AutoGineer
Generated banner for Datanalysis Credit Risk
Generated logo for Datanalysis Credit Risk

Datanalysis Credit Risk

GitHubOfficialFirst committed by REAL-Madrid01Distributed by GitHub

  • Skill
  • Workflow
  • Data and AI
  • MIT
  • Low risk
  • Has scripts
Download ZIP

From the author

Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.
— the description by REAL-Madrid01

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a comprehensive, multi-step pipeline designed to prepare raw credit risk data for modeling. It automates essential preprocessing tasks including data formatting, outlier filtering, missing value analysis, and feature selection. The workflow is modular, allowing for independent execution of steps, and concludes by generating a detailed Excel report that documents the cleaning process, statistical distributions, and the rationale behind feature removal.

What it does

  • Loads and formats raw credit risk datasets.
  • Filters out abnormal time periods based on sample counts.
  • Calculates and filters features based on missing rates, IV, and PSI metrics.
  • Performs noise reduction using Null Importance denoising.
  • Removes redundant features based on high correlation thresholds.
  • Exports a comprehensive Excel report detailing all cleaning steps and statistics.

When to use it

  • When preparing raw credit data for pre-loan modeling.
  • When you need to perform systematic feature selection and quality assessment.
  • When you require a documented audit trail of data cleaning decisions.
  • When working with multi-organization datasets requiring specific sample analysis.

Advantages

  • Modular design allows for independent step execution without data loss.
  • Provides granular organization-level analysis and OOS data separation.
  • Supports multi-process acceleration for intensive IV and PSI calculations.
  • Generates a structured Excel report for transparency and review.

Usage examples

“Run the full credit risk data cleaning pipeline on my raw dataset.”
The agent executes the 11-step cleaning process, filtering features and samples according to default thresholds, and generates an Excel report.
“Filter out features with more than 50 percent missing values in my credit dataset.”
The agent applies the missing rate check step with the specified threshold and updates the dataset accordingly.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/github/datanalysis-credit-risk/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip datanalysis-credit-risk.zip -d .agents/skills

Documentation by REAL-Madrid01

Data Cleaning and Variable Screening

Quick Start

# Run the complete data cleaning pipeline
python ".github/skills/datanalysis-credit-risk/scripts/example.py"

Complete Process Description

The data cleaning pipeline consists of the following 11 steps, each executed independently without deleting the original data:

  1. Get Data - Load and format raw data

  2. Organization Sample Analysis - Statistics of sample count and bad sample rate for each organization

  3. Separate OOS Data - Separate out-of-sample (OOS) samples from modeling samples

  4. Filter Abnormal Months - Remove months with insufficient bad sample count or total sample count

  5. Calculate Missing Rate - Calculate overall and organization-level missing rates for each feature

  6. Drop High Missing Rate Features - Remove features with overall missing rate exceeding threshold

  7. Drop Low IV Features - Remove features with overall IV too low or IV too low in too many organizations

  8. Drop High PSI Features - Remove features with unstable PSI

  9. Null Importance Denoising - Remove noise features using label permutation method

  10. Drop High Correlation Features - Remove high correlation features based on original gain

  11. Export Report - Generate Excel report containing details and statistics of all steps

Core Functions

FunctionPurposeModule
get_dataset()Load and format datareferences.func
org_analysis()Organization sample analysisreferences.func
missing_check()Calculate missing ratereferences.func
drop_abnormal_ym()Filter abnormal monthsreferences.analysis
drop_highmiss_features()Drop high missing rate featuresreferences.analysis
drop_lowiv_features()Drop low IV featuresreferences.analysis
drop_highpsi_features()Drop high PSI featuresreferences.analysis
drop_highnoise_features()Null Importance denoisingreferences.analysis
drop_highcorr_features()Drop high correlation featuresreferences.analysis
iv_distribution_by_org()IV distribution statisticsreferences.analysis
psi_distribution_by_org()PSI distribution statisticsreferences.analysis
value_ratio_distribution_by_org()Value ratio distribution statisticsreferences.analysis
export_cleaning_report()Export cleaning reportreferences.analysis

Parameter Description

Data Loading Parameters

  • DATA_PATH: Data file path (best are parquet format)

  • DATE_COL: Date column name

  • Y_COL: Label column name

  • ORG_COL: Organization column name

  • KEY_COLS: Primary key column name list

OOS Organization Configuration

  • OOS_ORGS: Out-of-sample organization list

Abnormal Month Filtering Parameters

  • min_ym_bad_sample: Minimum bad sample count per month (default 10)

  • min_ym_sample: Minimum total sample count per month (default 500)

Missing Rate Parameters

  • missing_ratio: Overall missing rate threshold (default 0.6)

IV Parameters

  • overall_iv_threshold: Overall IV threshold (default 0.1)

  • org_iv_threshold: Single organization IV threshold (default 0.1)

  • max_org_threshold: Maximum tolerated low IV organization count (default 2)

PSI Parameters

  • psi_threshold: PSI threshold (default 0.1)

  • max_months_ratio: Maximum unstable month ratio (default 1/3)

  • max_orgs: Maximum unstable organization count (default 6)

Null Importance Parameters

  • n_estimators: Number of trees (default 100)

  • max_depth: Maximum tree depth (default 5)

  • gain_threshold: Gain difference threshold (default 50)

High Correlation Parameters

  • max_corr: Correlation threshold (default 0.9)

  • top_n_keep: Keep top N features by original gain ranking (default 20)

Output Report

The generated Excel report contains the following sheets:

  1. 汇总 - Summary information of all steps, including operation results and conditions

  2. 机构样本统计 - Sample count and bad sample rate for each organization

  3. 分离OOS数据 - OOS sample and modeling sample counts

  4. Step4-异常月份处理 - Abnormal months that were removed

  5. 缺失率明细 - Overall and organization-level missing rates for each feature

  6. Step5-有值率分布统计 - Distribution of features in different value ratio ranges

  7. Step6-高缺失率处理 - High missing rate features that were removed

  8. Step7-IV明细 - IV values of each feature in each organization and overall

  9. Step7-IV处理 - Features that do not meet IV conditions and low IV organizations

  10. Step7-IV分布统计 - Distribution of features in different IV ranges

  11. Step8-PSI明细 - PSI values of each feature in each organization each month

  12. Step8-PSI处理 - Features that do not meet PSI conditions and unstable organizations

  13. Step8-PSI分布统计 - Distribution of features in different PSI ranges

  14. Step9-null importance处理 - Noise features that were removed

  15. Step10-高相关性剔除 - High correlation features that were removed

Features

  • Interactive Input: Parameters can be input before each step execution, with default values supported

  • Independent Execution: Each step is executed independently without deleting original data, facilitating comparative analysis

  • Complete Report: Generate complete Excel report containing details, statistics, and distributions

  • Multi-process Support: IV and PSI calculations support multi-process acceleration

  • Organization-level Analysis: Support organization-level statistics and modeling/OOS distinction

Files

Requirements and compatibility

Written for: GitHub Copilot

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

What our scanner noticed· Low risk

  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

First committed by
GitHub avatar of REAL-Madrid01REAL-Madrid01
Publisher
GitHub (Official)
Distributed by
GitHub
Source
github/awesome-copilot · skills/datanalysis-credit-risk · commit 797337f
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Contributors
REAL-Madrid01, srpatcha

AutoGineer is not affiliated with or endorsed by REAL-Madrid01 or GitHub.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download datanalysis-credit-risk.zip