreting; if asymmetric, model the reticulation rather than forcing a tree.

Quantitative Thresholds

QuantityThresholdMeaning / actionSource
Internode (coalescent units)< ~1.0>25% of gene trees discordant; ILS materialP(concordant)=1-(2/3)e^(-tau)
Internode (coalescent units)< ~0.3~50% discordant; coalescent method requiredsame
Two+ successive internodesboth < ~0.1-0.2Anomaly-zone risk; concatenation positively misleadingDegnan and Rosenberg 2006
Gene-tree branch support to contract before ASTRAL< ~10% bootstrap (or use wASTRAL)Collapse to polytomy to cut gene-tree-error biasASTRAL practice / Zhang and Mirarab 2022
gCF< 50Most loci disagree; distrust concatenation hereMinh 2020
gCF< 33Near the ILS coin-flip floor; effectively unresolvedMinh 2020
sCF~33Random-resolution floor (three quartet resolutions); no site signalMinh 2020
localPP~0.95+ to call "well-supported"Strong coalescent support, NOT a bootstrapSayyari and Mirarab 2016
localPP~0.33Three quartet resolutions tied; candidate polytomySayyari and Mirarab 2016
Polytomy test pfail to reject (e.g. p > 0.05)Cannot claim a resolved bifurcation; collapseSayyari and Mirarab 2018

The gCF/sCF cutoffs of 50/33 are practical heuristics relative to the dataset (a branch at gCF 45 amid branches at 90 is the weak one), not significance tests. The 10% contraction is a widely-used default, not a derived optimum; wASTRAL removes the need to pick it.

Common Errors

Error / symptomCauseSolution
ASTER astral ignores the annotation flagUsed Java -t 8 (annotation); ASTER -t is threadsUse -u 2 in ASTER; -t 8 only in classic Java
Coalescent tree worse than concatenationNoisy gene trees fed in as truthContract <10% branches; use wASTRAL; or SVDQuartets
localPP = 1.0 reported as bootstraplocalPP is a coalescent posterior on its own scaleReport gCF/sCF; do not apply a BS cutoff
Coalescent-unit branch read as timeBranch lengths are coalescent units, tips undefinedState units; for dates use StarBEAST2 / divergence-dating
High bootstrap, low gCF, called resolvedConcatenation in the anomaly zoneRun coalescent; trust it on low-gCF branches; polytomy test
Most gene families droppedForced single-copy orthologyUse ASTRAL-Pro on multi-copy family trees
Network claimed from any discordanceILS also produces discordanceConfirm q2 != q3 (D / HyDe / QuIBL) before invoking gene flow

References

Degnan JH, Rosenberg NA. 2006. Discordance of species trees with their most likely gene trees. PLoS Genetics 2(5):e68. Degnan JH, Rosenberg NA. 2009. Gene tree discordance, phylogenetic inference and the multispecies coalescent. Trends in Ecology & Evolution 24(6):332-340. Kubatko LS, Degnan JH. 2007. Inconsistency of phylogenetic estimates from concatenated data under coalescence. Systematic Biology 56(1):17-24. Roch S, Steel M. 2015. Likelihood-based tree reconstruction on a concatenation of aligned sequence data sets can be statistically inconsistent. Theoretical Population Biology 100:56-62. Mirarab S, Reaz R, Bayzid MS, Zimmermann T, Swenson MS, Warnow T. 2014. ASTRAL: genome-scale coalescent-based species tree estimation. Bioinformatics 30(17):i541-i548. Zhang C, Rabiee M, Sayyari E, Mirarab S. 2018. ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees. BMC Bioinformatics 19(Suppl 6):153. Zhang C, Mirarab S. 2022. Weighting by gene tree uncertainty improves accuracy of quartet-based species trees. Molecular Biology and Evolution 39(12):msac215. Zhang C, Scornavacca C, Molloy EK, Mirarab S. 2020. ASTRAL-Pro: quartet-based species-tree inference despite paralogy. Molecular Biology and Evolution 37(11):3292-3307. Zhang C, Nielsen R, Mirarab S. 2025. ASTER: a package for large-scale phylogenomic reconstructions. Molecular Biology and Evolution 42(8):msaf172. Sayyari E, Mirarab S. 2016. Fast coalescent-based computation of local branch support from quartet frequencies. Molecular Biology and Evolution 33(7):1654-1668. Sayyari E, Mirarab S. 2018. Testing for polytomies in phylogenetic species trees using quartet frequencies. Genes 9(3):132. Chifman J, Kubatko L. 2014. Quartet inference from SNP data under the coalescent model. Bioinformatics 30(23):3317-3324. Flouri T, Jiao X, Rannala B, Yang Z. 2018. Species tree inference with BPP using genomic sequences and the multispecies coalescent. Molecular Biology and Evolution 35(10):2585-2593. Ogilvie HA, Bouckaert RR, Drummond AJ. 2017. StarBEAST2 brings faster species tree inference and accurate estimates of substitution rates. Molecular Biology and Evolution 34(8):2101-2114. Minh BQ, Hahn MW, Lanfear R. 2020. New methods to calculate concordance factors for phylogenomic datasets. Molecular Biology and Evolution 37(9):2727-2733. Blischak PD, Chifman J, Wolfe AD, Kubatko LS. 2018. HyDe: a Python package for genome-scale hybridization detection. Systematic Biology 67(5):821-829. Edelman NB, Frandsen PB, Miyagi M, et al. 2019. Genomic architecture and introgression shape a butterfly radiation. Science 366(6465):594-599.

