{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_polymet","slug":"polymet","name":"Polymet","type":"product","url":"https://www.polymet.ai","page_url":"https://unfragile.ai/polymet","categories":["app-builders","deployment-infra"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_polymet__cap_0","uri":"capability://code.generation.editing.design.to.code.transformation.with.ai.synthesis","name":"design-to-code transformation with ai synthesis","description":"Converts design specifications, wireframes, or high-level requirements into syntactically valid, production-ready code by leveraging large language models to interpret design intent and generate corresponding implementation. The system likely uses prompt engineering and multi-turn reasoning to bridge the semantic gap between visual/textual specifications and executable code, potentially incorporating design-aware tokenization or AST-based code structuring to ensure output quality.","intents":["I want to convert a Figma design into React components without manually writing boilerplate","I need to generate initial code scaffolding from a product specification document","I want to accelerate MVP development by auto-generating CRUD endpoints from a data model"],"best_for":["startup product teams building greenfield projects with clear technical requirements","non-technical founders or designers who want to participate in code generation","teams prioritizing speed-to-MVP over architectural customization"],"limitations":["Output quality degrades significantly with vague or ambiguous requirements; specificity of input directly correlates with usability of generated code","No visibility into handling of legacy systems, complex architectural patterns, or non-standard tech stacks","Generated code may require significant refactoring for production use in complex domains (e.g., distributed systems, real-time applications)","Likely lacks context awareness across large codebases, potentially generating code that conflicts with existing patterns or conventions"],"requires":["Clear, detailed specification of requirements (design files, API specs, or structured descriptions)","Access to Polymet platform (web-based or API)","Target technology stack must be supported by the underlying LLM training data"],"input_types":["text (natural language requirements, API specifications)","image (design mockups, wireframes, screenshots)","structured data (JSON schemas, data models)"],"output_types":["code (JavaScript, TypeScript, Python, etc.)","structured templates (component definitions, endpoint scaffolds)"],"categories":["code-generation-editing","design-to-development"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_polymet__cap_1","uri":"capability://code.generation.editing.boilerplate.code.generation.with.pattern.recognition","name":"boilerplate code generation with pattern recognition","description":"Automatically generates repetitive structural code (CRUD operations, API endpoints, component scaffolds, database schemas) by recognizing common architectural patterns and applying them to user-specified contexts. The system likely analyzes input specifications to identify pattern types, then instantiates pre-trained or LLM-generated templates with appropriate variable substitution, type annotations, and framework-specific conventions.","intents":["I need to generate 10 CRUD endpoints for a REST API without writing repetitive boilerplate","I want to scaffold a React component library with consistent structure and prop types","I need to generate database migration files and ORM models from a schema definition"],"best_for":["developers building standard CRUD applications or microservices","teams with repetitive coding patterns that benefit from automation","projects using conventional tech stacks (React, Express, Django, etc.)"],"limitations":["Boilerplate generation is most effective for conventional patterns; custom or domain-specific patterns may require manual post-generation editing","Generated code may not include error handling, logging, or security considerations beyond basic structure","No built-in refactoring or consolidation of generated code; multiple generations may create redundant implementations"],"requires":["Clear specification of data models, API contracts, or component requirements","Target framework must be supported (React, Express, Django, FastAPI, etc.)"],"input_types":["text (API specifications, data model descriptions)","structured data (JSON schemas, OpenAPI specs, GraphQL schemas)"],"output_types":["code (endpoint definitions, component files, migration scripts)","configuration files (package.json, tsconfig.json, etc.)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_polymet__cap_2","uri":"capability://code.generation.editing.multi.language.code.generation.with.framework.aware.synthesis","name":"multi-language code generation with framework-aware synthesis","description":"Generates syntactically correct, framework-compliant code across multiple programming languages and technology stacks by maintaining language-specific AST representations and framework conventions. The system likely uses language-specific tokenizers, type systems, and framework-aware code generation rules to ensure output adheres to idiomatic patterns for each target language (e.g., Pythonic conventions vs. JavaScript idioms).","intents":["I need to generate equivalent API endpoints in both Python (FastAPI) and Node.js (Express)","I want to generate type-safe code in TypeScript that matches my existing codebase conventions","I need to generate code for multiple frameworks (React, Vue, Svelte) from a single specification"],"best_for":["polyglot teams working across multiple languages and frameworks","organizations standardizing on specific tech stacks and needing consistent code generation","projects requiring cross-platform