Nebula is a local-first security assessment workbench designed for penetration testing and red teaming operations. It functions as a desktop application built with a Tauri shell (Rust) wrapping a React/TypeScript frontend and a Python FastAPI backend sidecar. The system manages security engagements, scope policies, evidence collection, and AI-assisted analysis, emphasizing operator control, auditability, and data privacy through local-first execution and content-addressed artifact storage. The architecture follows a sidecar pattern where the Python Core service handles database operations (SQLite with Alembic), containerized command execution (Kali Linux via Docker/Podman), and AI provider integration. The frontend manages the UI state and terminal sessions via WebSocket. Key data flows involve user actions triggering API calls that validate against scope policies before executing commands in isolated containers, with all outputs stored as immutable artifacts for evidence integrity. The codebase demonstrates strong engineering practices with extensive test coverage (Pytest, Vitest, Playwright), a robust diagnostic system with secret redaction, and a policy engine for deterministic scope enforcement. It supports multiple AI providers and includes features for knowledge indexing, mission orchestration, and report generation. The project is actively maintained with recent commits and zero security alerts, though it is currently in alpha release.
This repository provides an all-in-one CLI/TUI application for launching and managing security testing tools. It features a curated catalog of 215+ tools across 21 categories, defined declaratively in YAML files and loaded into Python tool classes. The interactive REPL supports commands like /run, /search, /tags, /ai, /goal, and /find, with a rich console interface using prompt_toolkit and Rich. A headless engagement mode runs YAML-defined pipelines (e.g., subfinder → httpx → nuclei) and generates structured findings and Markdown reports. AI features are opt-in and use OpenAI-compatible endpoints or local Ollama, with built-in safety guardrails (charter injection, sanitization, anti-fabrication checks) and graceful degradation to offline behavior. The architecture is monolithic but modular, with clear separation between the tool catalog, execution engine, AI integration, and discovery module. The codebase demonstrates strong engineering practices: safe subprocess execution (list-form, cwd, skip if present), declarative YAML overlays, a fixed tag taxonomy enforced by tests, and extensive test coverage including monkeypatching of subprocess and AI transport. While the catalog inherently drifts as upstream tools become archived, the project includes audit scripts to manage this. Overall, it is a well-crafted, production-quality application for authorized security testing.
Ui.Vision is a browser extension for Robotic Process Automation (RPA) that provides web and desktop automation capabilities. It features a React/Redux UI with Ant Design, supporting both table-based macros (Selenium IDE compatible) and JavaScript script macros. The extension includes computer vision for image-based automation, OCR functionality, AI-assisted macro creation, and desktop automation through native XModules. It implements a storage abstraction layer supporting browser and file system modes, backup/restore functionality, and an MCP bridge for external AI agent control. The architecture follows web extension patterns with background scripts, content scripts, and a rich UI layer, communicating via IPC mechanisms.
gpt-review is a Python CLI tool that integrates OpenAI/Azure OpenAI models to assist with software development workflows. It provides three main functionalities: asking questions to GPT models (with optional file/context), reviewing GitHub pull requests and posting AI-generated comments, and generating git commit messages based on staged changes. The application uses a modular architecture with separate components for different functionalities: question answering (_ask.py), PR review (_review.py), git operations (_git.py), document indexing (_llama_index.py), and API wrappers (_openai.py). It leverages the knack library for CLI structure, GitPython for git operations, Requests for GitHub API calls, and LlamaIndex for context-aware queries. The tool supports both direct OpenAI API and Azure OpenAI with automatic fallback to Azure Key Vault for credentials.