FASTQC Report Interpreter

Analyze FastQC quality control reports for Next-Generation Sequencing data. Identifies per-base quality issues, adapter contamination, duplication levels, and sequence bias, then provides application-specific remediation recommendations.

Input Validation

This skill accepts: FastQC HTML or JSON report files from Illumina, PacBio, or Oxford Nanopore sequencing runs.

If the request does not involve FastQC report interpretation — for example, asking to perform alignment, call variants, or analyze non-sequencing data — do not proceed. Instead respond:

"fastqc-report-interpreter is designed to interpret FastQC quality control reports for NGS data. Your request appears to be outside this scope. Please provide a FastQC report file, or use a more appropriate tool for your task."

Do not generate any output or analysis before emitting this refusal. Validate scope first.

When to Use

• Assessing sequencing data quality before downstream analysis
• Diagnosing quality failures in RNA-seq, DNA-seq, or ChIP-seq datasets
• Generating batch QC summaries across multiple samples
• Deciding whether trimming or filtering is required before alignment

Quick Check

python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py --demo

Workflow

1. Validate input first — confirm the request involves a FastQC report before any processing.
2. Confirm the user objective, required inputs, and non-negotiable constraints.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Fallback template: If scripts/main.py fails or the report file is unreadable, report: (a) the failure point, (b) which metrics can still be assessed manually from the FastQC HTML, (c) the recommended manual review checklist.

Parameters

*Required unless --demo or --batch is used.

Implementation note: --batch and --application flags are documented here and must be present in scripts/main.py argparse. If the script returns "unrecognized arguments", add these flags to the argparse definition and implement: (1) a batch processing loop for --batch, and (2) application-specific threshold overrides for --application.

Usage

# Single report
python scripts/main.py --report sample_fastqc.json --application rna_seq

# Batch analysis
python scripts/main.py --batch "*_fastqc.json" --output batch_summary.csv

# Demo mode
python scripts/main.py --demo

# JSON output for agent consumption
python scripts/main.py --report sample_fastqc.json --output-format json

Key Quality Metrics

Application-specific thresholds:

• RNA-seq: duplication up to 40% acceptable (transcript abundance effect)
• DNA-seq: strict quality required for variant calling
• ChIP-seq: moderate quality; focus on enrichment metrics

Error Handling

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
• If the report file is not found, print a user-friendly error to stderr and exit with code 1 (do not show raw Python traceback): Error: Report file not found: {path}
• Do not fabricate files, citations, data, search results, or execution outcomes.
• Unicode symbols require PYTHONIOENCODING=utf-8 on Python 3.6 terminals; ASCII fallbacks (PASS/WARN/FAIL) are used when encoding is unavailable.

Output Requirements

Every response must make these explicit:

• Objective and deliverable
• Inputs used and assumptions introduced
• Workflow or decision path taken
• Core result: quality status per metric, issues found, recommendations
• Constraints, risks, caveats (e.g., platform-specific artifacts)
• Unresolved items and next-step checks

Response Template

1. Objective
2. Inputs Received
3. Assumptions
4. Workflow
5. Deliverable
6. Risks and Limits
7. Next Checks

Prerequisites

pip install -r requirements.txt