or multi-language implementations"],"limitations":["Code generation quality varies by language; well-represented languages in training data (Python, JavaScript) likely produce better results than niche languages","Framework-specific idioms and best practices may not be fully captured, especially for newer or less common frameworks","No built-in validation that generated code follows team-specific conventions or linting rules"],"requires":["Target language and framework must be supported by Polymet","Clear specification of requirements in language/framework-agnostic terms"],"input_types":["text (language-agnostic specifications)","structured data (API contracts, data models)"],"output_types":["code (Python, JavaScript, TypeScript, Java, Go, etc.)","framework-specific files (component files, endpoint definitions, configuration)"],"categories":["code-generation-editing","polyglot-development"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_polymet__cap_3","uri":"capability://image.visual.image.to.code.component.generation.from.design.mockups","name":"image-to-code component generation from design mockups","description":"Converts visual design mockups, wireframes, or screenshots into functional UI component code by performing visual understanding (likely via computer vision or multimodal LLM) to extract layout, styling, and interactive elements, then synthesizing corresponding HTML/CSS/JavaScript or framework-specific component code. The system likely uses image segmentation or object detection to identify UI elements, then maps them to component libraries or generates custom styling.","intents":["I have a Figma design and want to generate React components without manually coding the layout","I want to convert a screenshot or wireframe into HTML/CSS boilerplate","I need to generate responsive component code from a design mockup"],"best_for":["design-to-development teams wanting to reduce handoff friction","designers without coding experience who want to generate initial component code","rapid prototyping scenarios where speed-to-code matters more than pixel-perfect accuracy"],"limitations":["Visual-to-code accuracy depends on design clarity and complexity; intricate layouts or custom styling may require significant post-generation refinement","Generated code may not capture interactive behaviors, animations, or dynamic state management beyond basic structure","Styling accuracy is limited; generated CSS may require tweaking for exact color matching, spacing, or responsive behavior","No built-in understanding of design system tokens or component libraries; may generate redundant or non-standard styling"],"requires":["Design mockup or screenshot in common image formats (PNG, JPG, SVG, or design tool export)","Target framework specified (React, Vue, HTML/CSS, etc.)"],"input_types":["image (design mockups, wireframes, screenshots, Figma exports)"],"output_types":["code (React/Vue/HTML components with CSS)","styling files (CSS, SCSS, Tailwind classes)"],"categories":["image-visual","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_polymet__cap_4","uri":"capability://code.generation.editing.api.specification.driven.endpoint.generation","name":"api specification-driven endpoint generation","description":"Generates complete API endpoint implementations (handlers, validation, serialization, error handling) from structured API specifications (OpenAPI/Swagger, GraphQL schemas, or JSON schema definitions) by parsing the specification, extracting endpoint contracts, and synthesizing corresponding server-side code with appropriate middleware, type definitions, and request/response handling. The system likely uses specification parsing to extract operation details, then applies framework-specific code generation templates.","intents":["I have an OpenAPI spec and want to generate Express.js endpoints that match the contract","I need to generate FastAPI endpoints from a Swagger definition with automatic validation","I want to generate GraphQL resolvers from a schema definition"],"best_for":["API-first development teams using specification-driven design","organizations with strict API contracts that need implementation to match specifications","teams building microservices with multiple endpoint implementations"],"limitations":["Generated endpoints include basic structure and validation but may lack business logic, requiring manual implementation of core functionality","Error handling and edge cases may not be fully captured from specification alone; requires additional specification of error scenarios","No built-in database integration; generated endpoints may need connection to data layer","Specification quality directly impacts generated code quality; incomplete or ambiguous specs produce incomplete implementations"],"requires":["Structured API specification (OpenAPI 3.0+, GraphQL schema, JSON schema)","Target framework specified (Express, FastAPI, Django REST, etc.)"],"input_types":["structured data (OpenAPI/Swagger YAML/JSON, GraphQL schema, JSON schema)"],"output_types":["code (endpoint handlers, route definitions, type definitions)","validation code (request/response validators, middleware)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_polymet__cap_5","uri":"capability://code.generation.editing.database.schema.to.model.code.generation","name":"database schema-to-model code generation","description":"Generates ORM model definitions, database migrations, and type-safe data access code from database schema specifications (SQL DDL, JSON schema, or visual schema diagrams) by parsing schema definitions, extracting table/collection structures and relationships, then synthesizing corresponding ORM models with appropriate type annotations, relationships, and validation rules. The system likely uses schema parsing to extract column definitions, constraints, and