{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-nexu-io--open-design","slug":"nexu-io--open-design","name":"open-design","type":"agent","url":"https://github.com/nexu-io/open-design","page_url":"https://unfragile.ai/nexu-io--open-design","categories":["app-builders","code-editors"],"tags":["agent-skills","ai-agents","ai-design","byok","claude","claude-code-for-design","claude-design","coding-agents","design-systems","design-tools","desktop-app","figma-alternative","generative-ai","hermes-agent","local-first","nextjs","no-code","prototyping","ui-generator","vibe-coding"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-nexu-io--open-design__cap_0","uri":"capability://tool.use.integration.multi.model.agent.orchestration.with.skill.based.routing","name":"multi-model agent orchestration with skill-based routing","description":"Routes design generation tasks across 7+ LLM providers (Claude, Gemini, Copilot, Qwen, Hermes, Kimi, OpenCode) using a skill-registry pattern that maps user intents to provider-specific APIs. Implements provider abstraction layer that normalizes function-calling schemas and response formats, enabling seamless switching between models without code changes. Uses local-first architecture to avoid vendor lock-in while maintaining compatibility with cloud-based models.","intents":["I want to use my preferred LLM (Claude, Gemini, or open-source Hermes) without rewriting my design generation pipeline","I need to switch between models based on cost, latency, or capability without changing my agent code","I want to run design generation locally on my machine without sending data to Anthropic or Google"],"best_for":["teams building design automation that need model flexibility","enterprises with data residency requirements avoiding cloud LLM APIs","developers prototyping with multiple LLM providers before committing to one"],"limitations":["Provider abstraction adds ~50-100ms overhead per skill invocation due to schema normalization","Not all providers support identical function-calling capabilities — some skills may degrade gracefully on certain models","Local execution requires significant GPU memory (8GB+ recommended) for running models like Hermes locally"],"requires":["API keys for at least one supported LLM provider (Claude, Gemini, Copilot, etc.) OR local model runtime (Ollama, vLLM)","Node.js 18+ for agent runtime","TypeScript 5.0+ for type safety across provider integrations"],"input_types":["natural language design briefs","structured design specifications (JSON/YAML)","reference images or design system tokens"],"output_types":["executable design code (HTML/CSS/React)","design system configurations","provider-agnostic skill execution logs"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexu-io--open-design__cap_1","uri":"capability://code.generation.editing.19.skill.design.generation.system.with.composable.task.decomposition","name":"19-skill design generation system with composable task decomposition","description":"Implements a modular skill system where each of 19 discrete design capabilities (layout generation, component creation, color theming, responsive adaptation, etc.) is independently callable and composable. Uses a task-decomposition pattern that breaks user design briefs into skill sequences, executing them in dependency order with intermediate state passing. Each skill encapsulates design logic (e.g., layout skill uses CSS Grid/Flexbox generation, color skill applies WCAG contrast validation) and can be invoked standalone or as part of a larger design workflow.","intents":["I want to generate a complete web prototype from a text description without manually specifying every component","I need to apply a design system (colors, typography, spacing) consistently across generated designs","I want to regenerate only specific parts of a design (e.g., just the color scheme) without rebuilding the entire prototype"],"best_for":["product teams rapidly prototyping multiple design directions","design systems teams automating component generation from specifications","solo developers building full-stack prototypes without design expertise"],"limitations":["Skill composition is sequential — parallel skill execution not yet supported, limiting throughput for complex designs","Some skills have hard dependencies (e.g., responsive adaptation requires layout skill output), reducing flexibility in skill ordering","Generated designs may require 10-20% manual refinement for production use due to edge cases in complex layouts"],"requires":["Design brief or specification (text, image, or structured format)","Optional: design system tokens (colors, typography, spacing scales) as JSON","LLM provider access (Claude recommended for best results)"],"input_types":["natural language design briefs","reference images or wireframes","design system JSON (Figma tokens format supported)","component specifications"],"output_types":["HTML/CSS/React component code","design system configurations","responsive design variants","accessibility metadata (ARIA labels, semantic HTML)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexu-io--open-design__cap_10","uri":"capability://automation.workflow.batch.design.generation.with.template.based.workflows","name":"batch design generation with