{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-wjkang-lowcode","slug":"lowcode","name":"lowcode","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=wjkang.lowcode","page_url":"https://unfragile.ai/lowcode","categories":["app-builders"],"tags":["AI","amis","ChatGPT","formily","form-render","Gemini","generate code","GPT","json","json mock","json to ts","json to typescript","LLM","lowcode","low-code","mock","openai","ts mock","ts to json","typescript mock","typescript to json","yapi","yapi to ts"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-wjkang-lowcode__cap_0","uri":"capability://code.generation.editing.llm.powered.crud.scaffolding.from.visual.mockups","name":"llm-powered crud scaffolding from visual mockups","description":"Accepts 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.","intents":["Generate boilerplate CRUD code from a screenshot of a desired admin interface without manually typing field definitions","Quickly scaffold form and table components with correct field names and types extracted from visual design","Reduce repetitive code generation for list pages and detail forms in enterprise applications"],"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"],"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"],"requires":["VS Code extension installed from marketplace","API key for ChatGPT, OpenAI, or configured alternative LLM","Screenshot or image file of the desired UI layout","Formily or form-render library in the target project (optional, for component-specific output)"],"input_types":["image (PNG, JPG screenshot of UI mockup)","visual selection (query regions, table headers identified via OCR)"],"output_types":["TypeScript/JavaScript code","JSON configuration objects","Component template code"],"categories":["code-generation-editing","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-wjkang-lowcode__cap_1","uri":"capability://code.generation.editing.json.to.typescript.type.generation.with.mock.data","name":"json-to-typescript type generation with mock data","description":"Accepts 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.","intents":["Generate TypeScript interfaces from JSON API responses to enable type-safe code","Create mock data fixtures that match a TypeScript schema for testing and development","Convert between JSON and TypeScript representations without manual type definition"],"best_for":["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"],"limitations":["Complex nested structures with circular references may not generate optimal types","Union types and discriminated unions require manual refinement after generation","Mock data generation is basic — does not support custom generators or realistic data patterns","No integration with OpenAPI/Swagger specs for automatic type generation from API documentation"],"requires":["VS Code extension installed","Valid JSON sample or TypeScript interface in the editor","No external API key required for this capability (local processing)"],"input_types":["JSON object","JSON array","TypeScript interface definition"],"output_types":["TypeScript interface","TypeScript type alias","JSON mock data"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-wjkang-lowcode__cap_2","uri":"capability://text.generation.language.chinese.to.english.variable.naming.and.translation","name":"chinese-to-english variable naming and translation","description":"Analyzes 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.","intents":["Convert Chinese variable names to English camelCase for international code standards","Translate Chinese form labels and field names to English for multilingual applications","Generate English comments and documentation from Chinese code explanations"],"best_for":["Chinese developers working on international projects with English naming conventions","Teams maintaining codebases with mixed Chinese and English identifiers","Developers building multilingual applications with Chinese-first design"],"limitations":["LLM-based translation may produce non-idiomatic English variable names requiring manual review","Context-dependent translations (e.g., domain-specific terms) may not match team conventions","No support for custom naming dictionaries or team-specific terminology","Requires API calls to LLM for each translation, introducing latency"],"requires":["VS Code extension installed","API key for ChatGPT or configured LLM","Chinese text selection in the editor"],"input_types":["Chinese text","Chinese variable names","Chinese comments"],"output_types":["English text","camelCase variable names","English comments"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-wjkang-lowcode__cap_3","uri":"capability://code.generation.editing.yapi.schema.to.typescript.code.generation","name":"yapi schema-to-typescript code generation","description":"Integrates 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.","intents":["Generate TypeScript types directly from YApi API documentation without manual copying","Create API client code with proper request/response typing from YApi schemas","Synchronize frontend types with backend API definitions stored in YApi"],"best_for":["Chinese development teams using YApi for API documentation and mocking","Full-stack teams needing synchronized API contracts between frontend and backend","Developers building TypeScript clients for APIs documented in YApi"],"limitations":["YApi integration is specific to YApi platform — no support for other API documentation tools (Swagger, Postman, etc.)","Requires YApi instance to be accessible from the developer's network","Generated client code may require customization for authentication, error handling, and interceptors","No automatic type updates when YApi schemas change — requires manual re-generation"],"requires":["VS Code extension installed","YApi instance URL and project/API credentials","Network access to YApi server"],"input_types":["YApi project ID","YApi API endpoint reference"],"output_types":["TypeScript interface","API client code","Request/response types"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-wjkang-lowcode__cap_4","uri":"capability://tool.use.integration.llm.configurable.code.generation.with.multi.provider.support","name":"llm-configurable code generation with multi-provider support","description":"Provides 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.","intents":["Switch between different LLM providers (OpenAI, Gemini, etc.) without changing extension code","Configure custom system prompts to generate code matching team conventions and patterns","Use cost-effective or specialized LLM models for different code generation tasks"],"best_for":["Teams evaluating multiple LLM providers for code generation quality and cost","Organizations with specific LLM