Agent skill

Bio Genome Assembly Assembly Qc

by GPTomics in GPTomics/bioSkills

Evaluates genome assembly quality across the three orthogonal axes - contiguity (QUAST auN/NG50/NGx, not bare N50), completeness (BUSCO/compleasm gene-space plus Merqury k-mer completeness), and…

MITAuto-check passedResearch & Science

Install Bio Genome Assembly Assembly Qc

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-genome-assembly-assembly-qc -a claude-code

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

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-genome-assembly-assembly-qc --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/genome-assembly/assembly-qc .claude/skills/bio-genome-assembly-assembly-qc && 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-genome-assembly-assembly-qc
GitHub stars
1.2k
Used in
1 other repo
Token cost
~4.9k tokens
SKILL.md length
2,293 words
Files
4
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Evaluates genome assembly quality across the three orthogonal axes - contiguity (QUAST auN/NG50/NGx, not bare N50), completeness (BUSCO/compleasm gene-space plus Merqury k-mer completeness), and…

  • Works in 3 steps: Report auN/NGx, not bare N50. auN = the… → Default to reference-free. For a novel… → Report a Merqury QV - and never compute…
  • Judging whether an assembly is good enough to annotate
  • SKILL.md covers Version Compatibility, The Single Most Important…, Tool Taxonomy and Decision Tree by Scenario, plus 9 more sections
  • Runs Shell and Python scripts from its folder; calls pip

What it does

Bio Genome Assembly Assembly Qc is an agent skill from GPTomics/bioSkills. Evaluates genome assembly quality across the three orthogonal axes - contiguity (QUAST auN/NG50/NGx, not bare N50), completeness (BUSCO/compleasm gene-space plus Merqury k-mer completeness), and correctness (reference-free Merqury QV, Inspector/CRAQ structural errors, asmgene false-duplication/collapse). Covers why N50 is the most-gamed metric, why QV measured on the polishing reads is circular, distinguishing uncollapsed haplotigs from real WGD, and the EBP/VGP 6.C.Q40 standard. Use when judging whether an…

Its SKILL.md is about 4.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files (for example `examples/run_qc.sh`, `examples/summarize_three_axes.py` and `usage-guide.md`).

It sits in Research & Science, covering Bioinformatics and Accessibility. 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

  • Judging whether an assembly is good enough to annotate
  • Comparing assemblers
  • Diagnosing a fragmented
  • Duplicated assembly

Example prompts

  • “Use the bio-genome-assembly-assembly-qc skill to evaluate genome assembly quality across the three orthogonal axes - contiguity (QUAST auN/NG50/NGx…”
  • “/bio-genome-assembly-assembly-qc”

Requirements

  • Python 3
  • A Bash shell

Workflow steps

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

  1. Report auN/NGx, not bare N50. auN = the area under the Nx curve = length-weighted mean contig length; it integrates the whole curve and is…
  2. Default to reference-free. For a novel genome there is no trusted reference; QUAST against a divergent relative reports real…
  3. Report a Merqury QV - and never compute it on the polishing reads. QV is the reference-free accuracy standard reviewers now demand; an…

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 and Python), which the agent can run.

    Shell commands in SKILL.md call:

    • pip

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

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    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 Genome Assembly Assembly Qc loads about 4.9k tokens when it runs. Until then it costs about 176 tokens; SKILL.md has 2,293 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~176
When it runs · the whole SKILL.md, loaded when a task matches
~4.9k

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). 2,293 words, ~4,898 tokens.

Download SKILL.mdSave it as .claude/skills/bio-genome-assembly-assembly-qc/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
bio-genome-assembly-assembly-qc
description
Evaluates genome assembly quality across the three orthogonal axes - contiguity (QUAST auN/NG50/NGx, not bare N50), completeness (BUSCO/compleasm gene-space plus Merqury k-mer completeness), and correctness (reference-free Merqury QV, Inspector/CRAQ structural errors, asmgene false-duplication/collapse). Covers why N50 is the most-gamed metric, why QV measured on the polishing reads is circular, distinguishing uncollapsed haplotigs from real WGD, and the EBP/VGP 6.C.Q40 standard. Use when judging whether an assembly is good enough to annotate or publish, comparing assemblers, diagnosing a fragmented or duplicated assembly, or assessing a phased diploid assembly.
tool_type
cli
primary_tool
QUAST

Version Compatibility

Reference examples tested with: QUAST 5.2+, BUSCO 5.5+ (and 6.x for odb12 lineages), compleasm 0.2.6+, Merqury 1.3+, meryl 1.4+, minimap2 2.26+, Inspector 1.2+, CRAQ 1.0+, merfin 1.0+, GenomeScope2 2.0+.

