What problem does it solve?
Running sequencing quality control requires manually chaining FastQC, MultiQC, log parsing, and custom plotting scripts. This Skill automates the entire workflow: it discovers FASTQ files, executes FastQC in parallel, aggregates results with MultiQC, extracts per-base quality and GC content metrics, and renders publication-quality charts, all locally.
Core Features & Use Cases
- Automated QC Pipeline: Scans directories for FASTQ files (.fastq, .fq, .fastq.gz, .fq.gz), runs FastQC with configurable threads, and aggregates results into an interactive MultiQC HTML report.
- Metric Extraction & Visualization: Parses fastqc_data.txt and summary.txt to extract per-base quality scores and GC content, then renders 20+ chart types (line, violin, heatmap, box plot) with Matplotlib and Seaborn.
- Dual CLI/MCP Interface: Runs as a standard ClawBio CLI skill or as an MCP stdio server exposing 10 tools to AI agents in Cursor, Claude Desktop, and other MCP clients.
- Use Case: A researcher receives a batch of raw sequencing reads and asks the agent to run quality control; the skill produces a pass/warn/fail summary table, quality plots, an interactive MultiQC report, and a reproducibility bundle with checksums.
Quick Start
Ask the agent to run quality control on your FASTQ directory, or execute the demo with python clawbio.py run bioqc --demo to generate a synthetic QC report with figures.