What problem does it solve?
Raw sequencing reads often contain adapters, low-quality bases, and artifacts that silently corrupt downstream analysis. This Skill runs real local QC tools (FastQC, MultiQC, fastp, Cutadapt, seqkit) on FASTQ files, interprets every FastQC module flag, and produces an evidence-based trim-or-proceed decision without ever fabricating QC numbers or overwriting raw reads.
Core Features & Use Cases
- Automated QC orchestration: A bundled script preflights tool availability, runs FastQC and seqkit stats into an isolated workdir, and emits a conda/mamba install plan if required binaries are missing.
- FastQC report interpretation: Maps each module (per-base quality, adapter content, overrepresented sequences, duplication, GC content, N content) to its meaning and a concrete action for your library type.
- Opt-in trimming: Trimming with fastp or Cutadapt only happens on explicit request, writes new files to a separate workdir, and is verified by re-running FastQC on trimmed output.
- Use Case: You receive paired-end RNA-seq reads and ask whether they need trimming. The Skill runs FastQC, finds a 3' adapter ramp and quality tail decay, recommends fastp trimming, executes it into a new directory, and confirms the fix with a post-trim report.
Quick Start
Run QC on my paired-end reads sample_R1.fastq.gz and sample_R2.fastq.gz, interpret the FastQC report, and tell me whether I need to trim adapters before alignment.