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

  • CLI: <tool> --version then <tool> --help to confirm flags
  • Python: pip show <package> then help(module.function) to check signatures

Results depend on inputs that outlive the binary version - record them:

  • BUSCO/compleasm depend on the lineage dataset and OrthoDB generation. _odb10 (BUSCO 5) and _odb12 (BUSCO 6 default) gene sets are not comparable across the version boundary; a 99% on the shallow eukaryota_odb10 (~255 genes) is a different claim from 99% on a deep clade set (~5,500+).
  • Merqury QV/completeness depend on the k-mer size (from best_k.sh <genome_size>, not hardcoded) and the read set used for the k-mer DB (use accurate reads; see the circularity warning below).
  • NG50/NGx/auNG depend on the expected genome-size estimate (GenomeScope2 / flow cytometry / a congener).

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

Assembly QC

"Is my genome assembly any good?" -> Measure all three orthogonal axes - contiguity, completeness, correctness - with reference-free methods, because no single number (least of all N50) is quality.

  • CLI: quast.py asm.fa --large --eukaryote -o out (contiguity + reference-based structure), busco -i asm.fa -m genome -l <lineage> or compleasm run -a asm.fa -l <lineage> (gene completeness), merqury.sh reads.meryl asm.fa out (reference-free QV + k-mer completeness), inspector.py -c asm.fa -r reads.fq (reference-free structural errors)

The Single Most Important Modern Insight -- Quality Is Three Orthogonal Axes; N50 Is the Most-Gamed One

Assembly quality is three genuinely orthogonal axes - contiguity, completeness, correctness - and a single number on any one is not quality. The axes do not predict each other, and the diagnostic failure modes prove it:

  • Contiguous + wrong: a single-contig "chromosome" that is three chromosomes misjoined. Perfect N50, catastrophic correctness. Only Hi-C / a same-species reference / read-discordance catches it.
  • Complete + shredded: BUSCO 99%, but repeats collapsed, segmental duplications merged, intergenic space wrong. BUSCO is gene-space-only and cannot see it.
  • Accurate + incomplete: QV60 over the 92% that assembled, with the hard 8% (centromeres, rDNA, satellites) simply absent. QV is silent about what is not there.

The field's historical sin is reporting contiguity alone because it is cheapest to compute and easiest to game. N50 is the most-gamed metric in genomics: it rises when sequence is thrown away (N50 is computed on what survives), when misjoins are not broken (a misjoined contig is a long contig), and when haplotigs are retained. A bigger N50 is louder, not better. Three load-bearing moves:

  1. Report auN/NGx, not bare N50. auN = the area under the Nx curve = length-weighted mean contig length; it integrates the whole curve and is continuous where N50 jumps discontinuously (the small-L50 / T2T regime). NGx/auNG normalize to the expected genome size, coupling contiguity to completeness (an assembly that drops half the genome gets a great N50 but a terrible NG50). Always report contig AND scaffold N50 - if scaffold >> contig, the contiguity is glue (Ns), not sequence.
  2. Default to reference-free. For a novel genome there is no trusted reference; QUAST against a divergent relative reports real inversions/SVs as "misassemblies" and real SNPs as "mismatches". Use Merqury QV (accuracy) + Inspector/CRAQ (structure) + asmgene (false dup/collapse). QUAST is the special case "I have a same-organism reference," not the default.
  3. Report a Merqury QV - and never compute it on the polishing reads. QV is the reference-free accuracy standard reviewers now demand; an assembly paper with no QV is a red flag. But QV from the same reads used for polishing is circular - the polisher already made the assembly agree with those reads, so the QV measures convergence, not correctness. Build the k-mer DB from accurate, ideally independent reads (HiFi/Illumina, not noisy ONT).

