| Ecosystem |
| 1 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Generates boilerplate AI agent skill code through an interactive command-line interface that prompts developers for skill parameters (name, description, input/output schemas) and produces ready-to-use skill templates. Uses a guided questionnaire pattern to collect metadata, then renders templated code files with proper structure for immediate integration into AI SDK-based agents.
Unique: Provides interactive CLI-driven skill scaffolding specifically optimized for Vercel AI SDK agents, using guided prompts to capture skill semantics (name, description, input/output schemas) and generating immediately-runnable TypeScript templates with proper type definitions and integration hooks.
vs alternatives: Faster than manual skill creation or generic code generators because it understands AI SDK skill conventions and generates schema-aware, type-safe boilerplate in seconds rather than requiring manual file setup and schema definition.
Generates production-ready skill implementation files with proper TypeScript types, error handling patterns, and AI SDK integration hooks. The generator reads collected skill metadata (name, description, input/output schemas) and produces templated code that includes function signatures, parameter validation, and agent-compatible export patterns.
Unique: Generates type-safe skill implementations with schema-derived TypeScript interfaces, ensuring compile-time validation of skill input/output contracts and reducing runtime type errors in agent skill execution.
vs alternatives: More type-safe than copying generic skill examples because generated code includes schema-specific TypeScript interfaces and proper function signatures derived from the skill definition, catching schema mismatches at compile time rather than runtime.
Captures and validates skill metadata (name, description, input parameters, output format) through interactive CLI prompts, storing this as structured schema definitions that drive code generation and agent skill registration. Uses a questionnaire-based approach to collect semantic information about what the skill does and what data it accepts/produces.
Unique: Provides interactive schema definition through guided CLI prompts rather than requiring manual JSON/YAML editing, lowering the barrier for developers unfamiliar with JSON Schema or function-calling specifications.
vs alternatives: More accessible than writing JSON Schema directly because the CLI guides developers through parameter definition step-by-step, reducing schema definition errors and making the process discoverable for new users.
Generates skill code with built-in compatibility for Vercel AI SDK's tool/skill registration system, including proper export patterns, function signatures, and integration hooks that allow generated skills to be immediately registered with AI agents. Handles the boilerplate required to make skills discoverable and callable by the AI SDK's function-calling infrastructure.
Unique: Generates skills with native Vercel AI SDK integration patterns built-in, including proper tool schema registration, function signatures matching AI SDK expectations, and export patterns that make skills immediately discoverable by agents without additional wiring.
vs alternatives: Faster skill integration than manual setup because generated code already includes AI SDK-specific patterns (tool schema format, function signatures, export conventions), eliminating the need to consult documentation or debug integration issues.
Scaffolds complete skill project structures with multiple skills, directory organization, configuration files, and dependency management in a single operation. Generates not just individual skill files but the entire project layout including package.json, tsconfig, and skill registry patterns that support managing collections of related skills.
Unique: Generates entire skill project structures with proper organization, configuration, and dependency management in one operation, rather than requiring developers to manually create directory structures and configuration files for skill collections.
vs alternatives: Faster than manual project setup because it generates complete, production-ready project layouts with all necessary configuration files and skill organization patterns, reducing setup time from hours to minutes.
Provides interactive guidance during skill creation to help developers choose appropriate skill names, write clear descriptions, and document skill purpose in a way that's both human-readable and useful for LLM function-calling. The CLI prompts guide developers through naming conventions and documentation best practices specific to AI agent skills.
Unique: Provides interactive guidance on skill naming and documentation specifically tailored to AI agent function-calling requirements, helping developers write descriptions that are both human-readable and useful for LLM tool selection.
vs alternatives: More helpful than generic code generation because it guides developers through naming and documentation decisions with AI-specific context, reducing the likelihood of poorly-named or undocumented skills that confuse LLMs during function selection.
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 build-skill at 29/100. build-skill leads on adoption and ecosystem, while Supabase is stronger on quality.
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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