Lexer Generator
SkillDev toolsExpert skill for generating and hand-writing lexers using DFA-based, table-driven, and recursive approaches
Use Lexer Generator in Claude, ChatGPT or Ahel Desktop
Free. Sign in, add Lexer Generator and connect your AI. About a minute.
Also: Claude Code · Cursor · Codex
Then ask your AI: use the Lexer Generator skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/programming-languages/skills/lexer-generator/SKILL.md and read by Ahel’s review.
Overview
Expert skill for generating and hand-writing lexers using various approaches including DFA-based lexers, table-driven lexers, and hand-written recursive lexers.
Capabilities
- Generate lexer from regular expression specifications
- Implement maximal munch tokenization
- Handle Unicode character classes and normalization
- Implement efficient keyword recognition (tries, perfect hashing)
- Support incremental/resumable lexing for IDE integration
- Generate lexer tables and state machines
- Handle lexer modes and contexts (e.g., string interpolation)
- Implement error recovery with skip-to-next strategies
Target Processes
- lexer-implementation.js
- language-grammar-design.js
- lsp-server-implementation.js
- repl-development.js
Dependencies
- Flex-like generators
- RE2/Hyperscan libraries
Usage Guidelines
- Token Definition: Start by defining the complete set of tokens with their regex patterns
- Maximal Munch: Always implement maximal munch to handle ambiguous token boundaries
- Unicode Support: Consider Unicode normalization forms and character classes from the start
- Error Recovery: Implement skip-to-next-valid strategies for robust error handling
- Performance: Use table-driven approaches for large token sets, hand-written for simple lexers
Output Schema
{
"type": "object",
"properties": {
"tokens": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": { "type": "string" },
"pattern": { "type": "string" },
"priority": { "type": "integer" }
}
}
},
"lexerType": {
"type": "string",
"enum": ["dfa", "table-driven", "hand-written"]
},
"generatedFiles": {
"type": "array",
"items": { "type": "string" }
}
}
}
Signals
- GitHub stars
- 2k
- Forks
- 113
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
lexer-generator- Source
- github.com/a5c-ai/babysitter