Tool Taxonomy

ToolCitationAxis / RoleWhen
QUAST / calN50Gurevich 2013 Bioinformatics; auN = Li blog (no journal)contiguity (auN/NGx, N50/L50) + reference-based structure (NA50, misassemblies)always for contiguity; structure only vs a same-organism reference
BUSCOManni 2021 Mol Biol Evol; Simão 2015 Bioinformaticsgene-space completeness (C/S/D/F/M)universal; conservative on good genomes
compleasmHuang & Li 2023 Bioinformaticsgene-space completeness, miniprot-basedfaster + more sensitive; default on HiFi/T2T-era genomes
MerquryRhie 2020 Genome Biolreference-free QV + k-mer completeness + spectra-cn + phasingalways; the accuracy gold standard
merfinFormenti 2022 Nat Methodsmultiplicity-corrected QV / polishingrefine QV biased by k-mer multiplicity
InspectorChen 2021 Genome Biolreference-free structural + base errors (long reads)novel genomes; can also correct
CRAQLi 2023 Nat Communreference-free structural/regional errors (clipped alignments)novel genomes; flags misjoins to split
asmgeneLi (minimap2, no separate journal)gene collapse / false duplicationhigh-quality genomes where BUSCO saturates
GenomeScope2Ranallo-Benavidez 2020 Nat Commungenome size / het / repeat % from k-mersthe size estimate NG50/auNG/spectra-cn need (-> genome-profiling)

Decision Tree by Scenario

ScenarioRecommendedWhy
Novel genome, no trusted referenceMerqury QV + k-mer completeness + BUSCO/compleasm + auN/NGx + Inspector/CRAQreference-free triad; QUAST structure is uninterpretable here
Same-species (near-isogenic) reference availableadd QUAST --large --eukaryote -r ref.fa (NA50, misassemblies)reference-based structure is trustworthy only vs the same organism
High-quality HiFi/T2T-era assembly, BUSCO looks lowcompleasmBUSCO under-reports good genomes (its predictor, not the assembly, misses genes)
Contiguity claim must resist gamingauN/auNG via calN50.js -L <size>N50 is a single unstable order-statistic and is gameable
High BUSCO-Duplicated, size > expectedspectra-cn + asmgene + GenomeScope2 size -> purge_dupsdistinguish uncollapsed haplotigs (purge) from real WGD (keep)
Phased diploid / trio assemblyMerqury hap-mers: switch/hamming error + blob plotphasing accuracy is the extra axis
Need genome size / het before NG50-> genome-profiling (GenomeScope2)NG/auN/spectra-cn all need a size estimate
Reads not yet QC'd-> read-qc/quality-reportsgarbage-in caps assembly quality
Bacterial isolate / MAG completeness+contamination-> contamination-detection (CheckM2/GUNC/MIMAG)marker-gene completeness/contamination is a different problem

Contiguity -- auN/NGx (not bare N50)

bash
k8 calN50.js -L <genome_size> asm.fa       # N50/L50 + NG50/NGx + auN/auNG; -L sets genome size for NG/auNG (ships with minimap2)
quast.py asm.fa --large --eukaryote -t 16 -o quast_out   # N50/L50, GC, # contigs; NG50 only with -r or --est-ref-size; structure only if -r given

--large implies --eukaryote --min-contig 3000 --min-alignment 500 --extensive-mis-size 7000. Report contig AND scaffold N50; a scaffold N50 far above the contig N50 means the contiguity is scaffolding Ns, and every gap is a join hypothesis that could be a misassembly. NA50 (QUAST, contigs broken at misassemblies) far below N50 means the contiguity is partly fictional.

Completeness -- BUSCO / compleasm + Merqury k-mer completeness

bash
busco -i asm.fa -m genome -l vertebrata_odb10 -o busco_out -c 16   # metaeuk predictor (default)
busco -i asm.fa -m genome --auto-lineage -o busco_out -c 16        # auto-pick if clade unknown
compleasm run -a asm.fa -l vertebrata -o compleasm_out -t 16        # miniprot-based; faster + more sensitive

Reported as C:[S,D],F,M,n. Read C, F, and M together, never C alone: high Fragmented with high Complete signals a contiguity/base-quality problem hidden behind the headline. Use the deepest applicable clade dataset (a 99% on the shallow eukaryota_odb10 ~255-gene set is trivially easy and not comparable to a deep clade set), and record the lineage + OrthoDB generation. On a high-quality assembly, BUSCO reported ~95.7% complete where compleasm reported ~99.6% on the same human genome (Huang & Li 2023) - the missing ~4% was missing from BUSCO's predictor, not the genome - so prefer compleasm on good genomes. Both share gene-space blindness: they say nothing about intergenic/repeat/regulatory sequence. Merqury k-mer completeness scores the whole genome (reliable read k-mers found in the assembly / reliable read k-mers in the reads), catching missing sequence BUSCO cannot see; it is blind to structure (a scrambled-but-present genome scores 100%).

