| 0 |
| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 12 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Implements a BM25 ranking algorithm in core.py that searches across 344+ design resources stored in CSV databases covering 10 domains (styles, colors, typography, landing patterns, charts, UX guidelines, icons, products, reasoning rules) and 16 technology stacks. The search engine automatically detects the user's design domain context and filters results by stack-specific guidelines, returning ranked design recommendations that match both semantic intent and technical constraints.
Unique: Uses BM25 algorithm with automatic domain detection and stack-specific filtering in a single search pass, rather than requiring separate domain classification and filtering steps like traditional design tools
vs alternatives: Faster and more contextually accurate than manual design library searches because it ranks results by relevance to both design intent and technology stack simultaneously
The design_system.py reasoning engine performs sequential multi-domain searches (colors, typography, patterns, guidelines) and synthesizes complete design systems using a Master + Overrides architectural pattern. This pattern defines a master design configuration that can be selectively overridden per platform or component, enabling consistent design systems across 18+ AI platforms while maintaining platform-specific customizations without duplication.
Unique: Uses Master + Overrides pattern to generate platform-specific design systems from a single master definition, eliminating duplication and ensuring consistency across 18+ AI platforms through structured inheritance rather than copy-paste
vs alternatives: More maintainable than generating separate design systems per platform because changes to the master configuration automatically propagate to all platforms unless explicitly overridden
The system integrates with Claude Marketplace through a .claude-plugin/ directory structure that enables direct plugin installation for Claude Code users. The skill.json manifest declares capabilities and activation triggers, allowing the plugin to activate automatically when users request UI/UX work within Claude, with design resources and reasoning engine accessible through Claude's native function-calling interface.
Unique: Integrates directly with Claude Marketplace through .claude-plugin/ directory structure and skill.json manifest, enabling native plugin installation and automatic activation within Claude Code without requiring external CLI tools
vs alternatives: More seamless than external plugin installation because it integrates natively with Claude's plugin system, enabling automatic activation and direct access to Claude's function-calling interface without context switching
The system includes a pre-delivery checklist capability that validates generated designs against accessibility, performance, and consistency standards before delivery to users. The checklist is generated from reasoning rules and stack-specific guidelines, checking for common issues (color contrast, responsive design, component naming, design token usage) and providing actionable feedback for remediation.
Unique: Generates context-aware validation checklists from reasoning rules and stack-specific guidelines, checking designs against both universal standards (accessibility, performance) and team-specific conventions rather than applying generic validation rules
vs alternatives: More comprehensive than manual design review because it automatically checks against multiple validation dimensions (accessibility, performance, consistency, naming) in a single pass, reducing human review burden
The CLI tool's detectAIType() function in detect.ts identifies the user's AI coding assistant environment (Claude, Cursor, Windsurf, Copilot, etc.) by analyzing file system markers, environment variables, and configuration files. Once detected, the template generation system in template.ts automatically generates platform-specific configuration files from JSON templates (augment.json, kilocode.json, warp.json), enabling zero-configuration installation across 18+ supported platforms.
Unique: Combines file system introspection with environment variable analysis to detect AI platform type without user input, then generates platform-specific files from parameterized JSON templates rather than requiring manual configuration per platform
vs alternatives: Faster and more reliable than manual platform selection because it automatically discovers the correct environment and generates compatible files, reducing setup time from minutes to seconds
The system maintains stack-specific guideline configurations that filter and customize design recommendations based on technology stack (React, Vue, Tailwind, HTML5, etc.). When a user requests UI/UX work, the skill automatically detects the target stack from code context or user input, then filters design resources and applies stack-specific guidelines from the CSV database, ensuring generated designs follow framework conventions and best practices.
Unique: Maintains separate guideline rows per technology stack in CSV database and applies stack-specific filtering at search time, ensuring design recommendations automatically conform to framework conventions rather than requiring post-generation manual adjustment
vs alternatives: More accurate than generic design recommendations because it filters by framework-specific patterns (React hooks, Vue composition API, Tailwind utilities) rather than treating all stacks identically
The system stores 344+ design resources in CSV format across 10 domain-specific files (colors.csv, typography.csv, patterns.csv, etc.), with a source-of-truth synchronization pattern that maintains consistency between CLI templates and skill definitions. Each CSV row contains design metadata (name, description, stack, domain, implementation code) and is indexed for BM25 search, enabling version control, offline access, and collaborative design database management without requiring a backend database.
