| 0 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 12 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Exposes a unified JSON Schema interface to 30+ image generation models (Midjourney v7, Flux Kontext, DALL-E 3, Stable Diffusion XL) through the muapi-cli wrapper layer. The system maps high-level generation requests to model-specific API calls via schema_data.json lookup tables, handling authentication, parameter normalization, and async polling for result retrieval without requiring developers to learn individual model APIs.
Unique: Two-layer architecture separating Core Primitives (thin muapi-cli wrappers) from Expert Library (domain-specific skills) enables agents to call either raw generation APIs or high-level creative workflows; schema_data.json acts as a model registry enabling dynamic model selection without code changes
vs alternatives: Supports 30+ models through a single unified interface vs. Replicate/Together AI which require model-specific endpoint URLs; Expert Library skills encode professional knowledge (cinematography, atomic design, branding) that competitors require manual prompt engineering to achieve
The Nano-Banana skill encodes professional design reasoning into optimized prompt templates and multi-step generation workflows. When an agent requests a logo, UI mockup, or portrait pack, the system decomposes the creative intent into structured parameters (brand guidelines, design principles, identity constraints), executes generation with reasoning-aware prompts, and applies post-processing rules specific to the domain (e.g., identity-lock for portrait consistency).
Unique: Expert Library skills encode professional knowledge (atomic design principles, branding psychology, cinematography rules) into reusable prompt templates and multi-step workflows; identity-lock mechanism uses seed-based generation with consistency validation to produce coherent portrait sets
vs alternatives: Encodes domain expertise that competitors require manual prompt engineering to replicate; identity-lock portrait generation is unique vs. standard image generators which produce uncorrelated variations
The platform utilities handle file uploads to muapi.ai cloud storage, managing authentication, chunked uploads for large files, and result file retrieval. The system supports reference image uploads (for style transfer, inpainting), source video uploads (for extension), and audio uploads (for voice cloning). Files are stored with expiration policies and accessed via signed URLs returned in generation results.
Unique: Integrated file upload and cloud storage management through muapi.ai backend; system handles authentication, chunked uploads, and signed URL generation without requiring manual cloud storage configuration
vs alternatives: Unified asset management vs. competitors requiring separate cloud storage setup; automatic file expiration policies reduce storage costs vs. indefinite retention
The system supports batch generation of multiple media assets in parallel through async task submission and result polling. Agents submit a batch of generation requests (e.g., 10 image variations, 5 video clips), receive task IDs immediately, and poll for results asynchronously. The system aggregates results as they complete and returns a batch result object with per-item status and metadata.
Unique: Async batch submission with parallel execution and result aggregation; system manages task ID tracking and result polling across multiple concurrent requests
vs alternatives: Parallel batch execution reduces total time vs. sequential generation; built-in result aggregation vs. competitors requiring manual batch orchestration
The Cinema Director skill translates high-level cinematic direction (shot type, camera movement, mood, pacing) into optimized prompts for video generation models (Seedance 2.0, Kling 3.0). The system maps directorial concepts (e.g., 'Dutch angle establishing shot') to model-specific parameter sets, manages multi-shot composition, and handles async video rendering with progress polling and result validation.
Unique: Encodes cinematography domain knowledge (shot types, camera movements, pacing rules) into structured directorial intent parameters; Cinema Director skill maps high-level directorial concepts to model-specific prompts, enabling agents to specify video generation at the creative level rather than technical parameter level
vs alternatives: Abstracts cinematography expertise that competitors require manual prompt engineering to achieve; supports multi-model video generation (Seedance, Kling) through unified interface vs. single-model competitors
The Seedance 2 skill extends existing video clips by generating additional frames while maintaining temporal coherence and motion continuity. The system accepts a source video, target duration, and motion direction parameters, then uses Seedance 2.0's frame interpolation engine to synthesize intermediate frames that preserve object trajectories and scene consistency. Async polling monitors generation progress and validates output frame count and quality metrics.
Unique: Seedance 2.0 integration provides frame-level interpolation with temporal coherence validation; system monitors motion continuity across interpolated frames and validates output quality before returning results
vs alternatives: Native Seedance 2.0 integration provides superior temporal coherence vs. generic frame interpolation tools; supports motion-aware extension vs. simple frame duplication
Integrates Suno AI and other text-to-audio models through muapi-cli to generate music, voiceovers, and sound effects from text descriptions. The system supports voice cloning (map text to specific speaker identity), style control (genre, mood, instrumentation), and async audio rendering with format conversion. Audio files are polled asynchronously and returned with metadata (duration, sample rate, codec).
Unique: Unified audio generation interface supporting both music composition (Suno) and voiceover synthesis; voice cloning mechanism maps text to speaker identity through reference audio analysis
vs alternatives: Integrates Suno's music composition capabilities vs. competitors focused only on TTS; supports voice cloning for identity-consistent voiceovers
Exposes 19 structured generation and editing tools through the Model Context Protocol (MCP) server interface. Running `muapi mcp serve` starts an MCP server that publishes JSON Schema definitions for each tool, enabling AI agents (Claude Code, Cursor, Gemini) to discover, validate, and call generation functions directly without shell script execution. The system handles schema validation, async polling orchestration, and result streaming back to the agent.
Unique: MCP server implementation exposes 19 tools with full JSON Schema definitions, enabling agents to discover and validate tool parameters automatically; schema_data.json lookup mechanism maps tool calls to underlying muapi-cli commands
vs alternatives: Native MCP integration enables seamless agent tool calling vs. competitors requiring custom SDK integration; JSON Schema validation prevents invalid parameter combinations before API execution
+4 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 Generative-Media-Skills at 37/100.
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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