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data-diagnostics

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Run data quality checks on a CSV file. Reports shape, types, missing values, summary statistics, outlier flags, and duplicates without modifying the source file.

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Source SKILL.md: https://github.com/sischei/Deep_Learning_for_Solving_And_Estimating_Dynamic_Economic_Models/blob/HEAD/lectures/lecture_06_agentic_programming/code/skills/example_skill/SKILL.md

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/data-diagnostics

Description

Run data quality checks on a CSV file. Reports shape, types, missing values, summary statistics, outlier flags, and duplicate detection. Outputs a concise diagnostic report without modifying the source file.

Instructions

You are a data quality analyst. When the user invokes /data-diagnostics, perform the following steps on the CSV file they specify.

Step 1: Identify the target file

  • If the user provided a file path, use that.
  • If not, look for CSV files in data/ and ask which one to check.
  • NEVER modify the source file.

Step 2: Load and inspect

  • Load the CSV with pandas.read_csv().
  • Print the following header block: 
    === Data Diagnostics: <filename> ===
Shape: (rows, cols)
Memory: X.X MB

Step 3: Column-level report

For each column, report:

  • dtype (int, float, string, datetime)
  • n_missing and pct_missing
  • n_unique (flag if n_unique == n_rows, likely an ID column)
  • For numeric columns: mean, std, min, p25, median, p75, max
  • For string columns: top 5 most frequent values with counts
  • For datetime columns: min date, max date, gaps

Step 4: Data quality flags

Check and report:

  • Any column with >20% missing values
  • Any numeric column where max/min ratio > 1000 (possible outliers)
  • Any duplicate rows (report count)
  • Any constant columns (zero variance)
  • Any column name with spaces or special characters
  • Row count sanity: is it suspiciously round (e.g., exactly 1000)?

Step 5: Output

  • Print the full report to stdout in plain text.
  • Save a copy to notes/diagnostics_<filename>_<date>.md.
  • End with a one-paragraph summary of the most important findings.

Constraints

  • Do NOT modify the source CSV file under any circumstances.
  • Do NOT drop rows or impute missing values.
  • Use pandas for loading; numpy for computations.
  • If the file is larger than 100MB, warn the user and sample 10,000 rows.

Example usage

> /data-diagnostics data/synthetic_panel.csv

Expected output:

=== Data Diagnostics: synthetic_panel.csv ===
Shape: (5000, 6)
Memory: 0.2 MB

Column: firm_id
  dtype: int64 | missing: 0 (0.0%) | unique: 500
  min=1  p25=126  median=250  p75=375  max=500

Column: year
  dtype: int64 | missing: 0 (0.0%) | unique: 10
  min=2010  p25=2012  median=2014  p75=2017  max=2019

...

Quality flags:
  [PASS] No columns with >20% missing
  [PASS] No extreme outliers detected
  [PASS] No duplicate rows
  [PASS] No constant columns
  [PASS] Column names are clean

Summary: Clean balanced panel of 500 firms over 10 years.
No missing values or quality issues detected. Ready for analysis.