nightscout-platform-entrypoint
nightscout-platform-entrypoint is a foundational skill for Nightscout development, outlining essential tasks and tools for working with the platform.
Browse and install thousands of AI Agent skills in the Killer-Skills directory. Supports Claude Code, Windsurf, Cursor, and more.
nightscout-platform-entrypoint is a foundational skill for Nightscout development, outlining essential tasks and tools for working with the platform.
The goal of this AI agent is to generate personalised and rich UI components based on the user prompt, chat history and backend data provided by other agents in your AI assistant.
frontend-to-backend-requirements is a mode for frontend developers to communicate data needs to backend, focusing on what data is required without specifying implementation details.
A full-stack web app using Next.js and Tailwind CSS for the frontend, Python backend, and Neon PostgreSQL for the database. It also features an AI chatbot for real-time user interaction, ensuring scalability and high performance.
Provide actionable treatment recommendations for cancer patients based on molecular profile. Interprets tumor mutations, identifies FDA-approved therapies, finds resistance mechanisms, matches clinical trials. Use when oncologist asks about treatment options for specific mutations (EGFR, KRAS, BRAF, etc.), therapy resistance, or clinical trial eligibility.
ADP is an intelligent data platform that bridges the gap between heterogeneous data sources and AI agents. It abstracts data complexity through business knowledge networks (Ontology), provides unified data access (VEGA), and orchestrates data processing through DataFlow pipelines.
etl-duckdb is an AI Agent skill that automates Extract, Transform, Load (ETL) workflows using a PowerShell script. It processes data inputs to generate a DuckDB database file and a detailed Markdown report containing metrics like input rows and null cells.
NEXUS AI: Fair & Auditable Credit Scoring An advanced credit decisioning system bringing transparency and governance to AI lending. Features a Random Forest model trained on Indian datasets, cryptographic audit logging, and a real-time "Glassmorphism" executive dashboard. Built with Python, FastAPI, and Streamlit.