Agent skill · Code Review & Quality

bio-genome-assembly-contamination-detection

Detect contamination and assess genome quality using CheckM, CheckM2, GTDB-Tk, and GUNC for metagenome-assembled genomes and isolate assemblies. Use when checking assemblies for contamination.

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeships scriptsNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill contamination-detection --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 7 KB
Bundled scripts: yes
Path: skills/bioskills/contamination-detection/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

## Version Compatibility Reference examples tested with: pandas 2.2+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Contamination Detection **"Check my assembly for contamination"** → Evaluate genome completeness and detect contaminating sequences using marker gene sets or chimeric contig detection. - CLI: `checkm2 predict --input assembly.fa`, `gunc run`, `gtdbtk classify_wf` ## CheckM2 (Recommended) ```bash # Run CheckM2 on single genome checkm2 predict --input assembly.fa --output-directory checkm2_output --threads 16 # Run on multiple genomes (directory of FASTAs) checkm2 predict --input genomes/ --output-directory checkm2_output \ --threads 16 --extension fa # Output: quality_report.tsv with Completeness, Contamination, Coding_Density ``` ## Interpret CheckM2 Results ```bash # quality_report.tsv columns: # Name, Completeness, Contamination, Com

What's inside
Steps it walks through
  1. Version Compatibility
  2. CheckM2 (Recommended)
  3. Interpret CheckM2 Results
  4. CheckM (Original)
  5. CheckM Plots
  6. GTDB-Tk Taxonomic Classification
  7. GTDB-Tk De Novo Workflow
  8. GUNC Chimerism Detection
  9. GUNC Interpretation
  10. Comprehensive QC Pipeline
  11. Filter by Quality Standards
  12. Remove Contamination
  13. Detect Foreign Contigs
  14. Decontaminate with BlobTools
Ships with 2 files
  • examples/mag_qc_pipeline.sh
  • usage-guide.md
Commands it runs
Run CheckM2 on single genome
checkm2 predict --input assembly.fa --output-directory checkm2_output --threads 16
Run on multiple genomes (directory of FASTAs)
checkm2 predict --input genomes/ --output-directory checkm2_output \
quality_report.tsv columns:
Filter high-quality genomes (MIMAG standards)
awk -F'\t' 'NR==1 || ($2 > 90 && $3 < 5)' quality_report.tsv > high_quality_mags.tsv
Medium quality
awk -F'\t' 'NR==1 || ($2 >= 50 && $3 < 10)' quality_report.tsv > medium_quality_mags.tsv
Run CheckM lineage workflow
More from awesome-bio-agent-skills
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About this skill
What does the bio-genome-assembly-contamination-detection skill do?

Detect contamination and assess genome quality using CheckM, CheckM2, GTDB-Tk, and GUNC for metagenome-assembled genomes and isolate assemblies. Use when checking assemblies for contamination.

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill contamination-detection --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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