Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add boshi-xixixi/TraeSkill --skill datanalysis-credit-riskgit clone --depth 1 https://github.com/boshi-xixixi/TraeSkillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/boshi-xixixi/traeskill/datanalysis-credit-risk)<a href="https://agentmods.dev/skills/boshi-xixixi/traeskill/datanalysis-credit-risk"><img src="https://agentmods.dev/badge/skills/boshi-xixixi/traeskill/datanalysis-credit-risk.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00111 | $0.01354 |
| Opus 5 | $0.00056 | $0.00677 |
| Sonnet 5 | $0.00022 | $0.00271 |
| Haiku 4.5 | $0.00011 | $0.00135 |
Grade A, and why
datanalysis-credit-risk scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 114 lines · 111 tokens per session scan A c54807b18fa6
datanalysis-credit-risk is a skill published in the GitHub repository boshi-xixixi/TraeSkill (262 stars, last pushed 3mo ago), licensed MIT. It adds 111 tokens to every session and 1,354 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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