Read Qc Agent Skill

Read QC

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially fastp and the other tools listed below.

Before using code or command patterns, verify installed versions match the environment:

• Python: python -c "import <module>; print(<module>.__version__)"
• CLI: <tool> --version
• If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.

Overview

Workflow for sequencing read QC, trimming, contamination screening, and pre-alignment cleanup.

When To Use This Skill

• use when the task is FASTQ quality assessment or cleanup before analysis
• use when the user needs trimming, contamination review, or read-level reports
• use when downstream pipelines depend on deciding whether data quality is acceptable

Quick Route

• If the input is raw or minimally processed data, start with validation and QC before any modeling.
• If the input is already processed, skip directly to the first workflow step that matches the user goal.
• If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.

Progressive Disclosure

• Read references/technical_reference.md when you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance.
• Keep SKILL.md as the main execution path and load the reference file only when the task or failure mode needs the extra detail.

Default Rules

• Prefer Python-first workflows unless the task explicitly requires something else.
• Keep intermediate and final outputs separated.
• Record software versions, reference builds, and key parameters when they affect interpretation.
• Favor reproducible tables and figures over one-off interactive-only outputs.

Expected Inputs

• raw FASTQ files
• adapter sequences
• optional sequencing metadata

Expected Outputs

• QC reports
• filtered or trimmed reads
• contamination summaries

Preferred Tools

• fastp
• FastQC-style reports
• pandas
• matplotlib

Starter Pattern

Preferred starting point: fastp
Inputs: raw FASTQ files, adapter sequences, optional sequencing metadata
Outputs: QC reports, filtered or trimmed reads, contamination summaries

Workflow

1. Profile raw reads

Inspect quality scores, adapter content, duplication, and GC behavior before trimming.

2. Trim or filter judiciously

Apply adapter removal and quality filtering with settings matched to the assay.

3. Screen contamination

Check for host, ribosomal, or other unwanted content if the study design calls for it.

4. Re-evaluate after cleanup

Confirm that trimming improved quality without over-truncating useful reads.

5. Export both reports and cleaned reads

Keep raw and cleaned QC records for reproducibility.

Output Artifacts

• Recommended output layout:
• results/ for final tables and serialized objects
• figures/ for plots and static visual exports
• qc/ for checks that justify downstream interpretation
• Minimum expected outputs for this skill:
• QC reports
• filtered or trimmed reads
• contamination summaries

Quality Review

• Confirm identifiers and metadata join correctly before modeling or summarizing.
• Generate at least one QC artifact before final biological interpretation.
• Keep raw or minimally processed inputs separate from transformed outputs.
• Validate file structure and metadata before handing outputs to downstream tools.
• Retain provenance for every conversion, query, or pipeline execution step.

Anti-Patterns

• trimming aggressively without checking length distributions afterward
• assuming all contamination is removable without assay-specific review
• running downstream analysis on reads that failed basic QC without documenting it

Related Skills

• Sequence And Format IO
• Alignment And Mapping
• Database Access
• Reporting And Figure Export

Optional Supplements

• None required for the first pass.