A machine learning tool that predicts the risk of loan default using borrower financial data and outputs model probabilities and classifications from logistic regression, XGBoost, and a neural network.

Claude Code plugin for structured change management — risk classification (P0-P3), multi-domain collaborative analysis, adversarial spec review, implementation verification, and business impact analysis.

Guardrail API for detecting personalized mental-health advice with explainable safe/unsafe decisions. Docker-first, FastAPI, Cloud Run-ready, with auth, rate limits, and cost controls.

Experimental Claude Code skill for solo builders: structured prompts and reference checklists that surface the invisible production layer (security, GDPR, scaling, bus factor) of any build. No warranty; not legal or security advice.

Injury prediction model using machine learning to analyze factors like workload, player metrics, and environmental conditions. It identifies injury risk patterns early, enabling preventive actions, improved training decisions, and reduced injury occurrence in athletes.

LLM complaint insights: Structured summaries, risk tags, and entity extraction on CFPB consumer complaints — built with OpenAI + LangChain, MLflow, Streamlit.

This project uses a public vehicle trajectory dataset (proxy) to simulate real-time driving signals.

Enterprise IAM governance pipeline — maps 75 applications to IAM products, classifies risk, detects governance gaps, and produces management dashboards with remediation queues and department scorecards.

AI system registry and governance tracker with EU AI Act risk classification, impact assessments, and a compliance dashboard — built for legal and compliance teams.

Automated EU AI Act (2024/1689) compliance checker: classify risk, check obligations, generate audit-ready reports and dashboards.

120-Tool Autonomous AI Command Center - EU AI Act Compliance - Multi-Agent MCP + A2A Protocol - Nexus Control Center

Reference repository outlining protocol-level risk and liability classification concepts; taxonomy reference, not insurance or financial advice.

Research on systemic failures in AI applications identified through news media, exploring risk factors and mitigation taxonomies.

Machine learning-based mental health risk prediction system using XGBoost, Flask REST APIs, Docker, and behavioral analytics for early depression risk assessment.