provider requirements or compliance constraints","Developers building custom code generation workflows with specialized prompts"],"limitations":["LLM provider selection is global — cannot route different capabilities to different providers in a single workflow","API key management is manual — no built-in secret management or credential rotation","Custom prompt configuration requires editing VS Code settings JSON — no UI for prompt management","No built-in fallback mechanism if primary LLM provider is unavailable"],"requires":["VS Code extension installed","API key for at least one supported LLM provider (OpenAI, Gemini, etc.)","VS Code settings configuration (settings.json)"],"input_types":["LLM provider name","API key","Model name","Custom system prompt"],"output_types":["Configuration applied to all code generation requests"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-wjkang-lowcode__cap_5","uri":"capability://image.visual.form.and.table.configuration.generation.from.screenshots","name":"form and table configuration generation from screenshots","description":"Performs 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.","intents":["Generate form configuration JSON from a screenshot of a designed form layout","Create table column definitions and data schemas from screenshots of data tables","Rapidly prototype form and table configurations without manually defining schemas"],"best_for":["Developers building forms and tables with form-render or Formily","Teams prototyping admin interfaces and data entry forms","Low-code developers who want to accelerate form scaffolding from designs"],"limitations":["OCR accuracy depends on screenshot quality and field label clarity — complex or stylized designs may fail","Extracted field types are inferred from visual appearance — may require manual correction for non-standard input types","Validation rules are not extracted from screenshots — must be added manually","Generated configurations are basic — advanced features like conditional fields or custom validators require manual implementation"],"requires":["VS Code extension installed","Screenshot or image file of form/table layout","form-render or Formily library in the target project (optional, for component-specific output)"],"input_types":["image (PNG, JPG screenshot of form or table)"],"output_types":["JSON configuration object","form-render schema","Formily schema"],"categories":["image-visual","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-wjkang-lowcode__cap_6","uri":"capability://code.generation.editing.inline.code.generation.with.editor.context.awareness","name":"inline code generation with editor context awareness","description":"Integrates 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.","intents":["Generate function implementations based on function signatures and surrounding code context","Expand code templates and boilerplate based on the current project structure","Get code suggestions for common patterns without leaving the editor"],"best_for":["Developers using Formily, form-render, or other framework-specific components","Teams with standardized code patterns and conventions","Developers seeking to accelerate repetitive code generation tasks"],"limitations":["Context awareness is limited to the current file — no cross-file dependency analysis","Generated code requires review and integration — not guaranteed to be production-ready","No built-in refactoring or code quality checks on generated output","Latency from LLM API calls may interrupt editing flow"],"requires":["VS Code extension installed","API key for configured LLM provider","Code selection or cursor position in the editor"],"input_types":["selected code","function signature","code context"],"output_types":["code snippet","function implementation","template expansion"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-wjkang-lowcode__cap_7","uri":"capability://data.processing.analysis.mock.data.generation.from.json.schemas","name":"mock data generation from json schemas","description":"Accepts 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.","intents":["Generate mock API responses for testing frontend code before backend is ready","Create test fixtures and sample data for component development and testing","Populate development databases with realistic sample data matching schema definitions"],"best_for":["Frontend developers testing components without live API access","Teams practicing test-driven development with mock data fixtures","Developers prototyping features before backend implementation"],"limitations":["Mock data generation is basic — does not support custom generators or domain-specific patterns","No support for realistic data distributions or relationships between fields","Generated data is random — not deterministic or reproducible without seeding","No integration with faker libraries or advanced data generation tools"],"requires":["VS Code extension installed","JSON schema or TypeScript interface definition"],"input_types":["JSON schema","TypeScript interface"],"output_types":["JSON mock data","JavaScript object"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["VS Code extension installed from marketplace","API key for ChatGPT, OpenAI, or configured alternative LLM","Screenshot or image file of the desired UI layout","Formily or form-render library in the target project (optional, for component-specific output)","VS Code extension installed","Valid JSON sample or TypeScript interface in the editor","No external API key required for this capability (local processing)","API key for ChatGPT or configured LLM","Chinese text selection in the editor","YApi instance URL and project/API credentials"],"failure_modes":["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","Mock data generation is basic — does not support custom generators or realistic data patterns","No integration with OpenAPI/Swagger specs for automatic type generation from API documentation","LLM-based translation may produce non-idiomatic English variable names requiring manual review","Context-dependent translations (e.g., domain-specific terms) may not match team conventions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.68,"quality":0.26,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:33.198Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=lowcode","compare_url":"https://unfragile.ai/compare?artifact=lowcode"}},"signature":"CQpl5+hfv6VNQ0ZrxnU7OwYR4Rj8kKP3naWTVgB1a1vMcjN7BIRxwI1nNWEHn1k5IoCC1q28OtBw0tU4PhKuBQ==","signedAt":"2026-06-15T08:09:12.240Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lowcode","artifact":"https://unfragile.ai/lowcode","verify":"https://unfragile.ai/api/v1/verify?slug=lowcode","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}