A modular framework for building semantic-aware code transpilers and migration scaffolds.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install universal-code-converter
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install universal-code-converter using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Universal Code Converter?
The Universal Code Converter is a sophisticated workflow within the Openclaw Skills ecosystem designed for building source-to-source code translation pipelines. Unlike basic text-processing tools, this skill emphasizes semantic fidelity and structural integrity by utilizing a multi-stage approach. It guides developers through parsing source code into Concrete Syntax Trees (CST), normalizing the data, and mapping it to a robust Intermediate Representation (IR) before lowering it into the target language.
This workflow is essential for teams managing large-scale migrations or building custom transpilers. By focusing on diagnostic-driven development, it ensures that semantic gaps—such as differences in async handling or memory management—are explicitly handled rather than ignored. It provides the architectural blueprint needed to deliver reliable, behavior-preserving code conversion tools.
Universal Code Converter Use Cases
- Developing automated code porting tools for legacy system modernization.
- Architecting custom transpilers that maintain semantic equivalence across languages.
- Validating multi-language code conversion strategies using Tree-sitter grammars.
- Designing language-agnostic intermediate representations for deep code analysis.
How Universal Code Converter Works
- Scope definition involves identifying source/target constraints, runtime requirements, and representative code snippets.
- Parser verification ensures active Tree-sitter grammars are utilized for reliable frontend processing and node inspection.
- Pipeline separation divides the process into explicit stages: parse, normalize, semantic enrichment, IR, lowering, emit, and validate.
- IR design focuses on modeling program semantics like declarations and control flow rather than simple surface syntax.
- Vertical slice implementation starts with a narrow feature set (e.g., functions and literals) to prove the end-to-end pipeline logic.
- Validation is performed at multiple levels including fixture-based execution tests and snapshot regression coverage for the IR.
Universal Code Converter Setup
To begin using this skill in your Openclaw Skills environment, set up the required parser bindings and the recommended project structure:
# Example: Install tree-sitter and a specific language grammar
npm install tree-sitter tree-sitter-javascript
# Scaffolding the recommended project architecture
mkdir -p src/{frontends,normalization,semantic,ir,lowering,emitters,diagnostics} tests/{fixtures,golden}
Universal Code Converter Data Schema & Taxonomy
| Component | Description |
|---|---|
| frontends/ | Manages grammar bindings and parser-probe artifacts including root nodes and field names |
| ir/ | Defines the language-neutral semantic model for declarations, bindings, and control flow |
| diagnostics/ | Handles structured nodes or status flags for unsupported or lossy language constructs |
| emitters/ | Centralizes target language logic including naming, escaping, and formatting rules |
| tests/fixtures/ | Stores representative source code and golden-master outputs for regression testing |
Universal Code Converter Advanced Features
- Incremental reparse support utilizing Tree-sitter editing frontends for high-performance processing.
- Snapshot testing capabilities for both intermediate representation structures and final emitted code.
- Explicit diagnostic emitters that flag behavior changes or semantic drift during conversion.
- Typed target-language emitter APIs to ensure structural correctness over raw string manipulation.
SKILL.md
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