{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-gocodeo-gocodeo","slug":"gocodeo-best-of-cursor-and-lovable-combined","name":"GoCodeo: Best of Cursor and Lovable, Combined","type":"agent","url":"https://marketplace.visualstudio.com/items?itemName=GoCodeo.gocodeo","page_url":"https://unfragile.ai/gocodeo-best-of-cursor-and-lovable-combined","categories":["app-builders","deployment-infra"],"tags":["__ext_ipynb","__ext_py","ai","ai code snippets","AI copilot","AI-assisted code completion","AI-powered code completion","anthropic","assistant","autocomplete","automated testing","bash","bot","chat","chatbot","chatgpt","claude","code analysis","code completion","code generation","code hinting","code prediction","code suggestion","code-coverage","codegen","code-integrity","code-refactoring","coding ai","coding assistant","content assist","cpp","csharp","css","developer tools","docstring","gemini","generate","generative-ai","git","gocodeo","golang","gpt-3","gpt-4","html","intellicode","intellisense","java","javascript","javascriptreact","jupyter","keybindings","kotlin","llama","llm","mcp","method completion","model","node","node.js","nodejs","objectivec","objective-c","ocaml","openai","perl","php","python","react","refactor","ruby","rust","snippets","swift","test","typescript","typescriptreact","unit test generation","unit-testing"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-gocodeo-gocodeo__cap_0","uri":"capability://code.generation.editing.full.stack.application.scaffolding.from.natural.language.prompts","name":"full-stack application scaffolding from natural language prompts","description":"Generates complete, production-ready full-stack web applications from natural language specifications by decomposing prompts into functional and technical requirements, then orchestrating code generation across frontend, backend, and database layers. Uses a BUILD framework that maintains modular code generation state across multiple LLM calls, enabling iterative refinement of entire project structures rather than isolated code snippets.","intents":["I want to describe an app idea in plain English and get a working prototype with all boilerplate and folder structure","I need to scaffold a new project quickly without manually setting up build configs, routing, and database schemas","I want to generate full-stack code that's immediately deployable to Vercel with a database backend"],"best_for":["solo developers and small teams building MVPs or prototypes","non-technical founders wanting to validate product ideas with working code","developers migrating from manual scaffolding to AI-assisted project generation"],"limitations":["Generated code quality depends on prompt clarity; vague specifications produce generic boilerplate rather than optimized implementations","Limited to 25+ predefined framework templates (React, Vue, Next.js, etc.); custom framework support unknown","No built-in code quality scanning or security analysis of generated projects before deployment","Refactoring and optimization of generated code requires manual intervention or additional prompts"],"requires":["VS Code 1.80+ (specific minimum version unknown from documentation)","API key for at least one supported LLM provider (Claude, GPT-4, Gemini, or Deepseek)","Internet connection for LLM API calls and deployment services"],"input_types":["natural language description","image files (for visual-to-code generation)","project requirements and specifications"],"output_types":["complete project directory structure","source code files (JavaScript, TypeScript, Python, etc.)","configuration files (package.json, tsconfig.json, .env templates)","database schema definitions"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-gocodeo-gocodeo__cap_1","uri":"capability://image.visual.visual.to.code.generation.from.images.and.screenshots","name":"visual-to-code generation from images and screenshots","description":"Converts images, screenshots, and visual mockups into production-ready code by analyzing visual layouts and components, then generating corresponding HTML, CSS, React components, or framework-specific implementations. Supports image attachment in the chat interface, enabling developers to paste UI designs and receive functional code with proper styling and component structure.","intents":["I have a Figma design or screenshot and want to convert it to working React/Vue/HTML code","I want to generate styled components from a visual mockup without manually writing CSS","I need to quickly prototype a UI layout by uploading a screenshot and getting code"],"best_for":["frontend developers and designers collaborating on UI implementation","teams using design-to-code workflows with Figma or other design tools","rapid prototypers who want to skip manual HTML/CSS boilerplate"],"limitations":["Accuracy depends on image clarity and design complexity; low-resolution or ambiguous designs produce generic layouts","Generated code may require manual refinement for responsive design, accessibility, and advanced CSS features","No direct integration with Figma API; requires manual image export and upload workflow","Styling accuracy limited to CSS-in-JS