Skills

Skill 126 of 200

Datanalysis Credit Risk

Credit risk data cleaning and variable screening pipeline for pre-loan modeling.

3 minutes · 628 words · 15 sections

Install

$ npx skills add github/awesome-copilot --skill datanalysis-credit-risk

$ npx skills add github/awesome-copilot

» /plugin marketplace add github/awesome-copilot

The first command installs just this skill, by the name in its SKILL.md; the second installs the whole repository.

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:

Get Data - Load and format raw data
Organization Sample Analysis - Statistics of sample count and bad sample rate for each organization
Separate OOS Data - Separate out-of-sample (OOS) samples from modeling samples
Filter Abnormal Months - Remove months with insufficient bad sample count or total sample count
Calculate Missing Rate - Calculate overall and organization-level missing rates for each feature
Drop High Missing Rate Features - Remove features with overall missing rate exceeding threshold
Drop Low IV Features - Remove features with overall IV too low or IV too low in too many organizations
Drop High PSI Features - Remove features with unstable PSI
Null Importance Denoising - Remove noise features using label permutation method
Drop High Correlation Features - Remove high correlation features based on original gain
Export Report - Generate Excel report containing details and statistics of all steps

Core Functions

Function	Purpose	Module
`get_dataset()`	Load and format data	references.func
`org_analysis()`	Organization sample analysis	references.func
`missing_check()`	Calculate missing rate	references.func
`drop_abnormal_ym()`	Filter abnormal months	references.analysis
`drop_highmiss_features()`	Drop high missing rate features	references.analysis
`drop_lowiv_features()`	Drop low IV features	references.analysis
`drop_highpsi_features()`	Drop high PSI features	references.analysis
`drop_highnoise_features()`	Null Importance denoising	references.analysis
`drop_highcorr_features()`	Drop high correlation features	references.analysis
`iv_distribution_by_org()`	IV distribution statistics	references.analysis
`psi_distribution_by_org()`	PSI distribution statistics	references.analysis
`value_ratio_distribution_by_org()`	Value ratio distribution statistics	references.analysis
`export_cleaning_report()`	Export cleaning report	references.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:

汇总 - Summary information of all steps, including operation results and conditions
机构样本统计 - Sample count and bad sample rate for each organization
分离OOS数据 - OOS sample and modeling sample counts
Step4-异常月份处理 - Abnormal months that were removed
缺失率明细 - Overall and organization-level missing rates for each feature
Step5-有值率分布统计 - Distribution of features in different value ratio ranges
Step6-高缺失率处理 - High missing rate features that were removed
Step7-IV明细 - IV values of each feature in each organization and overall
Step7-IV处理 - Features that do not meet IV conditions and low IV organizations
Step7-IV分布统计 - Distribution of features in different IV ranges
Step8-PSI明细 - PSI values of each feature in each organization each month
Step8-PSI处理 - Features that do not meet PSI conditions and unstable organizations
Step8-PSI分布统计 - Distribution of features in different PSI ranges
Step9-null importance处理 - Noise features that were removed
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

About this skill

Trigger

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 verbatim description from this skill’s front matter — the string an agent matches on to decide whether to load it.

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How this issue was made

Source

github.com/github/awesome-copilot, branch main, last pushed 24 September 2026.

Licence

MIT — the text of every skill is reproduced unmodified, frontmatter included, under the upstream licence.

Discovery

200 skills found by walking the repository tree for SKILL.md, not by matching a directory convention. 2 distinct layouts observed: .github/skills/*/SKILL.md, skills/*/SKILL.md.

Authorship

3 of 200 skills are installed into the repository rather than published from it. They are shelved in the closing “Credited skills” part and remain the work of their own authors. A skill counts as credited when its only copy lives under one agent’s dot directory; visible directories, per-agent mirror sets, and skills installable from this repository on skills.sh all count as published.

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Heading repairs

1 repair applied to this skill so the document has one h1 and no skipped levels:

Shifted “15 headings” from h1 to h2 so the skill title is the only h1.

Spec compliance

Every skill satisfies the Agent Skills specification’s frontmatter rules.

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A plugin manifest is published at .github/plugin/marketplace.json by GitHub, declaring 162 plugins. It is read for editorial metadata only — never as the skill index, which is always the repository tree.

Signal

Install counts come from skills.sh. They measure downloads, not quality, and an unranked repository is not an unread one.

Agent surfaces

The whole issue is available as one markdown document at /github/awesome-copilot.md, and each skill at its own .md URL.

Publication

Set by Skills Docs from the source repository. Body text is Literata at the reader’s chosen size and measure; code is Geist Mono. Nothing on this page was written by us except this paragraph.

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:

Get Data - Load and format raw data
Organization Sample Analysis - Statistics of sample count and bad sample rate for each organization
Separate OOS Data - Separate out-of-sample (OOS) samples from modeling samples
Filter Abnormal Months - Remove months with insufficient bad sample count or total sample count
Calculate Missing Rate - Calculate overall and organization-level missing rates for each feature
Drop High Missing Rate Features - Remove features with overall missing rate exceeding threshold
Drop Low IV Features - Remove features with overall IV too low or IV too low in too many organizations
Drop High PSI Features - Remove features with unstable PSI
Null Importance Denoising - Remove noise features using label permutation method
Drop High Correlation Features - Remove high correlation features based on original gain
Export Report - Generate Excel report containing details and statistics of all steps

Core Functions

Function	Purpose	Module
`get_dataset()`	Load and format data	references.func
`org_analysis()`	Organization sample analysis	references.func
`missing_check()`	Calculate missing rate	references.func
`drop_abnormal_ym()`	Filter abnormal months	references.analysis
`drop_highmiss_features()`	Drop high missing rate features	references.analysis
`drop_lowiv_features()`	Drop low IV features	references.analysis
`drop_highpsi_features()`	Drop high PSI features	references.analysis
`drop_highnoise_features()`	Null Importance denoising	references.analysis
`drop_highcorr_features()`	Drop high correlation features	references.analysis
`iv_distribution_by_org()`	IV distribution statistics	references.analysis
`psi_distribution_by_org()`	PSI distribution statistics	references.analysis
`value_ratio_distribution_by_org()`	Value ratio distribution statistics	references.analysis
`export_cleaning_report()`	Export cleaning report	references.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:

汇总 - Summary information of all steps, including operation results and conditions
机构样本统计 - Sample count and bad sample rate for each organization
分离OOS数据 - OOS sample and modeling sample counts
Step4-异常月份处理 - Abnormal months that were removed
缺失率明细 - Overall and organization-level missing rates for each feature
Step5-有值率分布统计 - Distribution of features in different value ratio ranges
Step6-高缺失率处理 - High missing rate features that were removed
Step7-IV明细 - IV values of each feature in each organization and overall
Step7-IV处理 - Features that do not meet IV conditions and low IV organizations
Step7-IV分布统计 - Distribution of features in different IV ranges
Step8-PSI明细 - PSI values of each feature in each organization each month
Step8-PSI处理 - Features that do not meet PSI conditions and unstable organizations
Step8-PSI分布统计 - Distribution of features in different PSI ranges
Step9-null importance处理 - Noise features that were removed
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

About this skill

Trigger

Colophon

Appendix 126.1–126.3

ReferencesPython · 2 files

ScriptsPython · 1 file

About this skill

Trigger

Colophon

Appendix 126.1–126.3

ReferencesPython · 2 files

ScriptsPython · 1 file