5 Repos
Frameworks that leverage multiple specialized AI models to provide analytical insights for complex forecasting.
Distinguishing note: Focuses on the synthesis of multiple agent outputs for high-stakes decision support, distinct from single-model inference.
Explore 5 awesome GitHub repositories matching artificial intelligence & ml · Decision Support Systems. Refine with filters or upvote what's useful.
TradingAgents is an autonomous financial research and simulation framework that coordinates specialized agents to analyze market data and execute investment strategies. The system functions as a multi-agent debate environment where independent units critique financial insights through structured, adversarial reasoning to improve decision accuracy and mitigate investment risks. The platform distinguishes itself through a risk-gated transaction pipeline that validates all proposed financial actions against market volatility and liquidity constraints before execution on a simulated exchange. To
Facilitating structured debates between specialized analytical models to improve the accuracy and reliability of complex financial forecasting.
This project is a career strategy knowledge base and education decision engine designed to optimize academic choices and professional trajectories. It functions as a socioeconomic analysis model and an LLM persona framework that uses structured mental models to evaluate university majors and career paths based on employment data and social mobility. The system differentiates itself by simulating expert decision-making styles and high-certainty communication patterns through specialized persona-based prompting. It incorporates a specific cognitive system that injects vocational heuristics and
Provides high-stakes decision support systems based on socioeconomic filtering and employment backtracking.
dbskill ist eine Sammlung spezialisierter Bibliotheken, die Prompt-Spezifikationen, Diagnose-Frameworks, Content-Toolsets und agentenverhaltensbezogene Anweisungen umfasst. Es bietet ein Wissensmanagementsystem, das einen Vier-Schichten-Engineering-Ansatz verwendet, um Fakten und Muster in einer verifizierbaren Entscheidungsbasis zu organisieren, ergänzt durch eine Skill-Bibliothek mit Prompt-Templates für Business-Diagnostik und Content-Engineering. Das Projekt bietet ein Business-Diagnose-Framework zur Analyse organisatorischer Engpässe und zur Optimierung von Geschäftsmodellen durch strukturierte Analyse. Es enthält ein Content-Engineering-Toolset zur Prüfung der Textresonanz und zur Generierung von Social-Media-Hooks mit hoher Conversion-Rate basierend auf viralen Formeln. Das System deckt ein breites Spektrum an Fähigkeiten ab, einschließlich KI-Agenten-Workflow-Engineering, Wettbewerbsmarktanalyse und Social-Media-Content-Formatierung. Es bietet zudem Tools für Entscheidungssystem-Engineering, interaktive Lernunterstützung und strukturierte Wissensintegration unter Verwendung von JSON-basierten Bibliotheken. Das System enthält Dienstprogramme für die Migration von Agentenkonfigurationen und Workbenches, um Projektregeln und Namenskonventionen über verschiedene KI-Agentenplattformen hinweg zu standardisieren.
Implements a four-layer structural engineering system to organize long-term knowledge for informed decision-making.
Viper is a command and control infrastructure manager and post-exploitation framework designed for adversary attack simulation and security assessment. It functions as an orchestrator for penetration testing, combining a system for managing compromised hosts across multiple operating systems with tools for security workflow automation. The platform is distinguished by its use of large language model agents to coordinate red team tasks, automate data processing, and provide intelligent decision support. It includes a network pivot visualizer that uses directional graphs to map relationships an
Uses large language model agents to provide analytical insights and intelligent decision support during security operations.
Agriculture Knowledge Graph is a structured triple-store system and decision support platform designed to transform raw agricultural documents into a machine-readable graph. It functions as a domain information retrieval system that extracts and queries agricultural data to provide intelligent answers and planning support. The project implements a full pipeline for knowledge graph construction, featuring a relation extraction framework and named entity recognition tools. It utilizes remote supervision and machine learning to identify and classify relationships between entities, converting uns
Provides data-driven insights for agricultural planning by querying a structured knowledge graph.