template-based workflows","description":"Enables batch processing of multiple design requests using template-based workflows that define generation parameters, design system constraints, and export formats. Implements a workflow engine that queues design generation tasks, executes them sequentially or in parallel (depending on resource availability), and aggregates results. Uses a template system where users define once (design system, export formats, quality rules) and apply to many designs without repetition.","intents":["I want to generate 50 landing page variations for A/B testing without manually specifying parameters for each one","I need to batch-generate designs for multiple products using the same design system and export formats","I want to set up a workflow that generates designs on a schedule (e.g., nightly) without manual intervention"],"best_for":["agencies generating designs for multiple clients with consistent workflows","product teams creating design variations for A/B testing","teams automating design generation as part of CI/CD pipelines"],"limitations":["Batch processing is sequential by default — parallel execution requires manual configuration and may exceed API rate limits","Template-based workflows are static — dynamic parameter adjustment during batch execution is not supported","Large batches (100+ designs) may take hours to complete, requiring background job management"],"requires":["Workflow template definition (JSON or YAML)","Batch input data (CSV, JSON, or API endpoint)","LLM provider with sufficient rate limits for batch processing"],"input_types":["workflow template (design system, export formats, quality rules)","batch input data (design briefs, parameters)"],"output_types":["batch results (multiple designs in specified formats)","batch execution report (success/failure per design, timing metrics)","aggregated compliance report"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexu-io--open-design__cap_11","uri":"capability://code.generation.editing.design.to.code.with.context.aware.codebase.integration","name":"design-to-code with context-aware codebase integration","description":"Analyzes the user's existing codebase (React components, design system files, utility functions) and generates code that integrates seamlessly with existing patterns and conventions. Uses AST parsing to extract codebase patterns (component structure, naming conventions, import organization) and applies them to generated code. Implements a context-injection system that embeds relevant codebase snippets into the LLM prompt, enabling generation of code that matches existing style and architecture.","intents":["I want to generate React components that use my existing component library and design system, not create duplicates","I need generated code to follow my team's naming conventions, import patterns, and folder structure","I want the LLM to understand my codebase architecture and generate code that fits naturally into it"],"best_for":["teams integrating AI design generation into existing React/Vue/Angular codebases","developers wanting generated code to match their project's conventions without refactoring","teams with established component libraries wanting to reuse them in generated designs"],"limitations":["Codebase analysis requires local access to source code — cloud-based codebases may require manual context provision","AST parsing is language-specific (TypeScript/JavaScript supported) — other languages require custom parsers","Large codebases (10,000+ files) may exceed context window limits, requiring selective context sampling"],"requires":["Local access to codebase (or codebase context provided as JSON)","TypeScript/JavaScript codebase (other languages require custom configuration)","LLM provider with sufficient context window (8K+ tokens recommended)"],"input_types":["codebase path or context (AST, file structure, component definitions)","design brief or specification"],"output_types":["React/Vue/Angular components matching codebase patterns","import statements using existing component library","code that follows project naming conventions and folder structure"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexu-io--open-design__cap_12","uri":"capability://planning.reasoning.visual.design.feedback.loop.with.iterative.refinement","name":"visual design feedback loop with iterative refinement","description":"Enables iterative design refinement through a feedback loop where users provide visual or textual feedback on generated designs, and the agent regenerates designs incorporating the feedback. Implements a diff-based approach that highlights changes between iterations, helping users understand what changed. Uses a feedback-parsing system that interprets natural language feedback (e.g., 'make the button bigger', 'use a warmer color palette') and translates it into generation parameters for the next iteration.","intents":["I want to refine a generated design by saying 'make the colors warmer' or 'increase the spacing' without rewriting the entire brief","I need to see what changed between design iterations so I can approve or reject specific changes","I want to iterate on a design 5-10 times with the AI until it matches my vision"],"best_for":["designers collaborating with AI