Unique: Uses CSV files as the primary persistence layer with source-of-truth synchronization between CLI and skill definitions, enabling Git-based version control and collaborative editing without requiring database infrastructure or API servers
vs alternatives: More accessible than database-backed design systems because CSV files are human-readable, version-controllable, and editable without specialized tools, making it easier for non-technical team members to contribute design resources
The CLI tool orchestrates installation across 18+ AI platforms (Claude, Cursor, Windsurf, Copilot, Augment, Kiro, Qoder, Trae, etc.) by generating platform-specific skill or workflow files from templates and placing them in platform-specific directories. The skill.json manifest defines activation triggers and capabilities, enabling automatic activation when users request UI/UX work, with platform-specific behavior controlled through configuration overrides.
Unique: Generates platform-specific skill/workflow files from parameterized templates and manages installation across 18+ AI platforms with unified CLI, rather than requiring separate installation procedures per platform
vs alternatives: Faster and more reliable than manual installation because it autodetects platforms, generates compatible files, and verifies installation in a single command, reducing setup complexity from per-platform configuration to unified orchestration
+4 more capabilities
Generates single-line and multi-line code suggestions in real-time as developers type, using semantic indexing of the entire codebase to retrieve relevant type definitions, function signatures, and contextual patterns. The system maintains a 1M token context window (Pro/Team tiers) that enables suggestions informed by distant code definitions and cross-file dependencies, constructed via local codebase semantic search rather than simple token-based recency. Suggestions adapt to detected coding style on Pro/Team tiers through implicit pattern learning from recent edits.
Unique: 1M token context window with codebase-wide semantic indexing enables suggestions informed by distant code definitions and cross-file patterns, versus competitors (Copilot, Tabnine) that typically use fixed context windows (4K-32K tokens) or file-local context. Claimed 250ms latency suggests optimized retrieval pipeline, though indexing mechanism and performance at scale remain undisclosed.
vs alternatives: Larger context window than GitHub Copilot (8K-32K tokens) and faster latency than unnamed competitors (250ms vs 783ms claimed), enabling suggestions on large codebases with minimal typing delay; trade-off is cloud dependency and undisclosed free tier limitations.
Provides a separate chat interface supporting multiple LLM backends (GPT-4o, Claude 3.5 Sonnet, GPT-4, others) for conversational code assistance. Users attach files, reference recent edits, and trigger compiler diagnostic uploads; the system generates diffs and applies code changes directly to the editor. Model selection is per-conversation, and $5/month in credits (included in Pro/Team) covers external model API costs; overage pricing is undisclosed. Hotkey-driven workflow enables rapid context switching between inline completion and chat.
Unique: Multi-model chat interface with per-conversation model selection and integrated diff application, combined with compiler diagnostic auto-upload. Unlike Copilot Chat (single model per tier) or standalone ChatGPT, Supermaven Chat unifies multiple LLM backends in a single hotkey-driven workflow with direct editor integration for change application.
Supermaven scores higher at 71/100 vs ui-ux-pro-max-skill at 34/100. ui-ux-pro-max-skill leads on ecosystem, while Supermaven is stronger on adoption and quality.
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vs alternatives: Supports multiple LLM backends (GPT-4o, Claude 3.5 Sonnet) in one interface with included credits, whereas GitHub Copilot Chat is single-model per tier and requires separate ChatGPT subscription for model switching; trade-off is credit limits and unknown overage pricing.
Supermaven Chat can automatically upload compiler diagnostic messages (errors, warnings) alongside code context to provide error-aware suggestions and fixes. The mechanism is described as 'automatically uploading your code together with compiler diagnostic messages,' but specific language/compiler support and the upload trigger mechanism are undisclosed. This feature is Chat-only and not available in inline completion.
