| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 15 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Maintains a registry of 28 specialized agents organized into tiers (architecture, implementation, review, testing) that automatically route tasks based on delegation categories and agent specialization profiles. Uses a hook-driven execution model where pre-processing hooks analyze incoming requests, match them against agent capabilities via metadata, and delegate to the most appropriate tier. Agents can be customized with domain-specific prompts and skill bindings without modifying core orchestration logic.
Unique: Implements a tiered agent system with explicit specialization profiles and hook-driven delegation matching, allowing agents to be customized independently while maintaining centralized routing logic through pre-processing hooks that analyze task characteristics against agent metadata
vs alternatives: More structured than generic function-calling approaches because it uses explicit agent tiers and specialization categories, enabling better task-to-agent matching than systems that treat all agents as interchangeable
Implements project-level session isolation using an inbox/outbox pattern where each session maintains separate state files containing mode state, agent decisions, and execution history. State is persisted to disk in JSON schemas specific to each execution mode (Ralph Loop, Autopilot, Ultrawork, Team Orchestration), enabling recovery from interruptions and resumption of multi-step workflows. Session isolation prevents cross-project contamination and allows parallel execution of independent sessions with their own model routing and hook configurations.
Unique: Uses mode-specific state schemas and an inbox/outbox pattern for isolation, allowing each execution mode to define its own state structure while maintaining a unified recovery mechanism that can replay decisions and continue from checkpoints
vs alternatives: More robust than stateless orchestration because it persists intermediate decisions and enables recovery, and more flexible than global state because session isolation prevents cross-project contamination and allows parallel execution
Generates structured artifacts (code files, reports, documentation) from agent outputs using post-processing hooks that parse agent responses and format them according to artifact templates. Artifacts are stored in the project directory with metadata (agent, timestamp, mode) for tracking. Artifact generation supports multiple formats (code, markdown, JSON) and can apply transformations (linting, formatting) before writing. Artifacts are indexed in session state, enabling retrieval and versioning.
Unique: Implements post-processing hooks that parse agent outputs and generate formatted artifacts with metadata tracking, enabling structured output generation and artifact versioning without manual file management
vs alternatives: More structured than raw text output because artifacts include metadata and formatting, and more flexible than hardcoded templates because artifact generation is hook-based and supports custom transformations
Manages configuration through settings.json (hook registry, model routing, skill definitions) and CLAUDE.md (project-specific context and constraints). Configuration changes are merged intelligently when updating oh-my-claudecode, preserving user customizations while incorporating new defaults. Settings are validated against a schema before application, preventing invalid configurations. Configuration is scoped per project, enabling different teams to use different settings. Configuration changes trigger hook reloads without requiring plugin restart.
Unique: Implements intelligent configuration merging that preserves user customizations while incorporating new defaults, with schema-based validation and per-project scoping, enabling safe updates without losing configuration
vs alternatives: More robust than manual configuration because it validates settings before application, and more flexible than global configuration because it supports per-project customization
Provides automated installation via setup wizard and auto-update mechanism that checks for new versions and applies updates with rollback capability. Installation guards prevent incompatible versions from being installed. Plugin cache is managed to prevent stale code from being loaded. Version reconciliation ensures that installed components match the expected versions. Update process preserves user configurations and custom hooks through the merge strategy. Installation diagnostics help troubleshoot setup issues.
Unique: Implements automated installation with setup wizard and auto-update that preserves user configurations through intelligent merge strategy, with version guards and rollback capability for safe updates
vs alternatives: More user-friendly than manual installation because setup wizard automates configuration, and more reliable than simple version replacement because it includes rollback and configuration preservation
Provides a CLI interface with commands for launching execution modes, querying analytics, managing configurations, and running diagnostics. CLI commands can be invoked from external scripts or CI/CD pipelines, enabling integration with existing workflows. Launch system supports parameterized execution (mode, agents, skills, hooks) via command-line arguments. CLI output is structured (JSON, CSV) for easy parsing by external tools. Commands are authenticated and authorized based on project permissions.
Unique: Implements a structured CLI with parameterized execution and JSON/CSV output, enabling integration with CI/CD pipelines and external tools while maintaining project-based authentication
vs alternatives: More scriptable than UI-only interfaces because CLI commands can be invoked from scripts, and more flexible than fixed integrations because CLI supports parameterized execution
Provides a notification system that alerts users to execution events (task completion, failures, escalations) via configurable delivery channels (in-app, email, Slack, webhooks). Notifications are triggered by post-processing hooks and can be customized per project. Notification templates support variable substitution (agent name, task status, error details). Notification history is tracked in session state for audit purposes. Notification delivery is asynchronous and includes retry logic for failed deliveries.
Unique: Implements asynchronous notifications with configurable delivery channels and retry logic, triggered by post-processing hooks and supporting variable substitution in templates
vs alternatives: More flexible than hardcoded notifications because delivery channels are configurable, and more reliable than synchronous notifications because delivery is asynchronous with retry logic
Implements a multi-stage hook system with pre-processing hooks (analyze requests, validate context), orchestration hooks (route to agents, manage delegation), persistent mode hooks (maintain state across steps), quality control hooks (validate outputs), and post-processing hooks (recovery, artifact generation). Hooks are executed in a defined sequence and can modify request/response data, trigger side effects, or abort execution. Hook configuration is stored in settings.json and can be customized per project, enabling teams to inject custom logic (logging, validation, integration) without modifying core orchestration code.
Unique: Provides a multi-stage hook system with explicit stages (pre-processing, orchestration, persistent mode, quality control, post-processing) that execute in sequence, allowing teams to inject custom logic at specific points while maintaining a clear execution model
vs alternatives: More structured than generic middleware because hooks are stage-specific and execute in a defined order, and more flexible than hardcoded validation because hooks can be configured per-project without code changes
+7 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.
oh-my-claudecode scores higher at 44/100 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
+1 more capabilities