or standard CSS; complex design systems may not translate perfectly"],"requires":["VS Code 1.80+ (specific minimum version unknown)","API key for vision-capable LLM (Claude 4 with vision, GPT-4 with vision, or Gemini 2.5 Pro)","Image file in supported format (PNG, JPG, WebP, etc.)"],"input_types":["image files (PNG, JPG, WebP, GIF)","screenshots","design mockups","wireframes"],"output_types":["HTML with inline CSS","React/Vue/Svelte components","Tailwind CSS classes","styled-components or CSS modules","responsive layout code"],"categories":["image-visual","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-gocodeo-gocodeo__cap_10","uri":"capability://planning.reasoning.environment.aware.agent.configuration.with.context.injection","name":"environment-aware agent configuration with context injection","description":"Automatically detects and injects environment variables, project configuration, and runtime context into AI agent decision-making. Agents can access environment-specific settings (development, staging, production) and use them to generate environment-appropriate code, configurations, and deployment settings without explicit user specification.","intents":["I want the AI to generate different code for development vs. production environments automatically","I need environment variables to be injected into generated code without manual configuration","I want the AI to understand my project's runtime environment and generate compatible code"],"best_for":["teams with complex environment configurations (dev, staging, prod)","developers who want environment-specific code generation without manual branching","organizations with strict environment management policies"],"limitations":["Environment detection mechanism unknown; unclear how environments are identified","No UI for configuring environment-specific generation rules","Environment variable injection scope unknown; unclear which variables are accessible to agents","No validation of environment-specific code before deployment","Security implications of environment variable access unknown; no sandboxing documented"],"requires":["VS Code 1.80+ (specific minimum version unknown)",".env files or environment variable configuration in project","Environment context specification (development, staging, production)"],"input_types":["environment variables (.env files)","environment designation (dev/staging/prod)","runtime configuration"],"output_types":["environment-specific code","environment-specific configuration files","deployment-ready artifacts"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-gocodeo-gocodeo__cap_11","uri":"capability://code.generation.editing.iterative.code.refinement.through.multi.turn.chat.with.build.state.preservation","name":"iterative code refinement through multi-turn chat with build state preservation","description":"Enables developers to refine generated code through multiple chat turns while maintaining full BUILD framework state and context. Each follow-up prompt can reference previous generations, request specific modifications, or ask for alternative implementations, with the AI maintaining awareness of the entire generation history and project structure.","intents":["I want to ask the AI to modify generated code without losing the context of what was already generated","I need to request multiple iterations of the same feature with different implementations","I want to gradually refine my app specification through conversation without restarting from scratch"],"best_for":["developers who prefer iterative refinement over upfront specification","teams exploring multiple implementation approaches for the same feature","rapid prototypers who want to evolve their app through conversation"],"limitations":["Chat history length limits unknown; very long conversations may lose early context","No explicit version control for intermediate generations; only latest state is preserved","Refinement requests may conflict with previous generations; no conflict resolution mechanism","No UI for comparing different iterations or reverting to previous states","Latency accumulates with each turn; very long refinement sessions may become slow"],"requires":["VS Code 1.80+ (specific minimum version unknown)","Active build session with generated code","API key for LLM provider"],"input_types":["follow-up prompts","modification requests","alternative implementation requests"],"output_types":["refined code","modified project structure","updated BUILD state"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-gocodeo-gocodeo__cap_12","uri":"capability://code.generation.editing.code.generation.with.framework.specific.best.practices.and.patterns","name":"code generation with framework-specific best practices and patterns","description":"Generates code that adheres to framework-specific conventions, design patterns, and best practices for the selected tech stack. Includes automatic implementation of patterns like React hooks, Next.js API routes, Vue composition API, Django models, and other framework idioms, ensuring generated code is idiomatic and maintainable rather than generic.","intents":["I want generated code to follow