exploratory-data-analysis
Analyze Scientific Data Files Automatically
Also available from: davila7
Scientific data files come in hundreds of formats. This skill automatically detects file type, extracts metadata, assesses data quality, and generates comprehensive markdown reports with format-specific analysis recommendations.
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Using "exploratory-data-analysis". Analyze data/sample.fastq
Expected outcome:
- File: sample.fastq (24.5 MB)
- Format: FASTQ (sequence data with quality scores)
- Sampled 10,000 reads: Mean length 150bp, Mean quality: 35.2
- GC Content: 52.3%
- Quality Assessment: High-quality data, suitable for downstream analysis
- Recommendations: Proceed with alignment; no trimming required
Using "exploratory-data-analysis". Explore experiment_results.csv
Expected outcome:
- File: experiment_results.csv (1.2 MB)
- Format: CSV (tabular data)
- Dimensions: 5,000 rows x 12 columns
- Missing Values: 2.3% in column 'temperature'
- Statistics: Mean=45.2, Std=12.8, Range=[-5.2, 98.4]
- Recommendations: Impute missing values; check for outliers in temperature column
Security Audit
SafeAfter thorough evaluation of 1077 static findings, all are false positives. The scanner misinterpreted Markdown code formatting (backticks) as shell commands, bioinformatics format names (SAM) as Windows credentials, and documentation references to file format specifications as weak cryptography. The skill is a legitimate scientific data analysis tool that only reads data files and writes markdown reports. No network access, no command execution, and no sensitive data handling were found.
Risk Factors
📁 Filesystem access (1)
Quality Score
What You Can Build
Explore genomic sequencing data
Analyze FASTQ, BAM, and VCF files to understand sequence quality, mapping rates, and variant distributions.
Examine molecular structure files
Parse PDB, SDF, and CIF files to assess molecular structures, atomic coordinates, and bond information.
Inspect microscopy image metadata
Extract dimensions, channels, timestamps, and spatial calibration from TIFF, ND2, and CZI imaging files.
Try These Prompts
Analyze this scientific data file at path: <filepath>
Generate a comprehensive EDA report for this file and save it to <filepath>
Perform a data quality assessment on this file and identify any issues or anomalies.
Analyze these multiple related files and create a summary comparison report.
Best Practices
- Provide the full file path when requesting analysis for precise detection
- Specify output filename to generate a persistent markdown report
- Check required Python libraries for specialized formats before analysis
Avoid
- Do not ask the skill to modify or write back to source data files
- Do not expect the skill to perform advanced statistical modeling
- Do not assume the skill can interpret biological meaning from sequences