Lexer Generator
Expert skill for generating and hand-writing lexers using DFA-based, table-driven, and recursive approaches
Best use case
Lexer Generator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Expert skill for generating and hand-writing lexers using DFA-based, table-driven, and recursive approaches
Teams using Lexer 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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/lexer-generator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Lexer Generator Compares
| Feature / Agent | Lexer Generator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Expert skill for generating and hand-writing lexers using DFA-based, table-driven, and recursive approaches
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
# Lexer Generator Skill
## 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
1. **Token Definition**: Start by defining the complete set of tokens with their regex patterns
2. **Maximal Munch**: Always implement maximal munch to handle ambiguous token boundaries
3. **Unicode Support**: Consider Unicode normalization forms and character classes from the start
4. **Error Recovery**: Implement skip-to-next-valid strategies for robust error handling
5. **Performance**: Use table-driven approaches for large token sets, hand-written for simple lexers
## Output Schema
```json
{
"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" }
}
}
}
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