parser-generator

Solid

Expert skill for parser generation and implementation using LL, LR, LALR, PEG, and Pratt parsing techniques

AI & Automation 814 stars 53 forks Updated today MIT

Install

View on GitHub

Quality Score: 93/100

Stars 20%
97
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
90
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# Parser Generator Skill ## Overview Expert skill for parser generation and implementation using LL, LR, LALR, PEG, and Pratt parsing techniques. ## Capabilities - Generate parsers from grammar specifications (ANTLR, Bison, tree-sitter) - Implement recursive descent parsers with predictive parsing - Implement Pratt parsers for expression handling - Generate LALR/GLR parse tables - Implement PEG parsers with packrat memoization - Handle grammar conflicts (shift-reduce, reduce-reduce) - Generate concrete syntax trees (CST) and AST transformations - Implement operator precedence parsing ## Target Processes - parser-development.js - language-grammar-design.js - ast-design.js - lsp-server-implementation.js ## Dependencies - ANTLR4 - tree-sitter - Bison/Yacc ## Usage Guidelines 1. **Grammar Analysis**: Analyze grammar class requirements (LL(k), LALR, etc.) before selecting parser type 2. **Conflict Resolution**: Document and resolve all shift-reduce/reduce-reduce conflicts explicitly 3. **Error Recovery**: Implement synchronization points for robust error recovery 4. **AST Construction**: Design AST node types before implementing production actions 5. **Expression Parsing**: Use Pratt parsing for complex expression precedence handling ## Output Schema ```json { "type": "object", "properties": { "parserType": { "type": "string", "enum": ["recursive-descent", "pratt", "lalr", "glr", "peg", "ll"] }, "grammarClass": { "type": "string" }, "con...

Details

Author
a5c-ai
Repository
a5c-ai/babysitter
Created
4 months ago
Last Updated
today
Language
JavaScript
License
MIT

Related Skills