Agent skill · Code Review & Quality

bio-read-qc-quality-filtering

Filter reads by quality scores, length, and N content using Trimmomatic and fastp. Apply sliding window trimming, remove low-quality bases from read ends, and discard reads below thresholds. Use when reads have poor quality tails or require minimum quality for downstream analysis.

FreedomIntelligencegithub.com/FreedomIntelligenceGitHub ↗
claude-codeships scripts
Install
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-read-qc-quality-filtering --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-quality-filtering/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: Trimmomatic 0.39+, cutadapt 4.4+, fastp 0.23+ 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. # Quality Filtering Trim low-quality bases and filter reads using Trimmomatic sliding window or fastp quality filtering. **"Filter reads by quality"** → Remove low-quality bases and discard reads below quality/length thresholds. - CLI: `trimmomatic PE` with SLIDINGWINDOW and MINLEN options - CLI: `fastp --qualified_quality_phred 20 --length_required 50` ## Trimmomatic Quality Operations ### Single-End Mode ```bash trimmomatic SE -phred33 \ input.fastq.gz output.fastq.gz \ LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36 ``` ### Paired-End Mode ```bash trimmomatic PE -phred33 -threads 4 \ input_R1.fastq.gz input_R2.fastq.gz \ output_R1_paired.fastq.gz output_R1_unpaired.fastq.gz \ output_R2_paired.fastq.gz output_R2_unpaired.fastq.gz \ LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:

What's inside
Steps it walks through
  1. Version Compatibility
  2. Trimmomatic Quality Operations
  3. Single-End Mode
  4. Paired-End Mode
  5. Trimmomatic Operations
  6. Common Trimmomatic Recipes
  7. SLIDINGWINDOW Details
  8. fastp Quality Filtering
  9. Basic Quality Filtering
  10. fastp Quality Options
  11. Paired-End with fastp
  12. Length Filtering
  13. Cutadapt Quality Trimming
  14. Combined Adapter + Quality Trimming
Ships with 2 files
  • examples/quality_filter.sh
  • usage-guide.md
Commands it runs
trimmomatic SE -phred33 \
input.fastq.gz output.fastq.gz \
trimmomatic PE -phred33 -threads 4 \
input_R1.fastq.gz input_R2.fastq.gz \
output_R1_paired.fastq.gz output_R1_unpaired.fastq.gz \
output_R2_paired.fastq.gz output_R2_unpaired.fastq.gz \
Standard quality trimming
trimmomatic SE input.fq output.fq \
Aggressive 3' trimming
Trim both ends, strict filtering
More from OpenClaw-Medical-Skills
All skills →
About this skill
What does the bio-read-qc-quality-filtering skill do?

Filter reads by quality scores, length, and N content using Trimmomatic and fastp. Apply sliding window trimming, remove low-quality bases from read ends, and discard reads below thresholds. Use when reads have poor quality tails or require minimum quality for downstream analysis.

How do I install it?

Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-read-qc-quality-filtering --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.

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