to refine designs iteratively","product teams validating design directions through rapid iteration","teams wanting human-in-the-loop design generation"],"limitations":["Feedback parsing is heuristic-based — ambiguous feedback may be misinterpreted, requiring clarification","Each iteration requires a new LLM call, adding latency (30-60 seconds per iteration)","Diff visualization is limited to code-level changes — visual diffs require rendering both versions"],"requires":["Initial generated design","User feedback (text or visual annotations)"],"input_types":["generated design code","natural language feedback (text)","optional: visual annotations or reference images"],"output_types":["refined design code","change diff (what changed between iterations)","feedback interpretation log (how feedback was parsed)"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexu-io--open-design__cap_13","uri":"capability://data.processing.analysis.design.system.token.extraction.from.reference.designs","name":"design system token extraction from reference designs","description":"Analyzes reference designs (images, Figma files, existing websites) and extracts design system tokens (colors, typography, spacing, shadows) automatically. Uses image analysis and DOM parsing to identify visual patterns, then generates a design system JSON file with extracted tokens. Implements a token-mapping system that normalizes extracted values (e.g., 'font-size: 16px' → 'body-text') and creates semantic token names.","intents":["I have a Figma design or website screenshot and want to extract its design system tokens automatically","I want to reverse-engineer a competitor's design system from their website","I need to convert an existing design system (Figma, Sketch) into a JSON token file for use in generation"],"best_for":["teams migrating from Figma/Sketch to code-based design systems","designers wanting to extract tokens from reference designs without manual specification","teams reverse-engineering design systems from existing products"],"limitations":["Token extraction is heuristic-based — complex or non-standard designs may produce inaccurate tokens","Semantic token naming requires manual review — auto-generated names may not match team conventions","Image-based extraction (screenshots, images) is less accurate than DOM parsing (Figma, websites)"],"requires":["Reference design (image, Figma file, website URL, or HTML)","Optional: custom token naming conventions (JSON)"],"input_types":["reference design image (PNG, JPG)","Figma file (requires Figma API access)","website URL (requires DOM parsing)","HTML file"],"output_types":["design system tokens JSON (colors, typography, spacing, shadows)","token extraction report (confidence scores, manual review recommendations)","semantic token mapping"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexu-io--open-design__cap_14","uri":"capability://code.generation.editing.design.to.code.with.performance.profiling.and.optimization","name":"design-to-code with performance profiling and optimization","description":"Generates design code and automatically profiles it for performance (bundle size, render time, CSS specificity, unused styles), then optimizes based on profiling results. Uses tools like Lighthouse, Bundle Analyzer, and CSS analysis to identify bottlenecks, then applies optimizations (code splitting, CSS purging, lazy loading, image optimization). Generates a performance report with metrics before and after optimization.","intents":["I want to generate design code that's not just functional but also performant (fast load times, small bundle size)","I need to know the performance impact of generated designs before deploying them","I want the LLM to automatically optimize generated code for performance without manual tuning"],"best_for":["performance-conscious teams building high-traffic products","teams with strict performance budgets (e.g., Core Web Vitals targets)","developers wanting to understand performance implications of generated designs"],"limitations":["Performance profiling requires building/bundling the code, adding 20-60 seconds per generation","Optimizations are heuristic-based — some may not apply to specific use cases or may conflict with design requirements","Image optimization requires external services (ImageOptim, TinyPNG) or local tools"],"requires":["Generated design code (React/HTML/CSS)","Build tools (Webpack, Vite, or similar) for bundling and profiling","Optional: performance budget configuration (max bundle size, target Lighthouse score)"],"input_types":["generated design code","performance budget (optional, uses defaults if not provided)"],"output_types":["optimized design code","performance report (bundle size, render time, Lighthouse score before/after)","optimization recommendations (what was optimized and why)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexu-io--open-design__cap_15","uri":"capability://code.generation.editing.multi.language.code.generation.with.framework.support","name":"multi-language code generation with framework support","description":"Generates design code in multiple frameworks and languages (React, Vue, Angular, Svelte, HTML/CSS, Tailwind, Bootstrap) from a single design specification. Uses a framework-agnostic intermediate