Correctness -- Merqury QV (reference-free) and structural validation

Goal: Get a reference-free per-base accuracy (QV) plus a copy-number/false-duplication picture, then structural errors without a reference.

Approach: Build a meryl k-mer DB at the best_k.sh-derived k from accurate reads, run Merqury for QV + completeness + spectra-cn, and map raw long reads back with Inspector/CRAQ for structural errors. Refine QV with merfin where multiplicity bias matters.

bash
best_k.sh <genome_size>                          # prints recommended k (NOT hardcoded); ~18-21 for Gbp genomes
meryl count k=21 reads.fastq output reads.meryl  # k from best_k.sh; use ACCURATE reads (HiFi/Illumina)
merqury.sh reads.meryl asm.fa out                # -> out.qv (per-scaffold + overall), out.completeness.stats, spectra-cn

inspector.py -c asm.fa -r reads.fq -o insp_out --datatype hifi -t 16   # reference-free structural + base errors
craq -g asm.fa -sms long_reads.bam -ngs short_reads.bam -o craq_out    # R-AQI/S-AQI; CRE (regional)/CSE (structural)

QV: with E = K_asm-only / K_total, per-base error P = 1 - (1 - E)^(1/k) and QV = -10*log10(P) (the ^(1/k) converts a k-mer error rate to per-base, since one wrong base breaks k overlapping k-mers). QV40 = 1 error/10 kb (the EBP/VGP floor), QV50 strong, ~QV60 = T2T-grade (1/Mb). The spectra-cn plot reads completeness and false duplication in one figure: a black "missing" peak at homozygous depth = real content absent; 2-copy k-mers under the 1-copy peak = uncollapsed haplotigs; error k-mers sit far left.

EBP/VGP standards and phased QC

The EBP minimum is 6.C.Q40: x.y.z where x = log10 of contig NG50 (6 = 1 Mb), y = scaffold level (C = chromosome-scale), z = QV (40 = <1 error/10 kb). It is literally the triad turned into a label, and the bar moves - T2T pushed the achievable frontier to ~Q60/gapless, so bragging about QV40 in 2026 is hitting the floor. Match the bar to the organism (the relaxed "5" tier, >100 kb contig NG50, exists for low-input species). For phased diploid/trio assemblies, Merqury hap-mers (parental k-mers, or Hi-C) give the switch error (local haplotype flips within a block) and hamming error (global mis-assignment fraction) - report both, and read the hap-mer blob plot (cleanly phased contigs sit on one axis).

Show full SKILL.md (895 more words)Show less

Per-Method Failure Modes

Leading with N50 (and stopping)

Trigger: reporting a single N50 as the quality verdict. Mechanism: N50 rises on thrown-away sequence, unbroken misjoins, and retained haplotigs; it is also a single unstable order-statistic. Symptom: big N50, unstated QV/completeness/NG50. Fix: report auN/NGx + BUSCO/compleasm + Merqury QV; treat N50-only claims as untrustworthy.

QUAST against a divergent reference

Trigger: quast.py -r congener.fa on a novel genome. Mechanism: real inversions/SVs and SNPs between organism and reference are scored as "misassemblies"/"mismatches"; the count scales with divergence, not error. Symptom: "hundreds of misassemblies" on a correct assembly. Fix: use reference-free correctness (Inspector/CRAQ/Merqury); reserve QUAST structure for a same-organism reference.

QV computed on the polishing reads

Trigger: QV from the exact reads used to polish. Mechanism: the polisher made the assembly agree with those reads by construction. Symptom: impressively high QV that rose after polishing with the QV reads. Fix: build the k-mer DB from accurate, ideally independent reads; consider merfin for multiplicity-corrected QV.

