Use when analyzing FASTQC quality reports from sequencing data, identifying quality issues in NGS datasets, or troubleshooting sequencing problems. Interprets quality metrics and provides actionable recommendations for RNA-seq, DNA-seq, and ChIP-seq data.
Analyze FASTQC quality control reports for Next-Generation Sequencing (NGS) data to assess data quality and identify issues.
When to Use
Use this skill when the task needs Use when analyzing FASTQC quality reports from sequencing data, identifying quality issues in NGS datasets, or troubleshooting sequencing problems. Interprets quality metrics and provides actionable recommendations for RNA-seq, DNA-seq, and ChIP-seq data.
Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format.
Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.
Key Features
Scope-focused workflow aligned to: Use when analyzing FASTQC quality reports from sequencing data, identifying quality issues in NGS datasets, or troubleshooting sequencing problems. Interprets quality metrics and provides actionable recommendations for RNA-seq, DNA-seq, and ChIP-seq data.
Packaged executable path(s): scripts/main.py.
Structured execution path designed to keep outputs consistent and reviewable.
Dependencies
関連 Skill
Python: 3.10+. Repository baseline for current packaged skills.
Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.
Example Usage
cd "20260318/scientific-skills/Data Analytics/fastqc-report-interpreter"
python -m py_compile scripts/main.py
python scripts/main.py --help
Example run plan:
Confirm the user input, output path, and any required config values.
Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
Run python scripts/main.py with the validated inputs.
Review the generated output and return the final artifact with any assumptions called out.
Implementation Details
See ## Workflow above for related details.
Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
Primary implementation surface: scripts/main.py.
Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.
Quick Check
Use this command to verify that the packaged script entry point can be parsed before deeper execution.
python -m py_compile scripts/main.py
Audit-Ready Commands
Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.