representation (AST-like) that each framework exporter transforms into target syntax. Implements framework-specific optimizations (e.g., Vue's scoped styles, React hooks patterns, Angular dependency injection) so generated code follows framework conventions.","intents":["I want to generate the same design as React, Vue, and Angular components without creating separate designs","I need to generate Tailwind-based components that use Tailwind's utility classes instead of custom CSS","I want to generate code that follows my framework's conventions and best practices"],"best_for":["teams using multiple frameworks (React, Vue, Angular) that need consistent design generation","agencies building designs for clients with different tech stacks","developers wanting to generate Tailwind or Bootstrap components"],"limitations":["Framework-specific features (Vue slots, Angular directives, React hooks) may not translate perfectly across frameworks","Some frameworks have limitations that prevent perfect parity (e.g., CSS-in-JS not available in all frameworks)","Framework-specific optimizations add complexity — some edge cases may require manual adjustment"],"requires":["Design specification","Target framework selection (React, Vue, Angular, Svelte, HTML/CSS, Tailwind, Bootstrap)"],"input_types":["design brief or specification","framework selection (enum)"],"output_types":["React components (JSX/TSX with hooks)","Vue components (SFC with scoped styles)","Angular components (TypeScript with decorators)","Svelte components","HTML/CSS","Tailwind components (using utility classes)","Bootstrap components"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexu-io--open-design__cap_2","uri":"capability://code.generation.editing.71.pre.built.design.system.templates.with.brand.grade.quality","name":"71 pre-built design system templates with brand-grade quality","description":"Bundles 71 curated design systems (Material Design, Tailwind, Bootstrap, custom brand systems) as reusable templates that inject design tokens (colors, typography, spacing, components) into generated designs. Uses a template-injection pattern where selected design system is applied as a constraint layer during code generation, ensuring all outputs conform to the system's visual language. Templates include semantic token mappings (e.g., 'primary-color' → specific hex value) and component libraries that the generation skills reference.","intents":["I want to generate designs that automatically match my brand's design system without manually applying styles","I need to ensure all AI-generated prototypes comply with our design system constraints (colors, spacing, typography)","I want to quickly prototype using Material Design or Tailwind without building a custom design system"],"best_for":["enterprises with established design systems wanting to automate prototype generation","agencies building client prototypes that must match brand guidelines","startups using Material Design or Tailwind who want instant design consistency"],"limitations":["Pre-built templates are static — custom brand systems require manual JSON token definition (no visual editor for token creation)","Template coverage is limited to popular systems; niche or highly-customized design systems may require significant adaptation","Token injection may conflict with LLM's design preferences, requiring manual override in ~15-20% of cases"],"requires":["Selection of one of 71 pre-built design systems OR custom design system JSON with token definitions","Design brief or specification to apply the system to"],"input_types":["design system selection (enum from 71 templates)","custom design system JSON (Figma tokens, Style Dictionary, or custom format)","design brief or component specifications"],"output_types":["HTML/CSS/React code conforming to selected design system","design token application logs","component library exports"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexu-io--open-design__cap_3","uri":"capability://code.generation.editing.multi.format.prototype.export.with.sandboxed.preview","name":"multi-format prototype export with sandboxed preview","description":"Generates design prototypes in 5+ output formats (HTML, React, PDF, PPTX, MP4) with a sandboxed iframe-based preview system that renders outputs without executing untrusted code. Uses a format-agnostic intermediate representation (AST-like structure) that each exporter transforms into target format. Sandboxing is implemented via iframe isolation with Content Security Policy (CSP) headers, preventing XSS attacks while allowing interactive preview of generated designs.","intents":["I want to preview a generated design in the browser before exporting it to production","I need to export a design as a PDF for stakeholder review or as a PPTX for a pitch deck","I want to generate a video walkthrough of a prototype for design documentation"],"best_for":["design teams needing multi-format exports for different stakeholder audiences","product managers creating pitch decks with AI-generated mockups","developers building design documentation with video walkthroughs"],"limitations":["Video export (MP4) is limited to simple animations and transitions — complex interactive prototypes may not translate well to video","PDF export loses