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/exploratory-data-analysis

@2fe0cfa

Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

Use this Skill: https://skilld.dev/gh/davila7/claude-code-templates/exploratory-data-analysis

This session only. Nothing lands on disk.

assetsreport_template.md

≈872 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Exploratory Data Analysis Report: {FILENAME}

Generated: {TIMESTAMP}


Executive Summary

This report provides a comprehensive exploratory data analysis of the file {FILENAME}. The analysis includes file type identification, format-specific metadata extraction, data quality assessment, and recommendations for downstream analysis.


Basic Information

  • Filename: {FILENAME}
  • Full Path: {FILEPATH}
  • File Size: {FILE_SIZE_HUMAN} ({FILE_SIZE_BYTES} bytes)
  • Last Modified: {MODIFIED_DATE}
  • Extension: .{EXTENSION}
  • Format Category: {CATEGORY}

File Type Details

Format Description

{FORMAT_DESCRIPTION}

Typical Data Content

{TYPICAL_DATA}

Common Use Cases

{USE_CASES}

Python Libraries for Reading

{PYTHON_LIBRARIES}


Data Structure Analysis

Overview

{DATA_STRUCTURE_OVERVIEW}

Dimensions

{DIMENSIONS}

Data Types

{DATA_TYPES}


Quality Assessment

Completeness

  • Missing Values: {MISSING_VALUES}
  • Data Coverage: {COVERAGE}

Validity

  • Range Check: {RANGE_CHECK}
  • Format Compliance: {FORMAT_COMPLIANCE}
  • Consistency: {CONSISTENCY}

Integrity

  • Checksum/Validation: {VALIDATION}
  • File Corruption Check: {CORRUPTION_CHECK}

Statistical Summary

Numerical Variables

{NUMERICAL_STATS}

Categorical Variables

{CATEGORICAL_STATS}

Distributions

{DISTRIBUTIONS}


Data Characteristics

Temporal Properties (if applicable)

  • Time Range: {TIME_RANGE}
  • Sampling Rate: {SAMPLING_RATE}
  • Missing Time Points: {MISSING_TIMEPOINTS}

Spatial Properties (if applicable)

  • Dimensions: {SPATIAL_DIMENSIONS}
  • Resolution: {SPATIAL_RESOLUTION}
  • Coordinate System: {COORDINATE_SYSTEM}

Experimental Metadata (if applicable)

  • Instrument: {INSTRUMENT}
  • Method: {METHOD}
  • Sample Info: {SAMPLE_INFO}

Key Findings

  1. Data Volume: {DATA_VOLUME_FINDING}
  2. Data Quality: {DATA_QUALITY_FINDING}
  3. Notable Patterns: {PATTERNS_FINDING}
  4. Potential Issues: {ISSUES_FINDING}

Visualizations

Distribution Plots

{DISTRIBUTION_PLOTS}

Correlation Analysis

{CORRELATION_PLOTS}

Time Series (if applicable)

{TIMESERIES_PLOTS}


Recommendations for Further Analysis

Immediate Actions

  1. {RECOMMENDATION_1}
  2. {RECOMMENDATION_2}
  3. {RECOMMENDATION_3}

Preprocessing Steps

  • {PREPROCESSING_1}
  • {PREPROCESSING_2}
  • {PREPROCESSING_3}

Analytical Approaches

{ANALYTICAL_APPROACHES}

Tools and Methods

  • Recommended Software: {RECOMMENDED_SOFTWARE}
  • Statistical Methods: {STATISTICAL_METHODS}
  • Visualization Tools: {VIZ_TOOLS}

Data Processing Workflow

{WORKFLOW_DIAGRAM}

Potential Challenges

  1. Challenge: {CHALLENGE_1}

    • Mitigation: {MITIGATION_1}
  2. Challenge: {CHALLENGE_2}

    • Mitigation: {MITIGATION_2}

References and Resources

Format Specification

  • {FORMAT_SPEC_LINK}

Python Libraries Documentation

  • {LIBRARY_DOCS}

Related Analysis Examples

  • {EXAMPLE_LINKS}

Appendix

Complete File Metadata

{COMPLETE_METADATA}

Analysis Parameters

{ANALYSIS_PARAMETERS}

Software Versions

  • Python: {PYTHON_VERSION}
  • Key Libraries: {LIBRARY_VERSIONS}

This report was automatically generated by the exploratory-data-analysis skill. For questions or issues, refer to the skill documentation.

Source: SKILL.md on GitHub

1 warning17d5 checks · Risk SAFE
  • Gen Agent Trust Hub17d

    The skill provides comprehensive exploratory data analysis for over 200 scientific file formats. It involves an indirect prompt injection surface when processing untrusted file metadata and recommends installing numerous third-party scientific libraries to handle specialized formats.

  • Socket17d

    No alerts

  • Snyk17d

    Risk: LOW · No issues

  • Runlayer7mo

    9/9 files flagged

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Signed by skilld at 2fe0cfa. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

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Activeupdated 10 months ago

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