React/Vue/Next.js best practices automatically without manual refactoring","I need the AI to use the right patterns for my framework (e.g., hooks instead of class components)","I want generated code to be production-ready and follow industry standards for my tech stack"],"best_for":["developers working with specific frameworks who want idiomatic code","teams with strict code style and pattern requirements","organizations where generated code must pass code review without major refactoring"],"limitations":["Pattern implementation quality depends on LLM knowledge of framework; newer frameworks may not be well-supported","No customization of patterns; generated code follows default conventions","No linting or style checking of generated code; patterns may not match team preferences","Framework updates may not be reflected in generated code patterns","No UI for specifying custom patterns or conventions"],"requires":["VS Code 1.80+ (specific minimum version unknown)","Framework template selection","API key for LLM with knowledge of target framework"],"input_types":["framework selection","feature requirements","custom pattern specifications (optional)"],"output_types":["idiomatic framework code","framework-specific configuration","pattern-compliant implementations"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-gocodeo-gocodeo__cap_2","uri":"capability://tool.use.integration.multi.provider.llm.model.selection.and.configuration","name":"multi-provider llm model selection and configuration","description":"Provides a model selector dropdown UI allowing developers to choose between Claude 4, GPT-4.1, Gemini 2.5 Pro, Deepseek, and other supported LLMs without leaving VS Code. Implements a bring-your-own-key (BYOK) architecture where users supply their own API credentials, with storage and management handled through VS Code's secrets API or local configuration.","intents":["I want to switch between different LLM providers to compare code generation quality or cost","I need to use my own API keys for full transparency and control over API calls","I want to use a specific model known for better performance on my use case (e.g., Claude for reasoning, GPT-4 for speed)"],"best_for":["developers with existing API keys across multiple LLM providers","teams requiring cost optimization by selecting cheaper models for routine tasks","organizations with data privacy requirements necessitating direct API control"],"limitations":["API key storage mechanism unknown; unclear if credentials are encrypted or stored in plaintext","No built-in rate limiting or cost tracking across different providers","Model selector UI does not show pricing, latency, or capability differences between models","Switching models mid-project may produce inconsistent code styles or patterns","No fallback mechanism if selected model's API is unavailable"],"requires":["VS Code 1.80+ (specific minimum version unknown)","Active API key for at least one supported LLM provider (Anthropic, OpenAI, Google, Deepseek, etc.)","Internet connectivity to reach selected LLM provider's API endpoints"],"input_types":["API key (string)","model selection (dropdown choice)"],"output_types":["configuration state (selected model identifier)","API routing configuration (internal)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-gocodeo-gocodeo__cap_3","uri":"capability://tool.use.integration.mcp.based.tool.integration.and.orchestration.with.100.external.services","name":"mcp-based tool integration and orchestration with 100+ external services","description":"Integrates the Model Context Protocol (MCP) client and server architecture to enable AI agents to discover, select, and execute tools across 100+ external services including GitHub, Notion, Postgres, Stripe, and custom integrations. Tools are defined in an mcp.json configuration file, and the agent automatically selects appropriate tools based on task context and intent, executing them with live data fetching and state management.","intents":["I want my AI agent to fetch live data from GitHub, Notion, or Postgres and use it in code generation","I need to integrate Stripe, payment APIs, or other external services directly into generated code","I want to define custom tools and have the AI agent use them automatically based on task requirements"],"best_for":["teams building AI agents that require real-time data from external services","developers integrating with SaaS platforms (GitHub, Notion, Stripe) in generated applications","organizations with custom internal tools that need to be exposed to AI agents"],"limitations":["Only 4 example integrations documented (GitHub, Notion, Postgres, Stripe); full list of 100+ tools unknown","MCP server execution context unclear; unknown if servers run locally, remotely, or in hybrid mode","Tool selection logic is automatic and opaque; no UI for developers to see which tools were selected or why","No built-in error handling or retry logic for failed tool executions","Custom tool definition requires manual mcp.json editing; no UI-based tool configuration wizard","Security implications of automatic tool execution unknown; no sandboxing or approval workflow documented"],"requires":["VS