How do I install datanalysis-credit-risk?

Run `npx skills add https://github.com/github/awesome-copilot --skill datanalysis-credit-risk` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does datanalysis-credit-risk support?

datanalysis-credit-risk works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is datanalysis-credit-risk free to use?

Yes. datanalysis-credit-risk is free to install and use. It is available from the open explainx.ai skill registry published by github.

Where can I read ratings and reviews for datanalysis-credit-risk?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install datanalysis-credit-risk?

Run `npx skills add https://github.com/github/awesome-copilot --skill datanalysis-credit-risk` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does datanalysis-credit-risk support?

datanalysis-credit-risk works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is datanalysis-credit-risk free to use?

Yes. datanalysis-credit-risk is free to install and use. It is available from the open explainx.ai skill registry published by github.

Where can I read ratings and reviews for datanalysis-credit-risk?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install datanalysis-credit-risk?

Run `npx skills add https://github.com/github/awesome-copilot --skill datanalysis-credit-risk` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does datanalysis-credit-risk support?

datanalysis-credit-risk works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is datanalysis-credit-risk free to use?

Yes. datanalysis-credit-risk is free to install and use. It is available from the open explainx.ai skill registry published by github.

Where can I read ratings and reviews for datanalysis-credit-risk?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install datanalysis-credit-risk?

Run `npx skills add https://github.com/github/awesome-copilot --skill datanalysis-credit-risk` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does datanalysis-credit-risk support?

datanalysis-credit-risk works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is datanalysis-credit-risk free to use?

Yes. datanalysis-credit-risk is free to install and use. It is available from the open explainx.ai skill registry published by github.

Where can I read ratings and reviews for datanalysis-credit-risk?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install datanalysis-credit-risk?

Run `npx skills add https://github.com/github/awesome-copilot --skill datanalysis-credit-risk` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does datanalysis-credit-risk support?

datanalysis-credit-risk works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is datanalysis-credit-risk free to use?

Yes. datanalysis-credit-risk is free to install and use. It is available from the open explainx.ai skill registry published by github.

Where can I read ratings and reviews for datanalysis-credit-risk?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install datanalysis-credit-risk?

Run `npx skills add https://github.com/github/awesome-copilot --skill datanalysis-credit-risk` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does datanalysis-credit-risk support?

datanalysis-credit-risk works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is datanalysis-credit-risk free to use?

Yes. datanalysis-credit-risk is free to install and use. It is available from the open explainx.ai skill registry published by github.

Where can I read ratings and reviews for datanalysis-credit-risk?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install datanalysis-credit-risk?

Run `npx skills add https://github.com/github/awesome-copilot --skill datanalysis-credit-risk` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does datanalysis-credit-risk support?

datanalysis-credit-risk works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is datanalysis-credit-risk free to use?

Yes. datanalysis-credit-risk is free to install and use. It is available from the open explainx.ai skill registry published by github.

Where can I read ratings and reviews for datanalysis-credit-risk?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install datanalysis-credit-risk?

Run `npx skills add https://github.com/github/awesome-copilot --skill datanalysis-credit-risk` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does datanalysis-credit-risk support?

datanalysis-credit-risk works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is datanalysis-credit-risk free to use?

Yes. datanalysis-credit-risk is free to install and use. It is available from the open explainx.ai skill registry published by github.

Where can I read ratings and reviews for datanalysis-credit-risk?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install datanalysis-credit-risk?

Run `npx skills add https://github.com/github/awesome-copilot --skill datanalysis-credit-risk` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does datanalysis-credit-risk support?

datanalysis-credit-risk works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is datanalysis-credit-risk free to use?

Yes. datanalysis-credit-risk is free to install and use. It is available from the open explainx.ai skill registry published by github.

Where can I read ratings and reviews for datanalysis-credit-risk?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install datanalysis-credit-risk?

Run `npx skills add https://github.com/github/awesome-copilot --skill datanalysis-credit-risk` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does datanalysis-credit-risk support?

datanalysis-credit-risk works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is datanalysis-credit-risk free to use?

