This repository is a community-driven research project that maintains structured telemetry coverage matrices for major EDR (Endpoint Detection and Response) products across Windows, Linux, and macOS. It currently tracks 25+ vendors with approximately 100 telemetry sub-categories mapped to MITRE ATT&CK data sources. The primary data artifacts are three JSON files (EDR_telem_windows.json, EDR_telem_linux.json, EDR_telem_macOS.json) that are manually curated through pull requests, each with evidence requirements. The project provides a scoring system that computes weighted vendor scores based on coverage, an automated statistics pipeline that generates historical change events, vendor score timeseries, and contributor summaries, and platform-specific telemetry generators that produce test events for validation purposes. While the core asset is the curated dataset, the repository also contains reusable software components including a modular scoring model, a diff engine for snapshot comparison, and a PR evidence classifier. The project has a substantial community with nearly 2000 stars, active issue triage, and continuous updates through GitHub Actions workflows that regenerate statistics and synchronize data to a Supabase database.
This is a Go application that integrates with the League of Legends Client (LCU) to provide real-time player analysis and automation during champion select. It monitors the game flow via WebSocket, fetches recent match history for all team members, calculates a composite score based on KDA, first blood, multi-kills, team participation, gold, damage, vision, and CS metrics, then sends 'horse' ratings (e.g., ้ๅคฉไปฃ, ๅฐไปฃ) to the in-game chat. It also supports auto-accepting games, auto-picking/banning champions, and exposes an HTTP API for querying scores and configuration. The architecture is a monolithic Go service with a Gin web server, background goroutines for LCU monitoring, and a reverse proxy for the LCU API. It uses SQLite for local config storage and OpenTelemetry for logging. The code is well-structured, with clean separation of concerns, modern Go practices, and a complex scoring algorithm that is configurable via remote or local configuration.