Code 1.80+ (specific minimum version unknown)","mcp.json configuration file in project root with tool definitions","API credentials for each external service (GitHub token, Notion API key, Postgres connection string, etc.)","Internet connectivity to reach external service APIs"],"input_types":["mcp.json configuration file (JSON)","natural language prompts referencing external data","service API credentials"],"output_types":["tool execution results (JSON, structured data)","live data from external services","code generated with external service integration"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-gocodeo-gocodeo__cap_4","uri":"capability://automation.workflow.one.click.vercel.deployment.with.environment.configuration","name":"one-click vercel deployment with environment configuration","description":"Automates the deployment of generated full-stack applications to Vercel with a single click, handling environment variable configuration, build script execution, and domain setup. Integrates with Vercel's API to create projects, configure deployment settings, and manage environment variables without requiring manual CLI commands or dashboard navigation.","intents":["I want to deploy my generated app to production with a single click from VS Code","I need to set up environment variables and database connections for my deployed app automatically","I want to get a live URL for my app immediately after generation without manual deployment steps"],"best_for":["developers building and shipping MVPs quickly without DevOps expertise","teams using Vercel as their primary hosting platform","rapid prototypers who want to minimize deployment friction"],"limitations":["Vercel-only deployment; no support for AWS, Google Cloud, Azure, or self-hosted options","Environment variable configuration mechanism unknown; unclear if variables are auto-detected or manually specified","No built-in CI/CD pipeline configuration; assumes Vercel's default deployment behavior","Database connection setup requires Supabase integration; other database providers not supported","No rollback mechanism or deployment history management within the extension","Deployment logs and errors may not be visible in VS Code; requires Vercel dashboard navigation"],"requires":["VS Code 1.80+ (specific minimum version unknown)","Vercel account with API token configured","Generated project with package.json and build scripts","Internet connectivity to reach Vercel API"],"input_types":["generated project directory","environment variables (optional)","Vercel API token"],"output_types":["deployed application URL","deployment status","environment variable configuration"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-gocodeo-gocodeo__cap_5","uri":"capability://data.processing.analysis.supabase.database.integration.with.schema.generation","name":"supabase database integration with schema generation","description":"Automatically generates and deploys PostgreSQL database schemas to Supabase, including table definitions, relationships, and migrations. Integrates with Supabase's API to create projects, manage authentication, and configure real-time subscriptions, enabling full-stack applications with database backends without manual schema design or migration scripts.","intents":["I want to generate a database schema from my app requirements and deploy it to Supabase automatically","I need to set up authentication and real-time data subscriptions for my full-stack app","I want to avoid writing SQL migrations manually and have the AI generate them from my data model"],"best_for":["developers building full-stack applications with PostgreSQL backends","teams using Supabase for authentication and real-time features","rapid prototypers who want database setup to be automatic and invisible"],"limitations":["Supabase-only database support; no integration with other PostgreSQL providers or non-relational databases","Schema generation quality depends on prompt clarity; complex data models may require manual refinement","No built-in migration rollback or version control for schema changes","Real-time subscription configuration is automatic; no UI for developers to customize subscription logic","No data seeding or fixture management; generated schemas are empty","Performance optimization (indexing, partitioning) not handled automatically"],"requires":["VS Code 1.80+ (specific minimum version unknown)","Supabase account with API key configured","Generated project with data model specifications","Internet connectivity to reach Supabase API"],"input_types":["data model description (natural language or structured)","relationship definitions","authentication requirements","Supabase API key"],"output_types":["PostgreSQL schema (SQL DDL)","migration scripts","Supabase project configuration","connection strings and credentials"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-gocodeo-gocodeo__cap_6","uri":"capability://memory.knowledge.codebase.aware.code.referencing.with.symbol.syntax","name":"codebase-aware code referencing with @ symbol syntax","description":"Enables developers to reference specific files, functions, classes, and symbols in their