All-in-one FASTQ QC and adapter trimming. Auto-detects Illumina adapters, filters low-quality reads, corrects paired-end overlaps, emits HTML+JSON QC in one pass. 3-10x faster than Trim Galore/Trimmomatic. First step before STAR, BWA-MEM2, or Salmon.
SkillScore92Maintained
Automatic
◆Data Engineer pack
Last verified 8/24/2026
What it changes
Passed 21 of 23 test cases with the skill, and 16 of 23 without it.
Without skill71%
With skill92%
Checks it passed that the model alone missed
Includes --detect_adapter_for_pe flag
Specifies HTML report output with -h flag
Uses command -v fastp to check binary existence
and 3 more in the full report
5 fixed17 passed either way2 still failing0 broke
Pass-rate lift
+21 pp
Tokens
+251%
Agent turns
0%
Response time
-25%
Model gemini-3.6-flashMeasured 8/24/2026v1 · latest is v222 of 24 cases passed with skill · 23 comparable
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
See it in action
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 4
Prompt
When parsing `sample_qc.json` from fastp in Python, I need to extract the exact number of reads that had adapter sequences trimmed. Which key under `filtering_result` provides this count?
What was checked, without and with the skill
Checked
Without skill
With skill
Accesses filtering_result -> adapter_trimmed key
Missed
Passed
case-06 · gemini-3.6-flash · 8/24/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md439 linessingle file
fastp — Fast FASTQ Quality Control and Adapter Trimming
Overview
fastp performs adapter trimming, quality filtering, and QC reporting for Illumina FASTQ files in a single multi-threaded pass. It automatically detects adapter sequences from paired-end read overlaps — eliminating the need to specify adapters manually. fastp corrects mismatches in paired-end overlap regions, filters reads by quality score and length, removes polyX tails (polyA for RNA-seq), and generates interactive HTML and machine-readable JSON QC reports. Being 3–10× faster than Trim Galore and Trimmomatic while providing comparable or better results, fastp has become the standard preprocessing step before alignment in WGS, RNA-seq, and ChIP-seq pipelines.
When to Use
Trimming Illumina adapters and low-quality bases before alignment in any NGS pipeline (RNA-seq, WGS, WES, ChIP-seq, ATAC-seq)
Generating per-sample QC reports (HTML + JSON) as the first step of a pipeline, before MultiQC aggregation
Processing paired-end reads where adapter auto-detection from overlap is preferred over manual adapter specification
Removing polyA tails from RNA-seq reads from 3′ end-enriched protocols (Smart-seq, QuantSeq)
Splitting a FASTQ file by UMI or by index for demultiplexing workflows
Use Trim Galore as an alternative when TrimGalore's detailed per-base quality report from FastQC is required alongside trimming
Use Trimmomatic as an alternative for fine-grained control of sliding-window trimming steps
Prerequisites
Software: fastp (conda or pre-compiled binary)
Input: raw Illumina FASTQ files (single-end or paired-end, .fastq or .fastq.gz)
Check before installing: The tool may already be available in the current environment (e.g., inside a pixi / conda env). Run command -v fastp first and skip the install commands below if it returns a path. When running inside a pixi project, invoke the tool via pixi run fastp rather than bare fastp.