| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Provides 9 pre-built AI Skill templates that enforce 13 coding standards through a single npm install command. Templates are structured as reusable patterns for Vue 3 projects, with built-in linting rules and code style guidelines that automatically apply to imported skills. The framework uses a convention-over-configuration approach where each skill template includes standardized folder structures, naming conventions, and TypeScript/Vue 3 type definitions.
Unique: Bundles 13 coding standards + 9 AI Skill templates + 14 MCP Tools in a single installable package specifically optimized for Vue 3, with automatic enforcement on import rather than post-hoc linting
vs alternatives: More opinionated and integrated than generic Vue 3 scaffolders, providing AI-skill-specific standards and MCP tool bindings out-of-the-box rather than requiring manual configuration
Exposes 14 pre-configured Model Context Protocol (MCP) tools that integrate with AI editors (Cursor, Windsurf, Kiro) through a standardized tool registry. Each tool is pre-wired with schema definitions, input/output validation, and error handling. The framework manages tool discovery, schema serialization, and protocol-level communication without requiring developers to write MCP boilerplate.
Unique: Pre-packages 14 MCP tools with full schema definitions and error handling, eliminating the need for developers to write MCP protocol code or schema validation manually
vs alternatives: Faster integration than building custom MCP tools from scratch or using generic tool libraries, because schemas and bindings are pre-validated for Vue 3 + AI editor workflows
Collects performance metrics for skill execution including latency, error rates, and resource usage. The framework automatically instruments skills to measure execution time, token usage (for AI models), and error frequency. Metrics are exposed via a metrics API and can be exported to monitoring systems like Prometheus or DataDog for dashboarding and alerting.
Unique: Automatically instruments skills for performance monitoring without requiring manual metric collection code, with built-in support for AI-specific metrics like token usage
vs alternatives: More integrated than generic APM tools because it understands skill semantics and can correlate performance metrics with skill parameters and AI model usage
Automatically generates documentation for skills from their TypeScript definitions, including parameter descriptions, return types, and usage examples. The framework extracts JSDoc comments, type information, and error handling patterns from skill code and generates Markdown documentation that can be published to a documentation site. Documentation is kept in sync with skill definitions through automated generation.
Unique: Automatically generates skill documentation from TypeScript definitions and JSDoc comments, eliminating manual documentation maintenance and keeping docs in sync with code
vs alternatives: More integrated than generic documentation generators because it understands skill structure and can generate skill-specific documentation sections like parameter validation rules and error handling
Manages context and state across multiple skill invocations in a conversation or workflow. The framework maintains a context object that persists across skill calls, allowing skills to access previous results, user preferences, and conversation history. Context can be serialized and stored for resuming interrupted workflows, with built-in support for context isolation and cleanup.
Unique: Provides built-in context management for multi-turn skill execution with automatic context passing between skills, eliminating manual context threading in skill definitions
vs alternatives: More integrated than generic state management libraries because it understands skill execution semantics and can automatically manage context lifecycle across skill chains
Enables developers to compose multiple AI Skills into workflows where outputs from one skill feed into inputs of another. The framework manages skill state, error propagation, and context passing between skills using a pipeline pattern. Skills are registered in a skill registry and can be invoked sequentially or conditionally based on runtime logic, with built-in support for skill dependency resolution.
Unique: Provides a skill registry pattern with automatic dependency resolution and type-safe composition, allowing skills to be chained without manual context management or protocol conversion
vs alternatives: More lightweight than full workflow orchestration platforms (like Temporal or Airflow), but more structured than ad-hoc skill calling, with Vue 3-specific optimizations
Enforces TypeScript-based skill definitions where input/output types are declared at definition time and validated at runtime. Each skill is a TypeScript class or function with strict type signatures, and the framework performs schema validation on skill invocation using the declared types. This enables IDE autocomplete, compile-time type checking, and runtime safety without requiring separate schema files.
Unique: Combines TypeScript type definitions with runtime validation, eliminating the need for separate schema files (like JSON Schema) while maintaining both compile-time and runtime safety
vs alternatives: Tighter integration with TypeScript tooling than schema-based approaches, reducing boilerplate and enabling IDE features like refactoring across skill definitions
Automatically exposes installed AI Skills to Cursor, Windsurf, and Kiro editors through a standardized plugin interface. The framework scans the skill registry at editor startup and registers each skill as an available action in the editor's command palette and context menus. Skills are discoverable without manual configuration, and editor context (selected code, file path, project structure) is automatically passed to skills.
Unique: Implements automatic skill discovery and registration in AI editors without requiring manual plugin configuration, with built-in editor context passing for seamless skill invocation
vs alternatives: More integrated than generic editor extensions because skills are automatically discovered from the project's skill registry, reducing setup friction compared to manually configuring each skill in editor settings
+5 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Supabase scores higher at 42/100 vs @agile-team/wl-skills-kit at 25/100.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities