Authoritative Data Harvester
yushui2022/MathModel-Skill
Finds authoritative public data sources for modeling tasks, prefers official APIs and bulk downloads, and outputs a reproducible fetch and cleaning plan with citations.
Cleans raw credit data and screens variables before loan modeling, dropping unstable, noisy or redundant features and writing an Excel report of every step.
Install Credit Risk Data Cleaning
$ npx skills add github/awesome-copilot --skill datanalysis-credit-risk -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install github/awesome-copilot datanalysis-credit-risk --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/datanalysis-credit-risk .claude/skills/datanalysis-credit-risk && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "datanalysis-credit-risk" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/datanalysis-credit-risk into .claude/skills/datanalysis-credit-risk/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datanalysis-credit-risk", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/github/awesome-copilot/tree/main/skills/datanalysis-credit-riskType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add github/awesome-copilot --skill datanalysis-credit-risk -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install github/awesome-copilot datanalysis-credit-risk --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/datanalysis-credit-risk .agents/skills/datanalysis-credit-risk && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "datanalysis-credit-risk" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/datanalysis-credit-risk into .agents/skills/datanalysis-credit-risk/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datanalysis-credit-risk", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add github/awesome-copilot --skill datanalysis-credit-risk -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install github/awesome-copilot datanalysis-credit-risk --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/datanalysis-credit-risk .cursor/skills/datanalysis-credit-risk && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "datanalysis-credit-risk" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/datanalysis-credit-risk into .cursor/skills/datanalysis-credit-risk/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datanalysis-credit-risk", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/github/awesome-copilot.git --path skills/datanalysis-credit-risk--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add github/awesome-copilot --skill datanalysis-credit-risk -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install github/awesome-copilot datanalysis-credit-risk --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/datanalysis-credit-risk .gemini/skills/datanalysis-credit-risk && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "datanalysis-credit-risk" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/datanalysis-credit-risk into .gemini/skills/datanalysis-credit-risk/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datanalysis-credit-risk", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install github/awesome-copilot datanalysis-credit-riskInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add github/awesome-copilot --skill datanalysis-credit-risk -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/datanalysis-credit-risk .github/skills/datanalysis-credit-risk && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "datanalysis-credit-risk" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/datanalysis-credit-risk into .github/skills/datanalysis-credit-risk/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datanalysis-credit-risk", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add github/awesome-copilot --skill datanalysis-credit-risk -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install github/awesome-copilot datanalysis-credit-risk --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/datanalysis-credit-risk .opencode/skills/datanalysis-credit-risk && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "datanalysis-credit-risk" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/datanalysis-credit-risk into .opencode/skills/datanalysis-credit-risk/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datanalysis-credit-risk", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
datanalysis-credit-risk- GitHub stars
- 40k
- Used in
- 1 other repo
- Token cost
- ~1.5k tokens
- SKILL.md length
- 620 words
- Files
- 4 (incl. scripts, references)
- Skills in repo
- 417
- Repo updated
- First seen
- Licence
- MIT
At a glance
Cleans raw credit data and screens variables before loan modeling, dropping unstable, noisy or redundant features and writing an Excel report of every step.
- Works in 11 steps: Get Data - Load and format raw data → Organization Sample Analysis -… → Separate OOS Data - Separate… → …
- Preparing raw credit data before building a pre-loan model
- SKILL.md covers Quick Start, Complete Process Description, Core Functions and Parameter Description, plus 2 more sections
- Runs Python scripts from its folder; calls python
What it does
The pipeline runs eleven steps in order and never deletes the original data. It loads and formats the raw records, analyzes samples by organization, separates out-of-sample records, filters months with too few samples, computes missing rates, and then removes features that fail each check.
Feature filters cover high missing rate, low information value (IV), unstable PSI, noise found by shuffling labels in a Null Importance test, and high correlation. The last step exports an Excel report with details and statistics for every stage. A quick-start script, scripts/example.py, runs the whole flow, and reusable functions such as get_dataset, missing_check and drop_lowiv_features sit in the references folder.
When your agent uses it
- Preparing raw credit data before building a pre-loan model
- Screening candidate variables by missing rate, IV and PSI stability
- Removing noisy or highly correlated features before modeling
- Producing a cleaning report that lists what was dropped at each step
Example prompts
- “Run the credit risk cleaning pipeline on the loan applications dataset and give me the Excel report.”
- “Which features in our pre-loan data have too many missing values or unstable PSI?”
- “Drop low-IV and highly correlated variables before I fit the scorecard.”
Requirements
- Python, to run the bundled scripts
Workflow steps
11 steps, taken from the first numbered list in SKILL.md.
- 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
What it can do on your machine
Read from SKILL.md and the folder at commit 727ff2e. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Credit Risk Data Cleaning loads about 1.5k tokens when it runs, and up to ~17k if it reads all its reference files. Until then it costs about 153 tokens; SKILL.md has 620 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from github/awesome-copilot at commit 727ff2e, republished under its MIT licence (© github). 620 words, ~1,546 tokens.
Download SKILL.mdSave it as
.claude/skills/datanalysis-credit-risk/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- datanalysis-credit-risk
- description
- 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.
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 nameY_COL: Label column nameORG_COL: Organization column nameKEY_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)
Show full SKILL.md (257 more words)Show less
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
© github, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (scripts, references) in skills/datanalysis-credit-risk of github/awesome-copilot.
- SKILL.md
- references/analysis.py
- references/func.py
- scripts/example.py
Open the folder on GitHubat commit 727ff2e
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in github/awesome-copilot, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Credit Risk Data Cleaning next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Credit Risk Data Cleaning this skillgithub/awesome-copilot | 40k | 1 repos | ~1.5k | Automated safety check: Pass | MIT | |
| Authoritative Data Harvesteryushui2022/MathModel-Skill | 452 | 1 repos | ~1.1k | Automated safety check: Pass | MIT | |
| Data Quality Frameworkswshobson/agents | 40k | 10 repos | ~1.1k | Automated safety check: Pass | MIT | |
| Ingesting Dataancoleman/ai-design-components | 526 | — | ~1.9k | Automated safety check: Pass | MIT | |
| Bio Batch ProcessingGPTomics/bioSkills | 1.2k | 1 repos | ~3k | Automated safety check: Pass | MIT | |
| Data Cleaningmagnus919/agent-skills | 111 | — | ~2.1k | Automated safety check: Pass | MIT |
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Questions about Credit Risk Data Cleaning
What does Credit Risk Data Cleaning do?
Cleans raw credit data and screens variables before loan modeling, dropping unstable, noisy or redundant features and writing an Excel report of every step. The pipeline runs eleven steps in order and never deletes the original data. It loads and formats the raw records, analyzes samples by organization, separates out-of-sample records, filters months with too few samples, computes missing rates, and then removes features that fail each check.
When should I use Credit Risk Data Cleaning?
Credit Risk Data Cleaning fits situations like: preparing raw credit data before building a pre-loan model; screening candidate variables by missing rate, IV and PSI stability; removing noisy or highly correlated features before modeling; producing a cleaning report that lists what was dropped at each step.
How do I install Credit Risk Data Cleaning in Claude Code?
Run `npx skills add github/awesome-copilot --skill datanalysis-credit-risk -a claude-code`. Or copy the skill folder (skills/datanalysis-credit-risk in github/awesome-copilot) into .claude/skills/datanalysis-credit-risk in your project. Claude Code loads it when a task matches its description.
How do I install Credit Risk Data Cleaning in Codex?
Run `npx skills add github/awesome-copilot --skill datanalysis-credit-risk -a codex`. Or copy the skill folder (skills/datanalysis-credit-risk in github/awesome-copilot) into .agents/skills/datanalysis-credit-risk in your project. Codex loads it when a task matches its description.
Can I use Credit Risk Data Cleaning in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add github/awesome-copilot --skill datanalysis-credit-risk -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/datanalysis-credit-risk, .gemini/skills/datanalysis-credit-risk, .github/skills/datanalysis-credit-risk and .opencode/skills/datanalysis-credit-risk in your project.
What does Credit Risk Data Cleaning need to run?
Going by SKILL.md and its folder, Credit Risk Data Cleaning needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python, to run the bundled scripts.
Does Credit Risk Data Cleaning access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Credit Risk Data Cleaning safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Credit Risk Data Cleaning use?
Credit Risk Data Cleaning is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Credit Risk Data Cleaning use?
About 1.5k tokens (SKILL.md is roughly 6.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 16k tokens, read only when the agent opens those files.
What are the alternatives to Credit Risk Data Cleaning?
Skills that share tags, products or a category with Credit Risk Data Cleaning: Authoritative Data Harvester (yushui2022/MathModel-Skill, 452 stars), Data Quality Frameworks (wshobson/agents, 40k stars), Ingesting Data (ancoleman/ai-design-components, 526 stars) and Bio Batch Processing (GPTomics/bioSkills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Credit Risk Data Cleaning?
github (a GitHub organization, an official publisher) maintains it in github/awesome-copilot, which has 39,748 GitHub stars. The repository holds 417 skills in this directory. The repository was last updated on October 7, 2026.
Source: github/awesome-copilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.