| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 14 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Enables creation of domain-specific agents through a markdown-based agent definition format (.agent.md) that integrates with GitHub Copilot via MCP (Model Context Protocol) servers. Agents are installed and activated through a registry system that maps agent metadata (name, description, capabilities) to executable MCP server bindings, allowing Copilot to invoke specialized behavior for specific technologies (e.g., Terraform, ARM migration). The architecture supports both built-in agents and external plugin-based agents through a plugin manifest system.
Unique: Uses a declarative markdown-based agent definition format (.agent.md with YAML frontmatter) combined with MCP server bindings, enabling non-engineers to define agents without writing code. The plugin manifest system (plugin.json) allows external agents to be discovered and installed via a centralized marketplace, creating a composable agent ecosystem rather than monolithic Copilot customization.
vs alternatives: Simpler than building custom Copilot extensions from scratch because it abstracts MCP server complexity into declarative metadata; more discoverable than ad-hoc prompt engineering because agents are catalogued in a searchable marketplace.
Provides a modular skill system where discrete capabilities (e.g., 'sponsor finder', 'fabric lakehouse integration') are packaged as reusable units with SKILL.md format, including embedded prompts, examples, and asset bundles (code snippets, configuration templates). Skills are discoverable through a skills registry and can be composed into agents or used standalone within Copilot. The SKILL.md format enforces structured metadata (name, description, use cases, examples) and supports asset bundling for context-aware code generation.
Unique: Implements a structured SKILL.md format with embedded asset bundling (code snippets, templates, configuration) rather than just prompt text, enabling context-aware code generation. Skills are composable into agents and discoverable through a metadata-driven registry, creating a modular capability marketplace instead of monolithic prompt libraries.
vs alternatives: More modular than monolithic agent prompts because skills are independently versioned and composed; more discoverable than scattered code snippets because skills include structured metadata (use cases, examples, prerequisites) indexed in a searchable marketplace.
Provides automated documentation generation from content metadata and a learning hub with cookbook examples demonstrating how to use agents, skills, and workflows. The documentation pipeline generates API documentation, usage guides, and examples from content files, while the learning hub curates best practices and real-world examples. The system supports multiple documentation formats (Markdown, HTML) and integrates with a website (Astro-based) for publishing.
Unique: Implements automated documentation generation from content metadata combined with a curated learning hub of cookbook examples, enabling scalable documentation that stays in sync with content changes. The Astro-based website provides a modern, searchable documentation platform.
vs alternatives: More maintainable than manually written documentation because generation is automated; more discoverable than scattered examples because cookbook examples are curated and indexed in a learning hub.
Provides automated contributor recognition and attribution by extracting Git history, tracking contributions across content types, and generating contributor reports. The system maintains a contributor database (.all-contributorsrc) with attribution metadata and generates contributor recognition in documentation and marketplace. Metrics track contribution volume, content quality, and community impact.
Unique: Implements automated contributor recognition by extracting Git history and maintaining a contributor database (.all-contributorsrc), enabling scalable community recognition without manual curation. Metrics track contribution volume and community impact.
vs alternatives: More scalable than manual recognition because attribution is automated; more transparent than ad-hoc recognition because metrics are tracked and reported.
Provides a modern, searchable website (Astro-based) for discovering and exploring agents, skills, instructions, workflows, and plugins. The website includes full-text search powered by Pagefind, filtering by category/language/technology, and a responsive UI for browsing content. The platform integrates with the marketplace discovery system and learning hub to provide a unified discovery experience.
Unique: Implements a modern Astro-based website with Pagefind full-text search and metadata-driven filtering, providing a unified discovery platform for agents, skills, instructions, and workflows. The website integrates with the marketplace discovery system and learning hub.
vs alternatives: More user-friendly than GitHub repository browsing because the website provides search, filtering, and curated examples; more discoverable than scattered documentation because all content is indexed and searchable.
Provides a structured contribution workflow for submitting new agents, skills, instructions, and workflows through pull requests with automated quality checks, community review, and merge automation. The workflow includes contribution guidelines, templates for each content type, automated validation, and a review process that ensures quality before merging. Merge automation handles contributor recognition, documentation updates, and marketplace indexing.
Unique: Implements a structured contribution workflow with pull request templates, automated validation, and merge automation that handles contributor recognition and marketplace indexing. The workflow ensures quality while reducing manual review burden.
vs alternatives: More scalable than manual review because validation is automated; more consistent than ad-hoc contributions because templates and guidelines enforce standards.
Allows injection of custom instructions into Copilot's behavior through .instructions.md files with YAML frontmatter, supporting language-specific instructions (Python, JavaScript, Go, etc.) and context management strategies. Instructions are applied globally or scoped to specific file types/projects, enabling teams to enforce coding standards, architectural patterns (OOP design patterns), and domain-specific conventions without modifying Copilot's core behavior. The instruction system integrates with Copilot's prompt context management to prioritize instructions based on file type and project configuration.
Unique: Implements language-specific instruction scoping with context management that prioritizes instructions based on file type and project configuration, rather than applying all instructions uniformly. Instructions are stored as markdown with YAML frontmatter, making them human-readable and version-controllable in Git, enabling teams to evolve standards over time.
vs alternatives: More flexible than hardcoded linting rules because instructions can express architectural intent and design patterns; more discoverable than scattered documentation because instructions are indexed and searchable in the marketplace.
Provides a structured prompt file system (.prompt.md format) with quality standards and task-specific templates that enable composition of reusable prompt fragments for common Copilot tasks (code review, refactoring, documentation generation). Prompts are indexed by task type and can be combined to create complex multi-step workflows. The system enforces prompt quality standards (clarity, specificity, examples) and includes a validation pipeline to ensure prompts meet organizational guidelines before distribution.
Unique: Implements a structured prompt file system with enforced quality standards (clarity, specificity, example coverage) and task-specific templates that can be composed into complex workflows. Prompts are version-controlled in Git and indexed with metadata, enabling teams to evolve and share prompt libraries rather than treating prompts as ephemeral.
vs alternatives: More systematic than ad-hoc prompt engineering because prompts are validated against quality standards; more reusable than one-off prompts because task-specific templates can be composed and shared across projects.
+6 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 awesome-copilot 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