| 0 |
| 1 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 12 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Automatically detects active AI coding agents (Antigravity, Gemini CLI, Claude Code, Cursor) on the developer's system and installs standardized skills into agent-specific directories without manual configuration. Uses a skills CLI that scans the filesystem for agent installation paths and deploys skills following the Agent Skills open standard directory structure, enabling write-once-run-anywhere skill distribution across heterogeneous agent platforms.
Unique: Implements agent-agnostic skill distribution via automatic filesystem detection and standardized directory structure, eliminating the need for agent-specific skill versions or manual configuration per agent. The skills CLI acts as a universal installer that maps the Agent Skills open standard structure to each agent's expected skill location.
vs alternatives: Unlike agent-specific skill marketplaces (e.g., Copilot Extensions for VS Code only), Stitch Skills works across Cursor, Claude Code, Gemini CLI, and Antigravity with a single installation, reducing maintenance burden for skill developers and enabling seamless agent switching for users.
Provides a structured directory convention (SKILL.md, scripts/, resources/, examples/) that enables AI agents to consistently discover task instructions, validate outputs, and learn from reference implementations. Each skill follows the Agent Skills open standard, allowing agents to parse SKILL.md for mission/workflow/success criteria, execute validation scripts for quality enforcement, and reference example outputs for in-context learning without agent-specific adaptation.
Unique: Encodes skill semantics in a standardized directory structure (SKILL.md + scripts + resources + examples) that agents can parse and execute without custom integration, treating skills as self-contained, agent-agnostic modules. This contrasts with function-calling APIs that require schema definitions per provider.
vs alternatives: More portable than OpenAI/Anthropic function-calling schemas (which are provider-specific) and more discoverable than unstructured GitHub repositories because the standard structure enables agents to automatically locate instructions, validation logic, and examples without documentation parsing.
Provides syntactically valid reference implementations in the examples/ directory of each skill, enabling agents to learn expected output formats, coding patterns, and best practices through concrete examples. Agents can reference these examples during code generation to understand the desired output structure, style, and quality level, improving generation accuracy through in-context learning without requiring explicit instruction in SKILL.md.
Unique: Treats reference implementations as a first-class skill component (examples/ directory) that agents can reference during generation, enabling in-context learning without explicit instruction. This approach leverages agents' ability to learn from examples rather than relying solely on textual instructions.
vs alternatives: More effective than textual instructions alone because agents can learn patterns from concrete code, and more maintainable than hardcoded generation logic because examples can be updated independently of skill logic.
Provides structured reference materials, checklists, style guides, and API documentation in the resources/ directory of each skill, enabling agents to access design system guidelines, component specifications, and best practices during code generation. Resources serve as a knowledge base that agents can query to understand design system constraints, component APIs, styling conventions, and accessibility requirements, improving generation accuracy and consistency.
Unique: Organizes design system knowledge in a structured resources/ directory that agents can reference during code generation, treating design system documentation as a queryable knowledge base rather than static documentation. This approach enables agents to make informed decisions about component selection, styling, and accessibility without explicit instruction.
vs alternatives: More accessible than external design system documentation because resources are co-located with skill logic, and more actionable than unstructured documentation because resources are organized by type (checklists, style guides, API docs).
Transforms UI design data from the Stitch MCP Server into production-ready React components by first optimizing design prompts via the enhance-prompt skill, then generating component code via the react-components skill. The pipeline extracts design semantics (layout, styling, interactivity) from design files and synthesizes React/TypeScript code with proper component structure, prop interfaces, and styling integration, guided by optimized prompts that clarify design intent for the code generation model.
Unique: Chains the enhance-prompt skill (which optimizes design descriptions for code generation) with the react-components skill (which synthesizes React code), creating a two-stage pipeline that improves code quality by clarifying design intent before generation. This contrasts with single-stage design-to-code tools that generate code directly from design metadata without semantic optimization.
vs alternatives: More semantically aware than regex-based design-to-code converters because it uses LLM-based prompt optimization to extract and clarify design intent, and more flexible than template-based generators because it synthesizes code rather than filling templates.
Generates complete multi-page websites (HTML, CSS, JavaScript) from design specifications via the stitch-loop skill, which orchestrates iterative design-to-code transformation across multiple pages. The skill manages page-level decomposition, component reuse across pages, styling consistency, and navigation structure, producing a cohesive website codebase with shared component libraries and unified design system application.
Unique: Implements iterative design-to-code transformation via the stitch-loop skill, which decomposes multi-page websites into page-level tasks, manages component reuse across pages, and enforces styling consistency through a unified design system application. This orchestration approach enables scaling from single-page to multi-page generation without exponential complexity.
vs alternatives: More sophisticated than single-page design-to-code tools because it manages cross-page consistency and component reuse, and more maintainable than manually-coded websites because styling and components are generated from a single design source.
Provides structured guidance for integrating shadcn/ui components into generated code via the shadcn-ui skill, which includes a component catalog, customization patterns, migration guides, and best practices. The skill enables agents to select appropriate shadcn/ui components for design specifications, apply customization patterns (theming, variant composition), and generate code that leverages the shadcn/ui library instead of building components from scratch, reducing code generation complexity and improving consistency with a widely-used component library.
Unique: Encodes shadcn/ui component semantics, customization patterns, and best practices in a structured skill that agents can reference during code generation, enabling intelligent component selection and customization without requiring agents to parse shadcn/ui documentation. The skill includes a component catalog, customization guide, and migration guide as structured resources.
vs alternatives: More integrated than generic component library documentation because it's specifically designed for agent-driven code generation and includes customization patterns and migration guides, and more maintainable than hardcoding component logic because customization is externalized to the skill resources.
Generates comprehensive design system documentation (design-md skill) from design specifications in the Stitch MCP Server, producing markdown files that document design tokens, component definitions, usage patterns, and accessibility guidelines. The skill extracts semantic design information (colors, typography, spacing, components) from design metadata and synthesizes human-readable documentation that serves as a reference for developers and designers, enabling design-to-documentation transformation alongside design-to-code.
Unique: Transforms design metadata from Stitch MCP Server into structured markdown documentation via the design-md skill, enabling design-to-documentation generation alongside design-to-code. This approach treats documentation as a first-class output of the design system, not an afterthought, and keeps documentation synchronized with design specifications.
vs alternatives: More maintainable than manually-written design system documentation because it's generated from a single source of truth (design specifications), and more comprehensive than design tool exports because it synthesizes semantic documentation rather than exporting raw design data.
+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 stitch-skills at 43/100. stitch-skills 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