Bio Atac Seq Motif Deviation
FreedomIntelligence/OpenClaw-Medical-Skills
Analyze transcription factor motif accessibility variability using chromVAR.
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…
$ npx skills add GPTomics/bioSkills --skill bio-genome-assembly-assembly-qc -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install GPTomics/bioSkills bio-genome-assembly-assembly-qc --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "bio-genome-assembly-assembly-qc" agent skill from https://github.com/GPTomics/bioSkills/tree/main/genome-assembly/assembly-qc into .claude/skills/bio-genome-assembly-assembly-qc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-genome-assembly-assembly-qc", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/GPTomics/bioSkills/tree/main/genome-assembly/assembly-qcType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add GPTomics/bioSkills --skill bio-genome-assembly-assembly-qc -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install GPTomics/bioSkills bio-genome-assembly-assembly-qc --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/genome-assembly/assembly-qc .agents/skills/bio-genome-assembly-assembly-qc && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "bio-genome-assembly-assembly-qc" agent skill from https://github.com/GPTomics/bioSkills/tree/main/genome-assembly/assembly-qc into .agents/skills/bio-genome-assembly-assembly-qc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-genome-assembly-assembly-qc", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GPTomics/bioSkills --skill bio-genome-assembly-assembly-qc -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install GPTomics/bioSkills bio-genome-assembly-assembly-qc --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/genome-assembly/assembly-qc .cursor/skills/bio-genome-assembly-assembly-qc && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "bio-genome-assembly-assembly-qc" agent skill from https://github.com/GPTomics/bioSkills/tree/main/genome-assembly/assembly-qc into .cursor/skills/bio-genome-assembly-assembly-qc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-genome-assembly-assembly-qc", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/GPTomics/bioSkills.git --path genome-assembly/assembly-qc--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add GPTomics/bioSkills --skill bio-genome-assembly-assembly-qc -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install GPTomics/bioSkills bio-genome-assembly-assembly-qc --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/genome-assembly/assembly-qc .gemini/skills/bio-genome-assembly-assembly-qc && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "bio-genome-assembly-assembly-qc" agent skill from https://github.com/GPTomics/bioSkills/tree/main/genome-assembly/assembly-qc into .gemini/skills/bio-genome-assembly-assembly-qc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-genome-assembly-assembly-qc", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install GPTomics/bioSkills bio-genome-assembly-assembly-qcInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add GPTomics/bioSkills --skill bio-genome-assembly-assembly-qc -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/genome-assembly/assembly-qc .github/skills/bio-genome-assembly-assembly-qc && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "bio-genome-assembly-assembly-qc" agent skill from https://github.com/GPTomics/bioSkills/tree/main/genome-assembly/assembly-qc into .github/skills/bio-genome-assembly-assembly-qc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-genome-assembly-assembly-qc", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GPTomics/bioSkills --skill bio-genome-assembly-assembly-qc -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install GPTomics/bioSkills bio-genome-assembly-assembly-qc --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/genome-assembly/assembly-qc .opencode/skills/bio-genome-assembly-assembly-qc && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "bio-genome-assembly-assembly-qc" agent skill from https://github.com/GPTomics/bioSkills/tree/main/genome-assembly/assembly-qc into .opencode/skills/bio-genome-assembly-assembly-qc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-genome-assembly-assembly-qc", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
bio-genome-assembly-assembly-qcEvaluates 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). 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.
3 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.
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.
Ships script files (Shell and Python), which the agent can run.
Shell commands in SKILL.md call:
pipFrom the folder's file list and the shell code blocks in SKILL.md.
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.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 2,293 words, ~4,898 tokens.
.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.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:
<tool> --version then <tool> --help to confirm flagspip show <package> then help(module.function) to check signaturesResults depend on inputs that outlive the binary version - record them:
_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+).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).If code throws an error, introspect the installed tool and adapt rather than retrying.
"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.
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)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:
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:
| Tool | Citation | Axis / Role | When |
|---|---|---|---|
| QUAST / calN50 | Gurevich 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 |
| BUSCO | Manni 2021 Mol Biol Evol; Simão 2015 Bioinformatics | gene-space completeness (C/S/D/F/M) | universal; conservative on good genomes |
| compleasm | Huang & Li 2023 Bioinformatics | gene-space completeness, miniprot-based | faster + more sensitive; default on HiFi/T2T-era genomes |
| Merqury | Rhie 2020 Genome Biol | reference-free QV + k-mer completeness + spectra-cn + phasing | always; the accuracy gold standard |
| merfin | Formenti 2022 Nat Methods | multiplicity-corrected QV / polishing | refine QV biased by k-mer multiplicity |
| Inspector | Chen 2021 Genome Biol | reference-free structural + base errors (long reads) | novel genomes; can also correct |
| CRAQ | Li 2023 Nat Commun | reference-free structural/regional errors (clipped alignments) | novel genomes; flags misjoins to split |
| asmgene | Li (minimap2, no separate journal) | gene collapse / false duplication | high-quality genomes where BUSCO saturates |
| GenomeScope2 | Ranallo-Benavidez 2020 Nat Commun | genome size / het / repeat % from k-mers | the size estimate NG50/auNG/spectra-cn need (-> genome-profiling) |
| Scenario | Recommended | Why |
|---|---|---|
| Novel genome, no trusted reference | Merqury QV + k-mer completeness + BUSCO/compleasm + auN/NGx + Inspector/CRAQ | reference-free triad; QUAST structure is uninterpretable here |
| Same-species (near-isogenic) reference available | add 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 low | compleasm | BUSCO under-reports good genomes (its predictor, not the assembly, misses genes) |
| Contiguity claim must resist gaming | auN/auNG via calN50.js -L <size> | N50 is a single unstable order-statistic and is gameable |
| High BUSCO-Duplicated, size > expected | spectra-cn + asmgene + GenomeScope2 size -> purge_dups | distinguish uncollapsed haplotigs (purge) from real WGD (keep) |
| Phased diploid / trio assembly | Merqury hap-mers: switch/hamming error + blob plot | phasing 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-reports | garbage-in caps assembly quality |
| Bacterial isolate / MAG completeness+contamination | -> contamination-detection (CheckM2/GUNC/MIMAG) | marker-gene completeness/contamination is a different problem |
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.