Unique: Automatic compiler diagnostic upload in Chat for error-aware suggestions, versus competitors (Copilot, Tabnine) that require manual error context or have limited diagnostic integration. Supermaven's approach reduces friction but with undisclosed language/compiler support.
vs alternatives: Automatic diagnostic upload reduces manual context-gathering compared to manual copy-paste; trade-off is undisclosed language support and unclear upload trigger mechanism.
Supermaven offers a 30-day free trial of the Pro tier ($10/month), providing full access to 1M token context window, largest model, style adaptation, and $5/month chat credits. No credit card is required to start the trial (implied), and trial conversion to paid is automatic after 30 days unless cancelled. Trial terms and auto-renewal policy are not explicitly detailed.
Unique: 30-day free trial of Pro tier with full feature access (1M context, largest model, chat credits), versus competitors (Copilot 2-month free trial, Tabnine free tier only) with different trial lengths and feature access. Supermaven's approach is generous but with undisclosed auto-renewal terms.
vs alternatives: Full Pro feature access during trial compared to limited free tier; trade-off is undisclosed auto-renewal policy and potential unexpected charges if not cancelled.
Supermaven requires internet connectivity and server-side inference; no offline mode or local inference capability is mentioned or available. All code completion requests are sent to Supermaven's backend servers for processing, and responses are returned over the network. This creates a hard dependency on network connectivity and Supermaven's service availability; if the service is down or network is unavailable, code completion is not available.
Unique: Supermaven has no offline mode or local inference capability; all processing is server-side. GitHub Copilot also requires server-side inference, but Tabnine offers local inference options for some use cases. Supermaven's lack of offline capability is a significant limitation for developers with connectivity constraints.
vs alternatives: Supermaven's server-side-only approach is comparable to GitHub Copilot; Tabnine offers local inference options, making Tabnine more suitable for offline work. Supermaven's lack of offline capability is a weakness vs. Tabnine.
Analyzes recent code edits and inferred coding patterns to adapt inline suggestions to match team conventions, naming patterns, and structural preferences. The mechanism is implicit (not explicit fine-tuning) and operates only on Pro/Team tiers, suggesting pattern learning from editor activity rather than explicit configuration. Free tier uses a single base model without personalization.
Unique: Implicit style adaptation via editor activity analysis without explicit configuration, versus competitors (Copilot, Tabnine) that require manual style guides or explicit fine-tuning. Supermaven's approach is transparent to the user but also non-configurable and undisclosed in mechanism.
vs alternatives: Requires no manual style configuration compared to tools requiring explicit style guides; trade-off is lack of transparency and inability to control or export learned styles.
Delivers code suggestions to the editor inline as the developer types, with a claimed baseline latency of 250ms from keystroke to suggestion display. The system uses a cloud inference backend and local editor plugin to minimize round-trip time. Latency claim is positioned against an unnamed competitor (783ms), but methodology is undisclosed and no independent verification is provided.
Unique: Claimed 250ms latency via optimized cloud inference pipeline and editor plugin architecture, versus competitors with higher latency (783ms unnamed baseline). Actual differentiation is undisclosed; mechanism may involve request batching, model quantization, or edge caching, but specifics are not public.
vs alternatives: Faster than unnamed competitor (250ms vs 783ms claimed); trade-off is cloud dependency and unverified latency claim with no SLA or performance guarantee.
Provides native editor extensions for VS Code, JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.), and Neovim, enabling inline suggestion rendering, hotkey-driven chat access, and compiler diagnostic integration directly within the editor. Each plugin variant is maintained separately and integrates with the editor's native autocomplete UI, keybinding system, and file context APIs.
Unique: Native plugins for three major editor ecosystems (VS Code, JetBrains, Neovim) with integrated chat and diff application, versus competitors (Copilot, Tabnine) that support broader editor ecosystems but with less deep integration in some cases. Supermaven's approach prioritizes depth over breadth.
vs alternatives: Deep integration with VS Code and JetBrains (native autocomplete UI, hotkey system) compared to web-based tools or lighter integrations; trade-off is limited editor support (no Sublime, Vim, Emacs) and undisclosed Neovim support details.
+5 more capabilities