Parser Generator

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

509 stars

Best use case

Parser Generator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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

Teams using Parser Generator should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/parser-generator/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/programming-languages/skills/parser-generator/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/parser-generator/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How Parser Generator Compares

Feature / AgentParser GeneratorStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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" },
    "conflicts": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "type": { "type": "string" },
          "resolution": { "type": "string" }
        }
      }
    },
    "generatedFiles": {
      "type": "array",
      "items": { "type": "string" }
    }
  }
}
```

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