project using @ symbol syntax within chat prompts (e.g., @filename.js, @ClassName). The extension indexes all files and code symbols in the project folder, providing autocomplete suggestions and passing the referenced code as context to the LLM, enabling context-aware code generation and refactoring without manual copy-paste.","intents":["I want to reference a specific file or function in my prompt without copying and pasting the entire code","I need the AI to understand my existing codebase structure and generate code that integrates with it","I want to refactor a specific function while maintaining compatibility with the rest of my project"],"best_for":["developers working on existing codebases who need context-aware code generation","teams refactoring or extending large projects without losing architectural consistency","developers who want to avoid manual context management in chat prompts"],"limitations":["@ symbol indexing scope limited to current project folder; no support for external libraries or node_modules","Autocomplete suggestions may be slow for very large projects (1000+ files)","Symbol resolution limited to basic syntax parsing; complex language features may not be indexed correctly","No support for cross-file dependency analysis; referenced code is passed as isolated snippets","Indexing is not real-time; changes to files may not be reflected immediately in @ suggestions"],"requires":["VS Code 1.80+ (specific minimum version unknown)","Project folder open in VS Code","Supported language for code symbol extraction (JavaScript, TypeScript, Python, etc.)"],"input_types":["@ symbol followed by filename or symbol name","autocomplete selection from indexed symbols"],"output_types":["code snippet context passed to LLM","context-aware code generation results"],"categories":["memory-knowledge","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-gocodeo-gocodeo__cap_7","uri":"capability://memory.knowledge.build.session.history.and.multi.session.context.management","name":"build session history and multi-session context management","description":"Maintains separate build sessions for different projects or iterations, allowing developers to switch between multiple in-progress applications and resume work with full context preservation. Each session maintains its own BUILD framework state, generated code, and chat history, enabling parallel development workflows without context loss or manual state management.","intents":["I want to work on multiple app ideas in parallel and switch between them without losing context","I need to compare different code generation approaches by maintaining separate build sessions","I want to preserve the history of my generation prompts and results for future reference"],"best_for":["developers exploring multiple project ideas simultaneously","teams iterating on different versions of the same application","rapid prototypers who want to maintain a portfolio of generated projects"],"limitations":["Session storage mechanism unknown; unclear if sessions are stored locally or in the cloud","No built-in session export or backup functionality","Session switching may require manual selection; no automatic context detection","Session size limits unknown; very large projects may cause performance degradation","No collaboration features; sessions are single-user only","Session cleanup and deletion mechanism unknown"],"requires":["VS Code 1.80+ (specific minimum version unknown)","GoCodeo extension installed and configured"],"input_types":["session selection (dropdown or list)","new session creation trigger"],"output_types":["session context (BUILD state, chat history, generated code)","session list with metadata (creation date, project name, etc.)"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-gocodeo-gocodeo__cap_8","uri":"capability://planning.reasoning.prompt.enhancement.and.specification.generation","name":"prompt enhancement and specification generation","description":"Automatically transforms simple, informal user prompts into detailed functional and technical specifications before code generation. The enhancement process decomposes vague requirements into concrete features, data models, API endpoints, and implementation details, improving code generation quality without requiring users to write detailed specifications manually.","intents":["I have a rough idea for an app and want the AI to expand it into a detailed specification before generating code","I want to avoid writing lengthy prompts by having the AI infer missing requirements from my brief description","I need the AI to ask clarifying questions and generate a specification that covers edge cases and technical details"],"best_for":["non-technical founders and product managers who struggle with detailed technical specifications","developers who want to minimize prompt engineering effort","teams using AI-generated code where specification clarity is critical for quality"],"limitations":["Enhancement process may introduce assumptions that don't match user intent; no feedback loop for specification validation","Specification generation adds latency before code generation begins","No UI for