CognitiveOS Core is a web-based application for auditing and managing the cognitive state of AI agents. It provides a suite of tools for visualizing and managing symbolic scars, uncertainty reports, and computational historiography, as well as for designing and managing Meta-Product-Requirements Prompts (Meta-PRPs).

• Features
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Components
• Types

• Meta-PRP Designer: A tool for designing and managing Meta-Product-Requirements Prompts (Meta-PRPs).
• Symbolic Scar Manager: A tool for managing "Symbolic Scars," which are records of past interpretive failures.
• Uncertainty Reports: A tool for viewing reports generated when the AI expresses uncertainty.
• Computational Historiography: A tool for tracing an AI agent's reasoning pathways.
• Epistemic Budget Forecaster: A tool for forecasting the cognitive cost and potential for error for a given query.

• Node.js (v14 or later)
• npm

1. Clone the repository:

   git clone https://github.com/your-username/cognitive-os-core.git

2. Install the dependencies:

   npm install

3. Start the development server:

   npm run dev

Once the development server is running, you can access the application at http://localhost:3000.

The application is divided into five main views:

• Meta-PRP Designer: This view allows you to design and manage Meta-Product-Requirements Prompts (Meta-PRPs). You can create, rename, and delete profiles, as well as edit the core mission, governance parameters, and self-optimization goals for each profile.
• Symbolic Scar Manager: This view allows you to manage "Symbolic Scars," which are records of past interpretive failures. You can view a list of active scars, set decay timers, and override scars.
• Uncertainty Reports: This view allows you to view reports generated when the AI expresses uncertainty. You can view a list of recent reports, including the query that triggered the uncertainty, the uncertainty level, the reason for the uncertainty, and the timestamp of the report.
• Computational Historiography: This view allows you to trace an AI agent's reasoning pathways. You can view a reasoning trace for the last complex query, including the premise, inferences, scar influences, and conclusion.
• Epistemic Budget Forecaster: This view allows you to forecast the cognitive cost and potential for error for a given query. You can enter a query and view the forecasted epistemic budget cost, waste friction, and rationale.

The application is built using React and TypeScript. The following is a list of the main components:

• App.tsx: The main application component.
• Card.tsx: A reusable card component.
• ComputationalHistoriography.tsx: A component that displays the computational historiography view.
• EpistemicBudgetForecaster.tsx: A component that provides a real-time forecast of the cognitive cost and potential for error for a given query.
• Header.tsx: The header component for the application.
• MetaPRPDesigner.tsx: A component for designing and managing Meta-Product-Requirements Prompts (Meta-PRPs).
• Modal.tsx: A reusable modal component.
• Sidebar.tsx: The sidebar component for the application.
• SymbolicScarManager.tsx: A component for managing symbolic scars.
• Tooltip.tsx: A reusable tooltip component.
• UncertaintyReports.tsx: A component that displays a list of uncertainty reports.

The application uses the following types:

• View: An enum that defines the different views available in the application.
• CognitiveMode: An enum that defines the different cognitive modes for the AI.
• SymbolicScar: An interface that represents a "Symbolic Scar," a record of a past interpretive failure.
• UncertaintyReport: An interface that represents a report generated when the AI expresses uncertainty.
• ReasoningStep: An interface that represents a single step in an AI's reasoning process.
• AgentConfig: An interface that represents the configuration for an AI agent.