  • modern-tree-inference - per-locus ML gene trees (ASTRAL input) and gCF/sCF computation
  • bayesian-inference - full Bayesian co-estimation; StarBEAST2 for species tree plus dates
  • divergence-dating - turning a coalescent-unit species tree into a dated tree
  • tree-io - reading and writing the Newick gene-tree files ASTRAL consumes
  • comparative-genomics/ortholog-inference - single-copy vs multi-copy decision upstream (ASTRAL vs ASTRAL-Pro)

© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in phylogenetics/species-trees of GPTomics/bioSkills.

  • SKILL.md
  • examples/astral_pipeline.sh
  • usage-guide.md

Open the folder on GitHubat commit d91ed3d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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    A skill your agent uses when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls…

    3.2k GitHub starsUsed in 2 repos~3.9k tokens
    Research & ScienceAuto-check: notes
  • Metabolic Study Planner

    aiming-lab/AutoResearchClaw

    Turns a broad metabolic modelling topic into a concrete, paper-shaped plan with organism, model, perturbations, metrics and figures before any FBA code is written.

    15k GitHub stars~1.9k tokensUpdated 1 mo ago
    Research & ScienceAuto-check passed
  • Dbsnp Database

    google-deepmind/science-skills

    A skill your agent uses when you want to look up, map, and search for short genetic variants (SNPs, indels) in NCBI's dbSNP database.

    3.2k GitHub starsUsed in 2 repos~3.4k tokens
    Research & ScienceAuto-check: notes
  • MFA Pipeline Orchestrator

    aiming-lab/AutoResearchClaw

    Runs a metabolic flux analysis from model loading to phenotype prediction and figures by handing work to four sub-agents in sequence.

    15k GitHub stars~923 tokensUpdated 1 mo ago
    Research & ScienceAuto-check passed

More from GPTomics/bioSkills

All 559 skills in this repo
  • Bio Alignment Io

    GPTomics/bioSkills

    Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO.

    1.2k GitHub starsUsed in 3 repos~4.9k tokens
    Auto-check passed
  • bioSkills Installer

    GPTomics/bioSkills

    Installs the bioSkills collection of 425 bioinformatics skills in one step, or only chosen categories, so sequencing, RNA-seq, single-cell and variant tasks get specialized help.

    1.2k GitHub starsUsed in 1 repo~789 tokens
    Auto-check passed
  • Bio Write Sequences

    GPTomics/bioSkills

    Write biological sequences to files (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO.

    1.2k GitHub starsUsed in 3 repos~2.1k tokens
    Auto-check passed
  • Amplicon Primer Clipping

    GPTomics/bioSkills

    Soft- or hard-clips PCR primer footprints from aligned amplicon BAMs so primer bases stop masquerading as confirmed reference sequence.

    1.2k GitHub starsUsed in 2 repos~2.2k tokens
    Auto-check passed
  • Filters BAM alignments by FLAG bits, mapping quality and regions with samtools view or pysam, with recipes for common keep and drop cases.

    1.2k GitHub starsUsed in 2 repos~3.6k tokens
    Auto-check passed
  • Bio Alignment Indexing

    GPTomics/bioSkills

    Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam.

    1.2k GitHub starsUsed in 2 repos~2.4k tokens
    Auto-check passed

Questions about Bio Phylo Species Trees

What does Bio Phylo Species Trees do?

Estimates species trees under the multispecies coalescent from per-locus gene trees with the modern ASTER astral binary (ASTRAL-III/wASTRAL/ASTRAL-Pro), plus SVDQuartets, BPP, and StarBEAST2. Bio Phylo Species Trees is an agent skill from GPTomics/bioSkills. Estimates species trees under the multispecies coalescent from per-locus gene trees with the modern ASTER astral binary (ASTRAL-III/wASTRAL/ASTRAL-Pro), plus SVDQuartets, BPP, and StarBEAST2.

When should I use Bio Phylo Species Trees?

Bio Phylo Species Trees fits situations like: multi-locus discordance; rapid radiations; anomaly-zone risk; concordance-factor interpretation.

How do I install Bio Phylo Species Trees in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-phylo-species-trees -a claude-code`. Or copy the skill folder (phylogenetics/species-trees in GPTomics/bioSkills) into .claude/skills/bio-phylo-species-trees in your project. Claude Code loads it when a task matches its description.

How do I install Bio Phylo Species Trees in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-phylo-species-trees -a codex`. Or copy the skill folder (phylogenetics/species-trees in GPTomics/bioSkills) into .agents/skills/bio-phylo-species-trees in your project. Codex loads it when a task matches its description.

Can I use Bio Phylo Species Trees in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GPTomics/bioSkills --skill bio-phylo-species-trees -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-phylo-species-trees, .gemini/skills/bio-phylo-species-trees, .github/skills/bio-phylo-species-trees and .opencode/skills/bio-phylo-species-trees in your project.

What does Bio Phylo Species Trees need to run?

Going by SKILL.md and its folder, Bio Phylo Species Trees needs a shell for the scripts in its folder. Our summary lists: A Bash shell.

Does Bio Phylo Species Trees access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Bio Phylo Species Trees safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bio Phylo Species Trees use?

Bio Phylo Species Trees is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bio Phylo Species Trees use?

About 5.7k tokens (SKILL.md is roughly 23k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bio Phylo Species Trees?

Skills that share tags, products or a category with Bio Phylo Species Trees: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Clinvar Database (google-deepmind/science-skills, 3.2k stars) and Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bio Phylo Species Trees?

GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.

Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.