interactivity — only suitable for static design review, not clickable prototypes","Sandboxed preview adds ~200-500ms latency due to iframe initialization and CSP header processing","PPTX export requires manual slide layout — complex multi-page designs may require post-export adjustment"],"requires":["Generated design code (HTML/React/CSS)","For video export: FFmpeg installed locally OR cloud video rendering service API key","For PPTX export: optional custom slide templates"],"input_types":["generated design code (HTML, React, CSS)","design metadata (title, description, dimensions)"],"output_types":["HTML (standalone, self-contained)","React component (JSX/TSX)","PDF (static design mockup)","PPTX (presentation slides)","MP4 (video walkthrough)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexu-io--open-design__cap_4","uri":"capability://code.generation.editing.responsive.design.generation.with.device.specific.adaptation","name":"responsive design generation with device-specific adaptation","description":"Automatically generates responsive layouts that adapt to mobile, tablet, and desktop viewports using a breakpoint-aware code generation approach. Implements CSS media queries and responsive component patterns (e.g., mobile-first Flexbox, desktop Grid) based on device specifications. Uses a device-profile system that defines breakpoints, touch targets, and viewport constraints, then generates code that respects these constraints across all output formats.","intents":["I want to generate a design that looks good on mobile, tablet, and desktop without manually creating three separate designs","I need to ensure touch targets are large enough on mobile and spacing respects mobile constraints","I want to generate responsive React components that use Tailwind's responsive utilities (sm:, md:, lg:)"],"best_for":["mobile-first development teams building cross-device products","agencies creating responsive prototypes for client presentations","developers using Tailwind or Bootstrap who need responsive component generation"],"limitations":["Complex responsive behaviors (e.g., sidebar collapse, hamburger menu logic) may require manual JavaScript implementation","Device profiles are predefined (iPhone, iPad, desktop) — custom device specs require JSON configuration","Responsive images and media queries for different pixel densities (2x, 3x) are not automatically generated"],"requires":["Design brief with responsive requirements","Optional: custom device profile JSON (breakpoints, viewport widths, touch target sizes)"],"input_types":["design brief or wireframe","device profile specifications (breakpoints, viewport widths)","design system tokens"],"output_types":["HTML with CSS media queries","React components with Tailwind responsive utilities (sm:, md:, lg:)","responsive design metadata (breakpoints used, mobile-first approach)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexu-io--open-design__cap_5","uri":"capability://code.generation.editing.accessibility.first.code.generation.with.wcag.compliance","name":"accessibility-first code generation with wcag compliance","description":"Generates HTML/React code with semantic markup, ARIA labels, color contrast validation, and keyboard navigation support built-in. Uses an accessibility-constraint layer that validates generated code against WCAG 2.1 AA standards during generation, automatically fixing issues like insufficient color contrast, missing alt text, and non-semantic HTML. Implements a11y linting that runs post-generation to flag remaining accessibility issues.","intents":["I want to generate designs that are accessible by default without manual ARIA label addition","I need to ensure generated designs meet WCAG 2.1 AA color contrast requirements","I want to generate semantic HTML with proper heading hierarchy and landmark regions"],"best_for":["teams building accessible products with legal/compliance requirements","government and public sector organizations required to meet WCAG standards","inclusive design advocates wanting accessibility built into generation, not bolted on"],"limitations":["Automated accessibility fixes can't handle all edge cases — complex interactive patterns (custom dropdowns, modals) may require manual ARIA refinement","Color contrast validation is limited to text on solid backgrounds — gradients and images require manual review","Keyboard navigation is generated for standard components but custom interactive elements need manual testing"],"requires":["Design brief or specification","WCAG 2.1 AA compliance target (or custom a11y rules)"],"input_types":["design brief","design system with color tokens","optional: custom accessibility rules (JSON)"],"output_types":["semantic HTML with ARIA labels","React components with accessibility props","accessibility audit report (WCAG violations found and fixed)","keyboard navigation map"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexu-io--open-design__cap_6","uri":"capability://code.generation.editing.component.library.extraction.and.reusability","name":"component library extraction and reusability","description":"Extracts reusable components from generated designs and organizes them into a component library with props, variants, and composition patterns. Uses a component-detection