Yes. datanalysis-credit-risk is free to install and use. It is available from the open explainx.ai skill registry published by github.

Where can I read ratings and reviews for datanalysis-credit-risk?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

How do I install datanalysis-credit-risk?

Run `npx skills add https://github.com/github/awesome-copilot --skill datanalysis-credit-risk` in your terminal. You need to have run `npx skills init` once in your project first.

Productivity

datanalysis-credit-risk▌

github/awesome-copilot · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/github/awesome-copilot --skill datanalysis-credit-risk

0 commentsdiscussion

summary

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

›Executes 11 independent steps covering data loading, abnormal period filtering, missing rate analysis, low-IV and high-PSI variable removal, null importance denoising, and correlation-based feature elimination
›Supports organization-level analysis with separate modeling and out-of-sample (OOS) sample handling, plus multi-process acceleration for IV and PSI calculations
›Generates comprehensive Excel report

skill.md

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

how to use datanalysis-credit-risk

How to use datanalysis-credit-risk on Cursor

AI-first code editor with Composer

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add datanalysis-credit-risk

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/github/awesome-copilot --skill datanalysis-credit-risk

The skills CLI fetches datanalysis-credit-risk from GitHub repository github/awesome-copilot and configures it for Cursor.

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?

│

│ ── Universal (.agents/skills) ── always included ────

│ • Amp

│ • Antigravity

│ • Cline

│ • Codex

│ ●Cursor(selected)

│ • Cursor

│ • Windsurf

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/datanalysis-credit-risk

Reload or restart Cursor to activate datanalysis-credit-risk. Access the skill through slash commands (e.g., /datanalysis-credit-risk) or your agent's skill management interface.

⚠

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

Additional Resources

›View source code on GitHub ›Skills CLI documentation ›Learn more about Cursor ›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases▌

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

✓

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

✓

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

✓

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

✓

Save 3-5 hours/week on communication overhead

Implementation Guide▌

Prerequisites

›Claude Desktop or compatible AI client
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

1.Install product management skill
2.Start with user story generation for known feature
3.Progress to competitive analysis: research 2-3 competitors
4.Use for roadmap prioritization: apply RICE/ICE scoring
5.Draft stakeholder communications and refine based on feedback
6.Build template library for recurring PM tasks
7.Share effective prompts with product team

Common Pitfalls

⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements

Best Practices▌

✓ Do

+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition

✗ Don't

−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture

💡 Pro Tips

★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs

When to Use This▌

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path▌

1Basic: user stories, feature specs, status updates
2Intermediate: competitive analysis, prioritization frameworks, PRDs
3Advanced: product strategy, go-to-market planning, OKR setting
4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)

No comments yet — start the thread.

general reviews

Ratings

4.5★★★★★41 reviews

★★★★★Advait Ramirez· Dec 8, 2024
datanalysis-credit-risk reduced setup friction for our internal harness; good balance of opinion and flexibility.
★★★★★Camila Abebe· Nov 27, 2024
datanalysis-credit-risk has been reliable in day-to-day use. Documentation quality is above average for community skills.
★★★★★Luis Li· Oct 18, 2024
datanalysis-credit-risk fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
★★★★★Olivia Desai· Sep 25, 2024
datanalysis-credit-risk is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
★★★★★Piyush G· Sep 21, 2024
datanalysis-credit-risk fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
★★★★★Mateo Desai· Sep 17, 2024
datanalysis-credit-risk has been reliable in day-to-day use. Documentation quality is above average for community skills.
★★★★★Mateo Kapoor· Sep 5, 2024
datanalysis-credit-risk reduced setup friction for our internal harness; good balance of opinion and flexibility.
★★★★★Mateo Dixit· Aug 24, 2024
We added datanalysis-credit-risk from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
★★★★★Soo Shah· Aug 16, 2024
Solid pick for teams standardizing on skills: datanalysis-credit-risk is focused, and the summary matches what you get after install.
★★★★★Shikha Mishra· Aug 12, 2024
datanalysis-credit-risk has been reliable in day-to-day use. Documentation quality is above average for community skills.

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