multiqc-qc-reports
Aggregates QC from 150+ bioinformatics tools into one interactive HTML report. Scans FastQC, samtools, STAR, HISAT2, Trim Galore, featureCounts, Kallisto, Salmon, Picard, GATK logs; merges per-sample stats with plots. For NGS pipeline-wide QC. Use FastQC directly for single-sample; MultiQC for multi-sample reporting.
npx skills add BioTender-max/awesome-bio-agent-skills --skill multiqc-qc-reports --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# MultiQC — Multi-Sample QC Report Aggregator ## Overview MultiQC automatically searches directories for QC log files from 150+ bioinformatics tools and aggregates statistics across all samples into a single interactive HTML report. It parses outputs from FastQC, samtools flagstat, STAR, HISAT2, Trim Galore, Salmon, Kallisto, featureCounts, Picard, GATK, and many more — eliminating the need to manually review per-sample QC files. Reports include interactive bar plots, scatter plots, heatmaps, and tables with configurable warnings and pass/fail thresholds. ## When to Use - Reviewing QC metrics across 10+ samples at once after FastQC, alignment, or quantification - Final QC checkpoint before differential expression or variant analysis - Sharing QC summaries with collaborators or including in publications - Identifying batch effects, outlier samples, or failed sequencing runs - Combining QC from multi-step pipelines (trimming → alignment → quantification) into one view - Use FastQC directly instead for initial single-sample QC exploration - For custom QC metrics not from standard tools, use Python/R directly; MultiQC parses tool outputs only ## Prerequisites - **Python packages**: `mu
- Overview
- When to Use
- Prerequisites
- Workflow
- Step 1: Generate Tool-Specific QC Files
- Step 2: Run MultiQC on a Directory
- Step 3: Configure Report Behavior
- Step 4: Use MultiQC Modules and Filters
- Step 5: Export Data for Downstream Analysis
- Step 6: Automate in Pipeline Scripts
- Key Parameters
- Common Recipes
- Recipe: Add MultiQC to a Snakemake Pipeline
- Recipe: Parse MultiQC Output in Python
pip install multiqc Verify multiqc --version MultiQC v1.25.0 With conda (recommended for bioinformatics) conda install -c bioconda multiqc FastQC on all FASTQ files mkdir -p qc/fastqc fastqc data/*.fastq.gz -o qc/fastqc/ -t 8 samtools flagstat on all BAM files
What does the multiqc-qc-reports skill do?
Aggregates QC from 150+ bioinformatics tools into one interactive HTML report. Scans FastQC, samtools, STAR, HISAT2, Trim Galore, featureCounts, Kallisto, Salmon, Picard, GATK logs; merges per-sample stats with plots. For NGS pipeline-wide QC. Use FastQC directly for single-sample; MultiQC for multi-sample reporting.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill multiqc-qc-reports --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 BioTender-max/awesome-bio-agent-skills, a repository with 144 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.