High BUSCO-Duplicated read as success

Trigger: treating high BUSCO-D as "extra coverage / more complete". Mechanism: uncollapsed haplotigs (both alleles kept as separate primary contigs) vs real WGD vs split models. Symptom: D in high single digits to tens, assembly size >> GenomeScope2 estimate. Fix: triangulate size + spectra-cn 2-copy peak + asmgene false-dup; if no WGD -> purge_dups, then re-QC (watch for over-purge: size dropping below the estimate deletes real segmental duplications).

QV/BUSCO accepted on an incomplete genome

Trigger: QV60 + BUSCO 99% taken as "done". Mechanism: QV is measured on what assembled; BUSCO scores the conserved easy core only. Symptom: high accuracy and gene-completeness while 8-15% of sequence (repeats/centromeres) is absent. Fix: add Merqury k-mer completeness (whole-genome) and inspect read mapping-rate/coverage uniformity.

Quantitative Thresholds

ThresholdSourceRationale
Merqury QV >= 40EBP/VGP minimum (Rhie 2021)1 error/10 kb; QV50 strong, ~Q60 T2T-grade; report the actual value
QV from polishing readscircularity trapalways biased high; use independent/accurate reads, prefer merfin
BUSCO Complete >= 95%, Fragmented < 5%field conventionread F+M with C; high F = contiguity/base-quality problem behind a good C%
BUSCO Duplicated ~1-3% (clean haploid); >5-8% no WGDassembly normuncollapsed haplotigs -> purge_dups; cross-check size + spectra-cn + asmgene
compleasm preferred on good genomesHuang & Li 2023BUSCO under-reports (~95.7% vs ~99.6% on human) due to its predictor
k-mer completeness (Merqury) >= 95%field conventionlower = sequence absent that the BUSCO gene set cannot see
Contig NG50 >= 1 Mb (EBP "6")Rhie 2021 / EBP standardsthe 6.C.Q40 contig bar; relaxed "5" (>100 kb) for low-input species
Report auN/NGx, not bare N50Li (auN blog)N50 is gameable and a single unstable order-statistic
NG/auN need a genome-size estimateby definitionGenomeScope2/flow cytometry; couples contiguity to completeness

Common Errors

Error / symptomCauseSolution
Big N50, no QV reportedleading with the most-gamed metricadd Merqury QV, k-mer completeness, auN/NGx
Hundreds of QUAST "misassemblies" on a novel genomedivergent reference; biology scored as errorreference-free (Inspector/CRAQ); QUAST only vs same organism
QV suspiciously high, rose after polishingQV computed on the polishing reads (circular)independent/accurate-read k-mer DB; merfin
Assembly ~1.5-2x expected size, high BUSCO-Duncollapsed haplotigs (false duplication)purge_dups; verify with spectra-cn + asmgene
Size drops below GenomeScope2 estimate after purgingover-purged real segmental duplicationsback off purge stringency; check asmgene collapse direction
BUSCO low-90s on a HiFi/T2T assemblyBUSCO predictor misses present genesre-run compleasm before concluding incompleteness
Scaffold N50 >> contig N50 reported as contiguitycontiguity is gap-Ns, not sequencereport contig N50 too; each gap is a join hypothesis

