Agent skill

bio-read-qc-contamination-screening

Detect sample contamination and cross-species reads using FastQ Screen. Screen reads against multiple reference genomes to identify bacterial, viral, adapter, or sample swap contamination. Use when suspecting cross-contamination or working with samples prone to microbial contamination.

FreedomIntelligencegithub.com/FreedomIntelligenceGitHub ↗
claude-codeships scripts
Install
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-read-qc-contamination-screening --agent claude-code

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

Facts
Files in the skill folder: 3
SKILL.md size: 6 KB
Bundled scripts: yes
Path: skills/bio-read-qc-contamination-screening/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,909
Language: Python
Read our review of the source →

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: BBTools 39.0+, Bowtie2 2.5.3+, FastQ Screen 0.15+, FastQC 0.12+, MultiQC 1.21+ Before using code patterns, verify installed versions match. If versions differ: - 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 Screening Screen FASTQ files against multiple genomes to identify contamination sources using FastQ Screen. **"Check for contamination in sequencing data"** → Align a sample of reads against multiple reference genomes to identify cross-species or cross-sample contamination. - CLI: `fastq_screen --conf fastq_screen.conf reads.fq` ## FastQ Screen Overview FastQ Screen aligns a subset of reads against multiple reference genomes to identify: - Cross-species contamination - Bacterial/viral contamination - Adapter sequences - PhiX spike-in - Sample swaps ## Basic Usage ```bash # Screen against configured genomes fastq_screen sample.fastq.gz # Multiple files fastq_screen *.fastq.gz # Specify output directory fastq_screen --outdir qc_results/

What's inside
Steps it walks through
  1. Version Compatibility
  2. FastQ Screen Overview
  3. Basic Usage
  4. Configuration File
  5. Pre-built Databases
  6. Screening Options
  7. Output Options
  8. Filter Codes
  9. Output Files
  10. Results Format
  11. Interpreting Results
  12. Expected Results by Sample Type
  13. Common Issues
  14. MultiQC Integration
Ships with 2 files
  • examples/screen_samples.sh
  • usage-guide.md
Commands it runs
Screen against configured genomes
fastq_screen sample.fastq.gz
Multiple files
fastq_screen *.fastq.gz
Specify output directory
fastq_screen --outdir qc_results/ sample.fastq.gz
Custom config file
fastq_screen --conf my_screen.conf sample.fastq.gz
Download common screening databases
fastq_screen --get_genomes
More from OpenClaw-Medical-Skills
All skills →
About this skill
What does the bio-read-qc-contamination-screening skill do?

Detect sample contamination and cross-species reads using FastQ Screen. Screen reads against multiple reference genomes to identify bacterial, viral, adapter, or sample swap contamination. Use when suspecting cross-contamination or working with samples prone to microbial contamination.

How do I install it?

Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-read-qc-contamination-screening --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 FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 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.

Keep going