Datanalysis Credit Risk

Name: Datanalysis Credit Risk
Author: github

Official

github·vlatest·data·MIT

Analyzes financial data to assess credit risk, providing insights for informed lending decisions and mitigating potential losses.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

Run this in your project

terminal

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

Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

github

Matched to the vendor's own GitHub organisation.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned 28 Sept 2026

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

Declares what it does: failed
No SKILL.md was found in the skill's directory.
SKILL.md exists, its frontmatter parses, and it declares a name and a description.
No undeclared network calls: passed
Makes no network calls.
Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.
No obfuscated payloads: passed
No encoded or obfuscated payloads.
No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.
No credential access: passed
Reads no keys, tokens, keychains or credential files.
Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.
No pipe-to-shell installers: passed
No pipe-to-shell or download-and-run installers.
No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.
Pinned to a commit: passed
Scanned github/awesome-copilot at 6c4d33b9cf.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit: 6c4d33b9cf
Manifest: skills/datanalysis-credit-risk/SKILL.md
Scanned: 0 files

This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.

What each check looks for →

About this skill

What it does

The datanalysis-credit-risk skill enables AI agents to analyze credit risk data, assess loan default probabilities, and generate insights based on financial metrics. It can process datasets containing borrower information, historical payment behavior, and economic indicators to identify patterns that may indicate potential defaults.

When to use it

Evaluating a new loan application by analyzing the applicant’s credit history and income stability.
Identifying high-risk customers in an existing portfolio for targeted risk mitigation strategies.
Generating reports on overall credit risk trends within a financial institution or market segment.

Key capabilities

Analyzes borrower data to predict default likelihood.
Processes historical payment behavior and economic indicators.
Generates insights and visualizations for decision-making.

Example prompts

"Analyze this dataset of loan applicants and identify those with the highest risk of default."
"Generate a report on credit risk trends based on the last five years of borrower data."
"What factors most strongly correlate with loan defaults in this dataset?"

Tips & gotchas

Ensure that your input data is clean, well-labeled, and includes relevant financial metrics for accurate analysis. The skill may require additional configuration or domain-specific tuning to achieve optimal results on complex datasets.