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 sensitiveReported 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%).
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.
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.
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).
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.
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.
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.
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).
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.
| Threshold | Source | Rationale |
|---|---|---|
| Merqury QV >= 40 | EBP/VGP minimum (Rhie 2021) | 1 error/10 kb; QV50 strong, ~Q60 T2T-grade; report the actual value |
| QV from polishing reads | circularity trap | always biased high; use independent/accurate reads, prefer merfin |
| BUSCO Complete >= 95%, Fragmented < 5% | field convention | read F+M with C; high F = contiguity/base-quality problem behind a good C% |
| BUSCO Duplicated ~1-3% (clean haploid); >5-8% no WGD | assembly norm | uncollapsed haplotigs -> purge_dups; cross-check size + spectra-cn + asmgene |
| compleasm preferred on good genomes | Huang & Li 2023 | BUSCO under-reports (~95.7% vs ~99.6% on human) due to its predictor |
| k-mer completeness (Merqury) >= 95% | field convention | lower = sequence absent that the BUSCO gene set cannot see |
| Contig NG50 >= 1 Mb (EBP "6") | Rhie 2021 / EBP standards | the 6.C.Q40 contig bar; relaxed "5" (>100 kb) for low-input species |
| Report auN/NGx, not bare N50 | Li (auN blog) | N50 is gameable and a single unstable order-statistic |
| NG/auN need a genome-size estimate | by definition | GenomeScope2/flow cytometry; couples contiguity to completeness |
| Error / symptom | Cause | Solution |
|---|---|---|
| Big N50, no QV reported | leading with the most-gamed metric | add Merqury QV, k-mer completeness, auN/NGx |
| Hundreds of QUAST "misassemblies" on a novel genome | divergent reference; biology scored as error | reference-free (Inspector/CRAQ); QUAST only vs same organism |
| QV suspiciously high, rose after polishing | QV computed on the polishing reads (circular) | independent/accurate-read k-mer DB; merfin |
| Assembly ~1.5-2x expected size, high BUSCO-D | uncollapsed haplotigs (false duplication) | purge_dups; verify with spectra-cn + asmgene |
| Size drops below GenomeScope2 estimate after purging | over-purged real segmental duplications | back off purge stringency; check asmgene collapse direction |
| BUSCO low-90s on a HiFi/T2T assembly | BUSCO predictor misses present genes | re-run compleasm before concluding incompleteness |
| Scaffold N50 >> contig N50 reported as contiguity | contiguity is gap-Ns, not sequence | report contig N50 too; each gap is a join hypothesis |
© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 3 other files in genome-assembly/assembly-qc of GPTomics/bioSkills.
Open the folder on GitHubat commit d91ed3d
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.
Bio Genome Assembly Assembly Qc next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Bio Genome Assembly Assembly Qc this skillGPTomics/bioSkills | 1.2k | 1 repos | ~4.9k | Automated safety check: Pass | MIT | |
| Bio Atac Seq Motif DeviationFreedomIntelligence/OpenClaw-Medical-Skills | 3.1k | — | ~2.3k | Automated safety check: Pass | None | |
| Viennarna Structure Predictionjaechang-hits/SciAgent-Skills | 374 | 1 repos | ~5.4k | Automated safety check: Pass | MIT | |
| Bio Atac Seq Differential AccessibilityFreedomIntelligence/OpenClaw-Medical-Skills | 3.1k | — | ~1.8k | Automated safety check: Pass | None | |
| Alphagenome Single Variant Analysisgoogle-deepmind/science-skills | 3.2k | 2 repos | ~3k | Automated safety check: Notes | Apache-2.0 | |
| 13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.2k | Automated safety check: Pass | MIT |
FreedomIntelligence/OpenClaw-Medical-Skills
Analyze transcription factor motif accessibility variability using chromVAR.
jaechang-hits/SciAgent-Skills
Predict RNA secondary structure, MFE folding, base-pair probabilities, RNA-RNA interactions via ViennaRNA Python bindings.
FreedomIntelligence/OpenClaw-Medical-Skills
Find differentially accessible chromatin regions between conditions using DiffBind or DESeq2.
google-deepmind/science-skills
Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API.
K-Dense-AI/scientific-agent-skills
Estimates reaction fluxes inside cells from steady-state carbon-13 labeling data with a bundled mfapy-based solver, and reports which fluxes the data pin down.
google-deepmind/science-skills
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…
GPTomics/bioSkills
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO.
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.
GPTomics/bioSkills
Write biological sequences to files (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO.
GPTomics/bioSkills
Soft- or hard-clips PCR primer footprints from aligned amplicon BAMs so primer bases stop masquerading as confirmed reference sequence.
GPTomics/bioSkills
Filters BAM alignments by FLAG bits, mapping quality and regions with samtools view or pysam, with recipes for common keep and drop cases.
GPTomics/bioSkills
Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam.
Categories
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).
Bio Genome Assembly Assembly Qc fits situations like: judging whether an assembly is good enough to annotate; comparing assemblers; diagnosing a fragmented; duplicated assembly.
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.
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.
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.
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.
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.
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.
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.
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.
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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.