algorithm that identifies repeated patterns in generated code and abstracts them into parameterized components. Generates component documentation (Storybook-compatible format) and TypeScript type definitions for each component, enabling seamless integration into existing codebases.","intents":["I want to extract reusable components from a generated design and use them in my React codebase","I need to generate a component library with variants (primary, secondary, disabled states) from a design","I want to document generated components with Storybook so my team can discover and use them"],"best_for":["design systems teams automating component library generation","React teams wanting to convert AI-generated designs into production-ready components","teams using Storybook for component documentation and discovery"],"limitations":["Component extraction is heuristic-based — complex or highly-customized components may not be detected correctly","Variant generation is limited to visual variants (colors, sizes) — behavioral variants (loading, error states) require manual implementation","TypeScript type definitions are auto-generated but may require manual refinement for complex prop shapes"],"requires":["Generated design code (HTML/React)","Optional: Storybook configuration for documentation generation"],"input_types":["generated design code (React/HTML)","design system tokens (for variant generation)"],"output_types":["React components with props and variants","TypeScript type definitions (.d.ts)","Storybook stories (.stories.tsx)","component documentation (Markdown)"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexu-io--open-design__cap_7","uri":"capability://code.generation.editing.hyperframes.interactive.prototype.generation","name":"hyperframes interactive prototype generation","description":"Generates interactive prototypes with click-through flows, state management, and user interactions (form submissions, animations, navigation) using a HyperFrames abstraction that maps design interactions to executable code. Implements a state-machine pattern for managing prototype states and transitions, generating JavaScript/React code that handles user interactions without requiring manual event handler implementation. Supports linking between frames, conditional navigation, and data binding.","intents":["I want to generate a clickable prototype with working navigation between screens without manually coding interactions","I need to create a form prototype that validates input and shows success/error states","I want to generate a prototype with animations and transitions that respond to user interactions"],"best_for":["product teams creating interactive prototypes for user testing","designers wanting to validate interaction flows without coding","teams building rapid prototypes for stakeholder feedback"],"limitations":["Complex interactions (drag-and-drop, real-time collaboration, WebSocket-based features) are not supported — limited to click-through flows","State management is limited to client-side state — backend integration requires manual API implementation","Animations are CSS-based — complex physics-based or canvas animations require manual implementation"],"requires":["Design brief with interaction specifications (which screens link to which, form validations, etc.)","Optional: interaction rules JSON (state transitions, validation logic)"],"input_types":["design brief with interaction flows","screen/frame specifications","interaction rules (navigation, form validation, animations)"],"output_types":["React components with state management (useState, useReducer)","HTML with vanilla JavaScript event handlers","interaction flow documentation","prototype deployment package"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexu-io--open-design__cap_8","uri":"capability://code.generation.editing.design.to.code.with.ai.powered.code.review.and.refinement","name":"design-to-code with ai-powered code review and refinement","description":"Generates initial design code, then uses the LLM to review, refactor, and optimize the generated code for performance, maintainability, and best practices. Implements a multi-pass generation approach where the first pass creates functional code, subsequent passes apply code quality improvements (reducing bundle size, improving accessibility, optimizing CSS), and a final review pass flags potential issues. Uses static analysis (ESLint, Prettier) integrated with LLM feedback for comprehensive code quality.","intents":["I want to generate design code that's not just functional but also follows best practices and is production-ready","I need to optimize generated code for performance (bundle size, render performance) without manual refactoring","I want the LLM to review generated code and suggest improvements before I use it in production"],"best_for":["teams wanting production-ready code from design generation, not just prototypes","developers who want AI-assisted code review as part of the generation pipeline","performance-conscious teams needing optimized CSS and JavaScript"],"limitations":["Multi-pass generation adds 30-60% latency to the generation process due to additional LLM calls","Code quality improvements are heuristic-based — some suggestions may not apply to