relationships, then applies ORM-specific code generation.","intents":["I have a SQL schema and want to generate SQLAlchemy models with relationships","I need to generate Prisma schema and migrations from a database design","I want to generate TypeScript types and Mongoose models from a MongoDB schema"],"best_for":["teams using database-first or schema-first development approaches","projects requiring type-safe data access with ORM models","organizations standardizing on specific ORMs (SQLAlchemy, Prisma, Sequelize, etc.)"],"limitations":["Generated models include basic structure and relationships but may lack custom validation logic or computed properties","Complex constraints or database-specific features may not be fully captured in generated models","No built-in understanding of business logic or domain-specific validation rules; requires manual addition","Generated migrations may require manual review for production use, especially for data transformation scenarios"],"requires":["Database schema specification (SQL DDL, JSON schema, or visual diagram)","Target ORM specified (SQLAlchemy, Prisma, Sequelize, TypeORM, etc.)"],"input_types":["text (SQL DDL statements)","structured data (JSON schema, database schema diagrams)","image (visual schema diagrams)"],"output_types":["code (ORM model definitions, type definitions)","migration files (database migration scripts)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_polymet__cap_6","uri":"capability://code.generation.editing.context.aware.code.completion.with.codebase.understanding","name":"context-aware code completion with codebase understanding","description":"Provides intelligent code suggestions and completions by analyzing the current codebase context, understanding existing patterns, conventions, and architecture, then generating suggestions that align with project-specific style and structure. The system likely indexes the codebase (or accepts codebase context) to extract patterns, naming conventions, and architectural decisions, then uses this context to inform LLM-based completion generation.","intents":["I want code completions that match my project's existing patterns and conventions","I need to generate code that integrates seamlessly with my existing codebase architecture","I want suggestions that understand my project's dependencies and framework choices"],"best_for":["teams with established codebases and consistent architectural patterns","projects requiring code generation that adheres to specific conventions","developers working in large codebases where consistency is critical"],"limitations":["Codebase indexing may add latency or require local processing; cloud-based solutions may have privacy concerns","Context window limitations may prevent full codebase understanding for very large projects","Pattern extraction may miss subtle conventions or domain-specific patterns","No guarantee that suggestions will be optimal for new features or refactoring scenarios"],"requires":["Access to codebase (local or via API)","Sufficient codebase size to establish meaningful patterns (typically 1000+ lines of code)"],"input_types":["code (current file context, codebase structure)"],"output_types":["code (completion suggestions, code snippets)"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_polymet__cap_7","uri":"capability://code.generation.editing.deployment.configuration.generation.from.application.code","name":"deployment configuration generation from application code","description":"Automatically generates deployment configurations, infrastructure-as-code definitions, and containerization files (Dockerfiles, Kubernetes manifests, CI/CD pipelines) by analyzing application code to extract dependencies, runtime requirements, and deployment needs, then synthesizing appropriate configuration files. The system likely performs dependency analysis, framework detection, and environment requirement extraction to generate platform-specific deployment configurations.","intents":["I want to generate a Dockerfile and docker-compose.yml from my Node.js application","I need to generate Kubernetes manifests for my microservices without manual YAML writing","I want to generate GitHub Actions or GitLab CI pipelines from my project structure"],"best_for":["teams automating deployment configuration generation","developers unfamiliar with infrastructure-as-code or containerization","projects requiring consistent deployment configurations across multiple environments"],"limitations":["Generated configurations may require customization for production use; security, scaling, and performance tuning typically require manual review","Complex deployment scenarios (multi-region, service mesh, advanced networking) may not be fully captured","Generated configurations may not include monitoring, logging, or observability setup","Dependency detection may miss implicit requirements or optional dependencies"],"requires":["Application code with clear dependency declarations (package.json, requirements.txt, go.mod, etc.)","Target deployment platform specified (Docker, Kubernetes, AWS, GCP, etc.)"],"input_types":["code (application source code, dependency files)"],"output_types":["configuration files (Dockerfile, docker-compose.yml, Kubernetes manifests, CI/CD pipeline definitions)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_polymet__cap_8","uri":"capability://code.generation.editing.test.code.generation.with.coverage.aware.synthesis","name":"test code generation with coverage-aware synthesis","description":"Automatically generates unit tests, integration tests, and test fixtures by analyzing application code to understand function signatures, dependencies, and code paths, then synthesizing test cases that cover common scenarios and edge cases. The system likely performs code analysis to extract testable units, identify