References

  • Gurevich A, Saveliev V, Vyahhi N, Tesler G. 2013. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29:1072-1075.
  • Manni M, et al. 2021. BUSCO update: novel and streamlined workflows along with broader and deeper phylogenetic coverage for scoring of eukaryotic, prokaryotic, and viral genomes. Mol Biol Evol 38:4647-4654.
  • Simão FA, et al. 2015. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31:3210-3212.
  • Huang N, Li H. 2023. compleasm: a faster and more accurate reimplementation of BUSCO. Bioinformatics 39:btad595.
  • Rhie A, Walenz BP, Koren S, Phillippy AM. 2020. Merqury: reference-free quality, completeness, and phasing assessment for genome assemblies. Genome Biol 21:245.
  • Formenti G, et al. 2022. Merfin: improved variant filtering, assembly evaluation and polishing via k-mer validation. Nat Methods 19:696-704.
  • Chen Y, et al. 2021. Accurate long-read de novo assembly evaluation with Inspector. Genome Biol 22:312.
  • Li K, et al. 2023. CRAQ: identification of errors in draft genome assemblies at single-nucleotide resolution for quality assessment and improvement. Nat Commun 14:6556.
  • Ranallo-Benavidez TR, Jaron KS, Schatz MC. 2020. GenomeScope 2.0 and Smudgeplot for reference-free profiling of polyploid genomes. Nat Commun 11:1432.
  • Rhie A, et al. 2021. Towards complete and error-free genome assemblies of all vertebrate species (VGP). Nature 592:737-746.
  • Li H. 2020. auN: a new metric to measure assembly contiguity. Blog post (lh3.github.io); auN/asmgene tools ship in minimap2/calN50 (Li 2018 Bioinformatics 34:3094-3100).
  • Guan D, et al. 2020. Identifying and removing haplotypic duplication in primary genome assemblies (purge_dups). Bioinformatics 36:2896-2898.
  • short-read-assembly - Short-read assemblies plateau at the repeat structure; QC shows it
  • long-read-assembly - Produces the contiguous-but-error-prone contigs this QC evaluates
  • hifi-assembly - Phased diploid output whose false duplication and switch/hamming error this QC checks
  • assembly-polishing - Merqury QV plateau is the honest stop signal; never QV on the polishing reads
  • scaffolding - Validate chromosome-scale joins (Hi-C/contact map) before trusting scaffold NG50
  • contamination-detection - MAG completeness/contamination (CheckM2/GUNC/MIMAG) is a separate problem
  • genome-profiling - GenomeScope2 genome-size estimate that NG50/auNG/spectra-cn require
  • genome-annotation/annotation-qc - Assembly-side completeness; purge haplotigs before annotating
  • workflows/genome-assembly-pipeline - End-to-end QC -> assemble -> polish -> scaffold -> QC

© 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 3 other files in genome-assembly/assembly-qc of GPTomics/bioSkills.

  • SKILL.md
  • examples/run_qc.sh
  • examples/summarize_three_axes.py
  • 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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  • 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
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  • 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
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  • 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
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  • 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
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  • 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
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Questions about Bio Genome Assembly Assembly Qc

What does Bio Genome Assembly Assembly Qc do?

Evaluates genome assembly quality across the three orthogonal axes - contiguity (QUAST auN/NG50/NGx, not bare N50), completeness (BUSCO/compleasm gene-space plus Merqury k-mer completeness), and…. Bio Genome Assembly Assembly Qc is an agent skill from GPTomics/bioSkills. Evaluates genome assembly quality across the three orthogonal axes - contiguity (QUAST auN/NG50/NGx, not bare N50), completeness (BUSCO/compleasm gene-space plus Merqury k-mer completeness), and correctness (reference-free Merqury QV, Inspector/CRAQ structural errors, asmgene false-duplication/collapse).

When should I use Bio Genome Assembly Assembly Qc?

Bio Genome Assembly Assembly Qc fits situations like: judging whether an assembly is good enough to annotate; comparing assemblers; diagnosing a fragmented; duplicated assembly.

How do I install Bio Genome Assembly Assembly Qc in Claude Code?

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

How do I install Bio Genome Assembly Assembly Qc in Codex?

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

Can I use Bio Genome Assembly Assembly Qc 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-genome-assembly-assembly-qc -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-genome-assembly-assembly-qc, .gemini/skills/bio-genome-assembly-assembly-qc, .github/skills/bio-genome-assembly-assembly-qc and .opencode/skills/bio-genome-assembly-assembly-qc in your project.

What does Bio Genome Assembly Assembly Qc need to run?

Going by SKILL.md and its folder, Bio Genome Assembly Assembly Qc needs a shell and Python for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python 3; A Bash shell.

Does Bio Genome Assembly Assembly Qc access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Bio Genome Assembly Assembly Qc 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 Genome Assembly Assembly Qc use?

Bio Genome Assembly Assembly Qc 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 Genome Assembly Assembly Qc use?

About 4.9k tokens (SKILL.md is roughly 20k 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 Genome Assembly Assembly Qc?

Skills that share tags, products or a category with Bio Genome Assembly Assembly Qc: Bio Atac Seq Motif Deviation (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars), Viennarna Structure Prediction (jaechang-hits/SciAgent-Skills, 374 stars), Bio Atac Seq Differential Accessibility (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars) and Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bio Genome Assembly Assembly Qc?

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.