Credit risk data cleaning and variable screening pipeline for pre-loan modeling.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondatanalysis-credit-riskExecute the skills CLI command in your project's root directory to begin installation:
Fetches datanalysis-credit-risk from github/awesome-copilot and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate datanalysis-credit-risk. Access via /datanalysis-credit-risk in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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# Run the complete data cleaning pipeline
python ".github/skills/datanalysis-credit-risk/scripts/example.py"
The data cleaning pipeline consists of the following 11 steps, each executed independently without deleting the original data:
| 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 |
DATA_PATH: Data file path (best are parquet format)DATE_COL: Date column nameY_COL: Label column nameORG_COL: Organization column nameKEY_COLS: Primary key column name listOOS_ORGS: Out-of-sample organization listmin_ym_bad_sample: Minimum bad sample count per month (default 10)min_ym_sample: Minimum total sample count per month (default 500)missing_ratio: Overall missing rate threshold (default 0.6)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_threshold: PSI threshold (default 0.1)max_months_ratio: Maximum unstable month ratio (default 1/3)max_orgs: Maximum unstable organization count (default 6)n_estimators: Number of trees (default 100)max_depth: Maximum tree depth (default 5)gain_threshold: Gain difference threshold (default 50)max_corr: Correlation threshold (default 0.9)top_n_keep: Keep top N features by original gain ranking (default 20)The generated Excel report contains the following sheets:
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
github/awesome-copilot
github/awesome-copilot
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
datanalysis-credit-risk reduced setup friction for our internal harness; good balance of opinion and flexibility.
datanalysis-credit-risk has been reliable in day-to-day use. Documentation quality is above average for community skills.
datanalysis-credit-risk fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
datanalysis-credit-risk is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
datanalysis-credit-risk fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
datanalysis-credit-risk has been reliable in day-to-day use. Documentation quality is above average for community skills.
datanalysis-credit-risk reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added datanalysis-credit-risk from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: datanalysis-credit-risk is focused, and the summary matches what you get after install.
datanalysis-credit-risk has been reliable in day-to-day use. Documentation quality is above average for community skills.
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