| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 5 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
This capability allows AI agents to discover and evaluate skills through an API that supports trust-level filtering, categorizing skills as verified, community, or sandbox. It employs a standardized query mechanism based on the Universal Skill Kit (USK), ensuring that agents can seamlessly integrate skills across multiple platforms like Claude Code and Codex CLI. The architecture is designed to facilitate rapid skill evaluation without requiring authentication for read operations, which enhances accessibility and speeds up the integration process.
Unique: Utilizes the USK standard for skill categorization, allowing agents to filter skills by trust level without authentication barriers.
vs alternatives: More flexible than traditional marketplaces by allowing anonymous access to skill data while maintaining trust levels.
This capability enables AI agents to install skills across seven different platforms through a unified API interface. It leverages a modular architecture that abstracts the installation process, allowing agents to seamlessly integrate skills from platforms like OpenClaw and Gemini CLI without needing platform-specific adjustments. The use of a common installation protocol ensures that agents can easily adapt to new skills as they become available.
Unique: Features a unified installation process that abstracts platform-specific requirements, simplifying integration for developers.
vs alternatives: More efficient than platform-specific skill stores, reducing the overhead of managing multiple installation processes.
This capability allows agents to retrieve performance metrics and evaluations for skills available in the marketplace. It uses a standardized API endpoint that aggregates user feedback and performance data, providing insights into skill effectiveness and reliability. This data is crucial for agents to make informed decisions about which skills to integrate based on real-world usage and community feedback.
Unique: Aggregates and standardizes performance metrics from multiple sources, providing a comprehensive evaluation framework for skills.
vs alternatives: Offers a more holistic view of skill performance compared to isolated evaluations from individual platforms.
This capability provides a single point of access to a diverse range of skills from multiple AI platforms, utilizing the USK standard for interoperability. It employs a microservices architecture that allows for seamless integration and management of skills across platforms like Cursor and Codex CLI. This design choice enables developers to leverage a wide array of skills without needing to navigate multiple marketplaces or APIs.
Unique: Utilizes a microservices architecture to provide a seamless experience for accessing skills from various platforms through a single API.
vs alternatives: More efficient than accessing multiple individual marketplaces, reducing complexity for developers.
This capability allows users to contribute skills to the marketplace, fostering a community-driven ecosystem. It employs a structured submission process that includes validation and categorization based on the USK standard, ensuring that community contributions meet quality and trust criteria before being made available to agents. This approach encourages collaboration and innovation within the AI development community.
Unique: Incorporates a structured validation process for community contributions, ensuring quality and adherence to the USK standard.
vs alternatives: Encourages community engagement while maintaining high standards for skill quality, unlike many open marketplaces.
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.
AI Skill Store scores higher at 50/100 vs Supabase at 42/100.
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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
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