Agent skill · Data & Analytics

bio-read-qc-fastp-workflow

All-in-one read preprocessing with fastp including adapter trimming, quality filtering, deduplication, base correction, and HTML report generation. Use when preprocessing Illumina data and wanting a single fast tool instead of separate Cutadapt, Trimmomatic, and FastQC steps.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill fastp-workflow-gptomics-bioskills --agent claude-code

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

Facts
Files in the skill folder: 2
SKILL.md size: 6 KB
Bundled scripts: none
Path: skills/ai-ml/fastp-workflow-gptomics-bioskills/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

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

From the SKILL.md

# fastp Workflow All-in-one preprocessing tool that handles adapter trimming, quality filtering, deduplication, and report generation in a single pass. ## Basic Usage ### Single-End ```bash fastp -i input.fastq.gz -o output.fastq.gz ``` ### Paired-End ```bash fastp -i R1.fastq.gz -I R2.fastq.gz -o R1_clean.fastq.gz -O R2_clean.fastq.gz ``` ### With Custom HTML/JSON Reports ```bash fastp -i R1.fq.gz -I R2.fq.gz \ -o R1_clean.fq.gz -O R2_clean.fq.gz \ -h sample_report.html \ -j sample_report.json ``` ## Adapter Trimming fastp auto-detects Illumina adapters by default. ```bash # Auto-detect (default) fastp -i in.fq -o out.fq # Specify adapters manually fastp -i in.fq -o out.fq \ --adapter_sequence AGATCGGAAGAGCACACGTCTGAACTCCAGTCA # Paired-end with manual adapters fastp -i R1.fq -I R2.fq -o R1.out.fq -O R2.out.fq \ --adapter_sequence AGATCGGAAGAGCACACGTCTGAACTCCAGTCA \ --adapter_sequence_r2 AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT # Disable adapter trimming fastp -i in.fq -o out.fq --disable_adapter_trimming # Adapter FASTA file fastp -i in.fq -o out.fq --adapter_fasta adapters.fa ``` ## Quality Filtering ```bash # Per-base quality threshold (default Q15) fastp -i in.fq -o out.fq -q 20 # Mea

What's inside
Steps it walks through
  1. Basic Usage
  2. Single-End
  3. Paired-End
  4. With Custom HTML/JSON Reports
  5. Adapter Trimming
  6. Quality Filtering
  7. Quality Trimming
  8. Length Filtering
  9. Poly-X Trimming
  10. N Base Handling
  11. Deduplication
  12. Base Correction (Paired-End Only)
  13. Paired-End Merge
  14. UMI Processing
Ships with 1 file
  • metadata.json
Commands it runs
fastp -i input.fastq.gz -o output.fastq.gz
fastp -i R1.fastq.gz -I R2.fastq.gz -o R1_clean.fastq.gz -O R2_clean.fastq.gz
fastp -i R1.fq.gz -I R2.fq.gz \
Auto-detect (default)
fastp -i in.fq -o out.fq
Specify adapters manually
fastp -i in.fq -o out.fq \
Paired-end with manual adapters
fastp -i R1.fq -I R2.fq -o R1.out.fq -O R2.out.fq \
Disable adapter trimming
More from claude-skill-registry
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About this skill
What does the bio-read-qc-fastp-workflow skill do?

All-in-one read preprocessing with fastp including adapter trimming, quality filtering, deduplication, base correction, and HTML report generation. Use when preprocessing Illumina data and wanting a single fast tool instead of separate Cutadapt, Trimmomatic, and FastQC steps.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill fastp-workflow-gptomics-bioskills --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 majiayu000/claude-skill-registry, a repository with 534 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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