reviewing or editing enhanced specifications before code generation","Enhancement quality depends on LLM reasoning capability; weaker models may produce generic specifications","No version control or comparison of original vs. enhanced prompts"],"requires":["VS Code 1.80+ (specific minimum version unknown)","API key for LLM with strong reasoning capability (Claude 4 recommended)","Natural language prompt describing app requirements"],"input_types":["informal natural language description","rough requirements or feature list"],"output_types":["detailed functional specification","technical specification with data models and APIs","implementation roadmap"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-gocodeo-gocodeo__cap_9","uri":"capability://code.generation.editing.framework.agnostic.full.stack.template.library.with.25.starter.configurations","name":"framework-agnostic full-stack template library with 25+ starter configurations","description":"Provides a curated library of 25+ full-stack framework templates covering popular combinations like React+Node.js, Next.js+Supabase, Vue+Django, and others. Templates include pre-configured build scripts, folder structures, authentication patterns, and database integrations, serving as starting points for code generation that can be customized through prompts.","intents":["I want to generate an app using a specific tech stack (e.g., Next.js + Supabase) without manually configuring boilerplate","I need a starting template that follows best practices for my chosen framework combination","I want to explore different tech stacks by generating the same app idea with different templates"],"best_for":["developers familiar with specific tech stacks who want to skip boilerplate setup","teams standardizing on particular framework combinations","rapid prototypers who want to generate apps in their preferred tech stack quickly"],"limitations":["Only 25+ templates documented; custom template creation mechanism unknown","Templates are static starting points; no dynamic template selection based on requirements","Template selection must be explicit; no automatic recommendation based on app type","No template versioning; unclear how framework updates are handled","Template customization requires manual prompt engineering; no UI-based template configuration"],"requires":["VS Code 1.80+ (specific minimum version unknown)","Selection of desired framework template from available options"],"input_types":["template selection (dropdown or list)","customization prompts"],"output_types":["project directory with template structure","configured build scripts and dependencies","example code and documentation"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["VS Code 1.80+ (specific minimum version unknown from documentation)","API key for at least one supported LLM provider (Claude, GPT-4, Gemini, or Deepseek)","Internet connection for LLM API calls and deployment services","VS Code 1.80+ (specific minimum version unknown)","API key for vision-capable LLM (Claude 4 with vision, GPT-4 with vision, or Gemini 2.5 Pro)","Image file in supported format (PNG, JPG, WebP, etc.)",".env files or environment variable configuration in project","Environment context specification (development, staging, production)","Active build session with generated code","API key for LLM provider"],"failure_modes":["Generated code quality depends on prompt clarity; vague specifications produce generic boilerplate rather than optimized implementations","Limited to 25+ predefined framework templates (React, Vue, Next.js, etc.); custom framework support unknown","No built-in code quality scanning or security analysis of generated projects before deployment","Refactoring and optimization of generated code requires manual intervention or additional prompts","Accuracy depends on image clarity and design complexity; low-resolution or ambiguous designs produce generic layouts","Generated code may require manual refinement for responsive design, accessibility, and advanced CSS features","No direct integration with Figma API; requires manual image export and upload workflow","Styling accuracy limited to CSS-in-JS or standard CSS; complex design systems may not translate perfectly","Environment detection mechanism unknown; unclear how environments are identified","No UI for configuring environment-specific generation rules","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.51,"quality":0.5,"ecosystem":0.45,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"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=gocodeo-best-of-cursor-and-lovable-combined","compare_url":"https://unfragile.ai/compare?artifact=gocodeo-best-of-cursor-and-lovable-combined"}},"signature":"5h4Z0dE7UT5Qv3KO6BspiaHrW1vN303BDQLAach5pRwByLTVgzqwWC7jX71BPf1Rp9JN1zR7aPCVP4bTEmwLAw==","signedAt":"2026-06-21T04:40:22.553Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gocodeo-best-of-cursor-and-lovable-combined","artifact":"https://unfragile.ai/gocodeo-best-of-cursor-and-lovable-combined","verify":"https://unfragile.ai/api/v1/verify?slug=gocodeo-best-of-cursor-and-lovable-combined","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"}}