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Distributable packages containing parsing logic for custom or experimental language syntax.
Distinguishing note: Focuses on the packaging and distribution of parsing logic rather than the implementation of the parser.
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This project is a static analysis engine designed to identify patterns, enforce coding standards, and automate code quality improvements in software projects. By parsing source code into structured abstract syntax trees, it enables deep programmatic inspection and the automated remediation of identified programming issues. The engine functions as a pluggable linting framework, allowing developers to extend its core capabilities through a modular architecture. Users can inject custom rules, parsers, and processors to support non-standard file formats or domain-specific logic. This extensibilit
Package parsing logic as a module for installation and reference in configuration files to enable analysis of custom or experimental language syntax.
This project provides an integration of Tree-sitter into the Neovim editor to enable structural code analysis. It serves as a framework for structural code navigation, context-aware syntax highlighting, and the management of language-specific parsers. The system distinguishes itself through a multi-language injection handler that identifies and parses embedded languages within a single document. It uses a dedicated parser manager to install, update, and remove grammar definitions and their associated query files. The tool covers several capability areas including incremental text selection,
Downloads and compiles external grammar definitions into binary modules for the editor to execute.
TrumpScript is a Python-based domain specific language and compiler extension that wraps the Python runtime to enforce custom grammar and vocabulary rules. It transforms a specialized, case-insensitive vocabulary and natural speech patterns into executable Python instructions. The implementation distinguishes itself through strict constraints on source code, including a variable name system that restricts identifiers to a predefined whitelist and a numeric parser that rejects any integer not exceeding one million. It further utilizes a token-filtering preprocessor to remove filler words and n
Maps a specialized, spoken-style vocabulary into instructions compatible with the Python runtime.
This project is a comprehensive Lisp AI implementation library that provides reference implementations for various artificial intelligence paradigms and symbolic algorithms. It functions as a multi-purpose toolkit containing a logic programming engine, a natural language processing suite, and a symbolic mathematics toolkit. The library is distinguished by its diverse architectural frameworks, including a Prolog-style execution engine that uses unification and goal-driven backtracking, and a system for simulating human decision-making through expert system shells and certainty factors. It also
Translates high-level grammar rules into executable functions to eliminate runtime interpretation overhead.
Lark este un toolkit de parsare Python utilizat pentru a defini gramatici și a converti textul brut în arbori de parsare adnotați. Servește drept generator de arbori de sintaxă abstractă și un limbaj de definire a gramaticii pentru specificarea regulilor limbajului prin terminale și expresii regulate. Biblioteca oferă două implementări principale de parsare: o bibliotecă de parsare Earley capabilă să gestioneze toate limbajele context-free, inclusiv cele cu ambiguitate și recursivitate la stânga, și o bibliotecă de parsare LALR de înaltă performanță concepută pentru limbaje deterministe cu un consum redus de memorie. Dincolo de parsarea de bază, toolkit-ul include capabilități pentru compoziția gramaticală modulară, transformarea arborilor bazată pe reguli și urmărirea coordonatelor pentru pozițiile sursă. De asemenea, suportă serializarea gramaticilor LALR în module de parsare independente.
Provides the ability to import terminals and rules from multiple external grammar files to build complex parsing logic.
Pest este o bibliotecă de parsare pentru Rust și un generator automat de parsere care transformă definițiile de gramatică formală în parsere funcționale. Se specializează în Parsing Expression Grammar (PEG) pentru a recunoaște și structura tipare complexe de text, oferind un sistem pentru parsarea gramaticilor libere de context. Biblioteca implementează tokenizarea de tip zero-copy și compilarea statică a gramaticii pentru a reduce overhead-ul la runtime. Suportă compatibilitatea no-std, permițând compilarea parserului pentru medii embedded sau bare-metal unde biblioteca standard nu este disponibilă. Proiectul acoperă o gamă de capabilități de parsare, inclusiv extragerea perechilor de token-uri imbricate și validarea sintactică automatizată. Este utilizat pentru implementarea de limbaje specifice domeniului (DSL), parsarea de limbaje personalizate și evaluarea expresiilor matematice. De asemenea, oferă raportarea automată a erorilor pentru a identifica token-uri neașteptate sau input-uri lipsă.
Transforms formal grammar definitions into optimized internal representations during the build process.
Nearley is a JavaScript parser toolkit used to define context-free grammars and generate corresponding parsers. It features an EBNF grammar compiler that transforms language definitions written in extended Backus-Naur Form into executable JavaScript code, utilizing an Earley parser implementation to process any context-free grammar. The toolkit distinguishes itself by its ability to handle left-recursion and ambiguity without failing, allowing it to identify and return multiple valid derivations for a single input string. It also includes a grammar fuzzing generator to produce random strings
Transforms EBNF grammar definitions into executable JavaScript modules to process input text efficiently.
Nearley is a JavaScript parser toolkit and context-free grammar compiler. It functions as an Earley parser generator that transforms BNF-like grammar definitions into executable code capable of analyzing text and generating abstract syntax trees. The project is distinguished by its ability to handle any context-free grammar, including those with left recursion and ambiguity, by maintaining multiple valid derivations for a single input. It further supports incremental parsing, allowing input strings to be processed in chunks to provide partial results and real-time feedback. Beyond core parsi
Translates high-level grammar definitions into executable JavaScript functions for high-performance runtime parsing.
SBJson is an Objective-C JSON parser and generator designed for the parsing and generation of JSON data. It functions as a strict JSON validator, enforcing rigid grammar rules to ensure input data adheres to formal specifications. The project features an incremental JSON stream parser that processes UTF8 data in chunks to extract documents without loading the entire payload into memory. It also serves as a JSON data serializer that transforms native data objects into formatted strings using deterministic key sorting. The system manages data serialization workflows and implements security mea
Enforces rigid JSON grammar rules to reject any input that deviates from formal specifications.