Integrates the Quint formal specification language into LLM workflows for accessible formal verification. It provides tools for type-checking, random simulation, exhaustive model checking, and syntax documentation.
Enables AI assistants to identify and judge unexplained terms in prose documents by providing differential linting, term context, and ledger management.
Provides deterministic, verifiable text/code/measurement utilities for AI agents, enabling tasks like unit conversion, citation formatting, diffing, proofreading, readability scoring, and syntax checking with re-executable proof.
Provides a quality framework and enforceable conventions for AI coding assistants, ensuring code quality, environment hygiene, and project standards across multiple AI tools.
Brings rubber duck debugging to AI-powered IDEs by providing a tool for articulating problems and clarifying logic in natural language. It helps developers and AI agents reveal hidden assumptions and surface solutions through structured self-explanation and reflection.
Automatically collects, summarizes, and documents code from vibe coding sessions, generating multiple document types (README, DESIGN, TUTORIAL, etc.) and publishing them to platforms like Notion, GitHub Wiki, and Obsidian.
