Pandas Dataframe Analyzer

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pandas-dataframe-analyzer

Pandas Dataframe Analyzer is an Agent Skill for documents published in the a5c-ai/babysitter repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Automated DataFrame analysis skill for statistical summaries, missing value detection, data type inference, and memory optimization recommendations.

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How to use this skill

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Inspect the Agent Skill at https://github.com/a5c-ai/babysitter/blob/HEAD/library/specializations/data-science-ml/skills/pandas-dataframe-analyzer/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/pandas-dataframe-analyzer, but wait for my approval before writing files or running scripts.

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pandas-dataframe-analyzer

Overview

Automated DataFrame analysis skill for statistical summaries, missing value detection, data type inference, and memory optimization recommendations using pandas and profiling libraries.

Capabilities

Statistical profiling of DataFrames
Missing value pattern detection
Data type optimization suggestions
Memory footprint analysis
Duplicate detection and handling
Distribution analysis and visualization
Correlation matrix computation
Cardinality analysis for categorical features

Target Processes

Exploratory Data Analysis (EDA) Pipeline
Data Collection and Validation Pipeline
Feature Engineering Design and Implementation

Tools and Libraries

pandas
pandas-profiling / ydata-profiling
numpy
scipy (for statistical tests)

Input Schema

{
  "type": "object",
  "required": ["dataPath"],
  "properties": {
    "dataPath": {
      "type": "string",
      "description": "Path to the data file (CSV, Parquet, JSON)"
    },
    "sampleSize": {
      "type": "integer",
      "description": "Number of rows to sample for analysis",
      "default": 10000
    },
    "profileType": {
      "type": "string",
      "enum": ["minimal", "standard", "full"],
      "default": "standard"
    },
    "outputFormat": {
      "type": "string",
      "enum": ["json", "html", "markdown"],
      "default": "json"
    }
  }
}

Output Schema

{
  "type": "object",
  "required": ["summary", "columns", "recommendations"],
  "properties": {
    "summary": {
      "type": "object",
      "properties": {
        "rowCount": { "type": "integer" },
        "columnCount": { "type": "integer" },
        "memoryUsageMB": { "type": "number" },
        "duplicateRows": { "type": "integer" },
        "missingCells": { "type": "integer" },
        "missingCellsPercent": { "type": "number" }
      }
    },
    "columns": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "name": { "type": "string" },
          "dtype": { "type": "string" },
          "nullCount": { "type": "integer" },
          "uniqueCount": { "type": "integer" },
          "stats": { "type": "object" }
        }
      }
    },
    "recommendations": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "type": { "type": "string" },
          "column": { "type": "string" },
          "suggestion": { "type": "string" },
          "impact": { "type": "string" }
        }
      }
    }
  }
}

Usage Example

{
  kind: 'skill',
  title: 'Analyze training dataset',
  skill: {
    name: 'pandas-dataframe-analyzer',
    context: {
      dataPath: 'data/train.csv',
      profileType: 'full',
      outputFormat: 'json'
    }
  }
}