安全扫描（ClawHub）

• VirusTotal ：良性
• OpenClaw ：可疑

OpenClaw 评估

The skill's documentation promises FastQC HTML/text parsing, batch analysis, and CLI usage that don't match the included code (which expects a pre-parsed JSON structure and provides a different CLI), so the package is internally inconsistent and likely incomplete or misdocumented.

安装（复制给龙虾 AI）

将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。

请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Fastqc Report Interpreter」。简介：Use when analyzing FASTQC quality reports from sequencing data, identifying qua…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/aipoch-ai/fastqc-report-interpreter/SKILL.md
（来源：yingzhi8.cn 技能库）

SKILL.md

打开原始 SKILL.md（GitHub raw）

---
name: fastqc-report-interpreter
description: 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.
allowed-tools: "Read Write Bash Edit"
license: MIT
metadata:
  skill-author: AIPOCH
  version: "1.0"
---

# FASTQC Report Interpreter

Analyze FASTQC quality control reports for Next-Generation Sequencing (NGS) data to assess data quality and identify issues.

## Quick Start

```python
from scripts.fastqc_interpreter import FASTQCInterpreter

interpreter = FASTQCInterpreter()

# Analyze report
analysis = interpreter.analyze("sample_fastqc.html")
print(f"Overall Quality: {analysis.quality_status}")
print(f"Issues Found: {analysis.issues}")
```

## Core Capabilities

### 1. Quality Metrics Analysis

```python
metrics = interpreter.parse_metrics("fastqc_data.txt")
```

**Key Metrics:**
| Metric | Good | Warning | Fail |
|--------|------|---------|------|
| Per base sequence quality | Q > 28 | Q 20-28 | Q < 20 |
| Per sequence quality scores | Peak at Q30 | Peak Q20-30 | Peak < Q20 |
| Per base N content | < 5% | 5-20% | > 20% |
| Sequence duplication | < 20% | 20-50% | > 50% |
| Adapter content | < 5% | 5-10% | > 10% |

### 2. Issue Diagnosis

```python
issues = interpreter.diagnose_issues(metrics)
for issue in issues:
    print(f"{issue.severity}: {issue.description}")
    print(f"Recommendation: {issue.recommendation}")
```

**Common Issues:**

**Low Quality at Read Ends**
- **Cause**: Phasing effects, reagent depletion
- **Solution**: Trim last 10-20 bases

**Adapter Contamination**
- **Cause**: Incomplete adapter removal
- **Solution**: Re-run cutadapt/Trimmomatic with stricter parameters

**High Duplication**
- **Cause**: PCR over-amplification, low input
- **Solution**: Use deduplication; consider library prep optimization

**Per Base Sequence Content Bias**
- **Cause**: Adapter dimers, non-random priming
- **Solution**: Check for adapter contamination; randomize primers

### 3. Batch Analysis

```python
batch_results = interpreter.analyze_batch(
    fastqc_files=["sample1_fastqc.html", "sample2_fastqc.html", ...],
    output_summary="batch_summary.csv"
)
```

### 4. Recommendation Generation

```python
recommendations = interpreter.get_recommendations(
    analysis,
    application="rna_seq",  # or "dna_seq", "chip_seq"
    quality_threshold="high"
)
```

**Application-Specific Thresholds:**
- **RNA-seq**: Acceptable duplication up to 40% (transcript abundance)
- **DNA-seq**: Strict quality requirements (variant calling)
- **ChIP-seq**: Moderate quality, focus on enrichment metrics

## CLI Usage

```bash
# Analyze single report
python scripts/fastqc_interpreter.py --input sample_fastqc.html

# Batch analysis
python scripts/fastqc_interpreter.py --batch "*fastqc.html" --output report.pdf

# With custom thresholds
python scripts/fastqc_interpreter.py --input fastqc.html --application rna_seq
```

## Output Interpretation

**PASS (Green)**: Proceed with analysis
**WARNING (Yellow)**: Review but likely acceptable
**FAIL (Red)**: Requires action before downstream analysis

## Troubleshooting Guide

See `references/troubleshooting.md` for:
- Platform-specific issues (Illumina, PacBio, Oxford Nanopore)
- Library prep problem diagnosis
- Downstream analysis impact assessment

---

**Skill ID**: 205 | **Version**: 1.0 | **License**: MIT