specific use cases","Static analysis tools (ESLint, Prettier) may conflict with LLM suggestions, requiring manual resolution"],"requires":["Generated design code (HTML/React)","ESLint and Prettier configuration (optional, uses defaults if not provided)","LLM provider with sufficient context window for code review"],"input_types":["generated design code (React/HTML/CSS)","code quality rules (ESLint config, custom rules)"],"output_types":["refactored, optimized code","code review report (issues found, improvements applied)","performance metrics (bundle size, render time estimates)","best practices checklist"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-nexu-io--open-design__cap_9","uri":"capability://safety.moderation.design.system.compliance.validation.and.enforcement","name":"design system compliance validation and enforcement","description":"Validates generated designs against design system rules (color palette, typography scale, spacing grid, component usage) and enforces compliance by rejecting or auto-correcting non-compliant code. Uses a constraint-validation layer that checks generated code against design system specifications before export, flagging violations and suggesting corrections. Implements a compliance report that documents which design system rules were applied and which violations were found.","intents":["I want to ensure all generated designs strictly comply with our design system without manual review","I need to prevent designers from using off-brand colors or typography in generated prototypes","I want a compliance report showing which design system rules were applied to each generated design"],"best_for":["enterprises with strict design system governance requirements","teams managing multiple brands that need to enforce brand-specific design systems","design systems teams automating compliance checks"],"limitations":["Compliance validation is rule-based — edge cases or exceptions require manual override","Auto-correction may produce designs that don't match the original intent — manual review recommended for critical designs","Custom design system rules require JSON specification — no visual rule builder"],"requires":["Design system specification with rules (colors, typography, spacing, components)","Generated design code to validate"],"input_types":["design system rules (JSON format)","generated design code (React/HTML/CSS)"],"output_types":["compliance report (violations found, rules applied)","corrected design code (auto-fixed violations)","compliance metrics (% of design system coverage)"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["API keys for at least one supported LLM provider (Claude, Gemini, Copilot, etc.) OR local model runtime (Ollama, vLLM)","Node.js 18+ for agent runtime","TypeScript 5.0+ for type safety across provider integrations","Design brief or specification (text, image, or structured format)","Optional: design system tokens (colors, typography, spacing scales) as JSON","LLM provider access (Claude recommended for best results)","Workflow template definition (JSON or YAML)","Batch input data (CSV, JSON, or API endpoint)","LLM provider with sufficient rate limits for batch processing","Local access to codebase (or codebase context provided as JSON)"],"failure_modes":["Provider abstraction adds ~50-100ms overhead per skill invocation due to schema normalization","Not all providers support identical function-calling capabilities — some skills may degrade gracefully on certain models","Local execution requires significant GPU memory (8GB+ recommended) for running models like Hermes locally","Skill composition is sequential — parallel skill execution not yet supported, limiting throughput for complex designs","Some skills have hard dependencies (e.g., responsive adaptation requires layout skill output), reducing flexibility in skill ordering","Generated designs may require 10-20% manual refinement for production use due to edge cases in complex layouts","Batch processing is sequential by default — parallel execution requires manual configuration and may exceed API rate limits","Template-based workflows are static — dynamic parameter adjustment during batch execution is not supported","Large batches (100+ designs) may take hours to complete, requiring background job management","Codebase analysis requires local access to source code — cloud-based codebases may require manual context provision","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7246082447108204,"quality":0.6,"ecosystem":0.7000000000000001,"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":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:58:21.998Z","last_commit":"2026-05-03T13:16:42Z"},"community":{"stars":17920,"forks":1998,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=nexu-io--open-design","compare_url":"https://unfragile.ai/compare?artifact=nexu-io--open-design"}},"signature":"dveAWU36cx6ow6kzilX+wYeZw6dzj7ezh5dXlJfGndtn0KVGBL0ulxxUgBgQsd7/PvIfH2qkHx6gpxwuCb1dBg==","signedAt":"2026-06-21T04:06:43.204Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/nexu-io--open-design","artifact":"https://unfragile.ai/nexu-io--open-design","verify":"https://unfragile.ai/api/v1/verify?slug=nexu-io--open-design","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"}}