lowcode
ExtensionFreelowcode tool, support ChatGPT and other LLM
Capabilities8 decomposed
llm-powered crud scaffolding from visual mockups
Medium confidenceAccepts screenshot input of admin dashboards, list pages, or form layouts, performs OCR-based field extraction to identify table headers and form regions, then generates complete CRUD operation code templates (create, read, update, delete) by sending the extracted structure to ChatGPT or configured LLM. The extension parses the LLM response and outputs TypeScript/JavaScript code with proper typing and component bindings for frameworks like Formily or form-render.
Combines OCR-based visual field extraction with LLM code generation in a single VS Code workflow, allowing developers to generate CRUD scaffolding directly from UI screenshots rather than writing field definitions manually or using separate design-to-code tools.
Faster than manual CRUD scaffolding and more integrated than standalone design-to-code tools because it operates within VS Code and directly outputs code to the editor, eliminating context-switching and copy-paste overhead.
json-to-typescript type generation with mock data
Medium confidenceAccepts JSON objects or API response samples and generates corresponding TypeScript interface definitions with proper typing. The extension also generates mock data matching the schema, enabling developers to test components and services without live API calls. Supports bidirectional conversion (JSON to TS and TS to JSON) and integrates with mock data generation for rapid prototyping.
Provides bidirectional JSON↔TypeScript conversion with integrated mock data generation, allowing developers to maintain type definitions and test fixtures in sync without external tools or CLI commands.
More integrated than standalone tools like json-to-ts.com because it operates within the editor and generates mock data alongside types, reducing the need for separate mocking libraries or manual fixture creation.
chinese-to-english variable naming and translation
Medium confidenceAnalyzes Chinese variable names, field labels, or code comments and generates semantically appropriate English equivalents using LLM inference. Supports camelCase conversion for JavaScript/TypeScript naming conventions and can translate entire Chinese code blocks or comments to English. The extension sends Chinese text to the configured LLM and applies naming convention transformations to the response.
Integrates LLM-based translation with automatic naming convention transformation (Chinese → camelCase English), enabling single-command conversion of Chinese identifiers to international standards without manual case conversion.
More context-aware than regex-based camelCase converters because it uses LLM inference to generate semantically meaningful English names rather than phonetic transliteration, producing more readable and maintainable code.
yapi schema-to-typescript code generation
Medium confidenceIntegrates with YApi (a popular Chinese API documentation and mock data platform) by importing API schemas directly into VS Code. The extension fetches API definitions from YApi endpoints, parses the schema, and generates TypeScript interfaces, request/response types, and API client code. Supports automatic mock data generation from YApi mock configurations.
Provides direct YApi integration within VS Code, allowing developers to import and generate TypeScript code from YApi schemas without leaving the editor or using separate CLI tools.
More seamless than manual schema copying or external code generation because it maintains a live connection to YApi and can regenerate types when schemas are updated, reducing synchronization overhead.
llm-configurable code generation with multi-provider support
Medium confidenceProvides a pluggable LLM configuration system supporting ChatGPT, OpenAI, Gemini, and other LLM providers through a unified interface. Developers configure API keys and model parameters in VS Code settings, and the extension routes all code generation requests through the selected provider. Supports custom prompts and system instructions for domain-specific code generation.
Abstracts LLM provider selection into a unified configuration interface, allowing developers to swap between ChatGPT, OpenAI, Gemini, and other providers without modifying code or extension logic.
More flexible than single-provider extensions because it supports multiple LLM backends, enabling teams to optimize for cost, latency, or model capabilities without being locked into one provider.
form and table configuration generation from screenshots
Medium confidencePerforms OCR on screenshots of form layouts and data tables, extracts field names, types, and validation rules from visual elements, then generates JSON configuration objects compatible with form-render or Formily libraries. The extension identifies input types (text, select, date, etc.) from visual cues and generates corresponding schema definitions.
Combines OCR field extraction with intelligent type inference to generate form-render and Formily configurations directly from screenshots, eliminating manual schema definition for common form patterns.
More efficient than manual form configuration because it extracts field definitions from visual designs automatically, reducing the time to generate working form schemas from hours to minutes.
inline code generation with editor context awareness
Medium confidenceIntegrates with VS Code's editor context to provide inline code generation suggestions and completions. The extension analyzes the current file, selected code, and surrounding context, then sends requests to the configured LLM to generate relevant code snippets, function implementations, or template expansions. Supports command-palette triggered generation and potentially inline suggestions.
Provides LLM-powered code generation directly within the VS Code editor using local file context, avoiding the need for external code generation tools or copy-paste workflows.
More integrated than standalone code generation tools because it operates within the editor and has access to the current file context, enabling more relevant and contextual code suggestions.
mock data generation from json schemas
Medium confidenceAccepts JSON schema definitions or TypeScript interfaces and generates realistic mock data matching the schema structure. The extension creates sample data with appropriate types, ranges, and formats (e.g., valid email addresses, phone numbers, dates) for testing and development. Supports bulk mock data generation for arrays and nested structures.
Generates mock data directly from JSON schemas or TypeScript interfaces within VS Code, eliminating the need for separate mocking libraries or external tools for basic test data generation.
More convenient than manual mock data creation or external tools because it generates data inline in the editor and maintains synchronization with schema definitions.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓Full-stack developers building admin dashboards and CRUD interfaces
- ✓Teams using Formily or form-render component libraries
- ✓Developers prototyping low-code admin panels who want AI-assisted scaffolding
- ✓TypeScript developers building type-safe frontend applications
- ✓Teams integrating with REST APIs and needing rapid type generation
- ✓Developers prototyping features before backend APIs are finalized
- ✓Chinese developers working on international projects with English naming conventions
- ✓Teams maintaining codebases with mixed Chinese and English identifiers
Known Limitations
- ⚠OCR accuracy depends on screenshot clarity and field label legibility — handwritten or low-contrast designs may fail
- ⚠LLM context window limits the complexity of layouts that can be processed in a single request
- ⚠Generated code requires manual review and integration — not production-ready without developer refinement
- ⚠No built-in support for custom component libraries beyond Formily and form-render
- ⚠Complex nested structures with circular references may not generate optimal types
- ⚠Union types and discriminated unions require manual refinement after generation
Requirements
Input / Output
UnfragileRank
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