dependencies, and generate mock/stub definitions, then creates test cases with appropriate assertions.","intents":["I want to generate unit tests for my API endpoints without writing boilerplate test cases","I need to generate test fixtures and mocks for my service dependencies","I want to generate integration tests that cover common user workflows"],"best_for":["teams improving test coverage without manual test writing","projects with clear, well-structured code that's easy to analyze","developers seeking to accelerate test suite development"],"limitations":["Generated tests may lack business logic validation; tests typically cover happy paths and basic error cases, not complex scenarios","Test quality depends on code clarity; poorly structured or undocumented code produces less useful tests","Generated tests may require manual review and refinement to ensure they test meaningful scenarios","Mock/stub generation may not capture complex dependency behavior or side effects"],"requires":["Well-structured application code with clear function signatures and dependencies","Target testing framework specified (Jest, pytest, Mocha, etc.)"],"input_types":["code (application source code, function definitions)"],"output_types":["code (test files, test fixtures, mock definitions)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_polymet__cap_9","uri":"capability://text.generation.language.documentation.generation.from.code.and.specifications","name":"documentation generation from code and specifications","description":"Automatically generates API documentation, README files, and code documentation by analyzing source code, extracting function signatures and docstrings, and synthesizing human-readable documentation in multiple formats (Markdown, HTML, OpenAPI specs). The system likely performs code analysis to extract documentation-relevant information, then applies documentation templates and formatting rules to generate comprehensive documentation.","intents":["I want to generate comprehensive API documentation from my code without manual writing","I need to generate README files with setup instructions and usage examples","I want to generate OpenAPI specs from my existing API code"],"best_for":["teams automating documentation generation","projects with well-documented code that can be analyzed for documentation extraction","organizations standardizing on documentation formats and structures"],"limitations":["Generated documentation quality depends on code clarity and existing docstrings; poorly documented code produces incomplete documentation","Generated documentation may lack context, examples, or business logic explanation","Complex architectural decisions or design patterns may not be captured in generated documentation","Manual review and enhancement typically required for production documentation"],"requires":["Well-documented source code with clear function signatures and docstrings","Target documentation format specified (Markdown, HTML, OpenAPI, etc.)"],"input_types":["code (source code with docstrings, API definitions)"],"output_types":["documentation (Markdown, HTML, OpenAPI specs, README files)"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Clear, detailed specification of requirements (design files, API specs, or structured descriptions)","Access to Polymet platform (web-based or API)","Target technology stack must be supported by the underlying LLM training data","Clear specification of data models, API contracts, or component requirements","Target framework must be supported (React, Express, Django, FastAPI, etc.)","Target language and framework must be supported by Polymet","Clear specification of requirements in language/framework-agnostic terms","Design mockup or screenshot in common image formats (PNG, JPG, SVG, or design tool export)","Target framework specified (React, Vue, HTML/CSS, etc.)","Structured API specification (OpenAPI 3.0+, GraphQL schema, JSON schema)"],"failure_modes":["Output quality degrades significantly with vague or ambiguous requirements; specificity of input directly correlates with usability of generated code","No visibility into handling of legacy systems, complex architectural patterns, or non-standard tech stacks","Generated code may require significant refactoring for production use in complex domains (e.g., distributed systems, real-time applications)","Likely lacks context awareness across large codebases, potentially generating code that conflicts with existing patterns or conventions","Boilerplate generation is most effective for conventional patterns; custom or domain-specific patterns may require manual post-generation editing","Generated code may not include error handling, logging, or security considerations beyond basic structure","No built-in refactoring or consolidation of generated code; multiple generations may create redundant implementations","Code generation quality varies by language; well-represented languages in training data (Python, JavaScript) likely produce better results than niche languages","Framework-specific idioms and best practices may not be fully captured, especially for newer or less common frameworks","No built-in validation that generated code follows team-specific conventions or linting rules","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:32.437Z","last_scraped_at":"2026-04-05T13:23:42.560Z","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=polymet","compare_url":"https://unfragile.ai/compare?artifact=polymet"}},"signature":"+MaQLnXvuqwhRi+BNB/+LTopGRvmdcqCxRBSIXKC393jnKxmYtMYlLjZXPBYo/ZCmm3BTouAF3Zbr/2Y6rVeCw==","signedAt":"2026-06-22T18:30:05.942Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/polymet","artifact":"https://unfragile.ai/polymet","verify":"https://unfragile.ai/api/v1/verify?slug=polymet","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"}}