| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Installs modular, self-contained skill packages (48 total across 6 domains: Marketing, Product, Engineering, C-Level, Project Management, Regulatory/Quality) into Claude Code, Cursor, VS Code, Copilot, Goose, Amp, Codex, Letta, and OpenCode via standardized marketplace.json configuration and platform-specific plugin.json manifests. Each skill package bundles Python CLI tools, reference frameworks, templates, and documentation following a 4-component structure (SKILL.md, scripts/, references/, assets/), enabling agents to discover and load domain expertise without manual configuration.
Unique: Uses domain-based organization (6 skill domains) with standardized 4-component package structure (SKILL.md + scripts/ + references/ + assets/) and relative path resolution (../../) to enable agent-skill separation, allowing the same skill to be installed across 8+ heterogeneous platforms without platform-specific rewrites. Marketplace.json provides centralized discovery while platform-specific plugin.json manifests handle registration.
vs alternatives: Broader platform coverage (8+ agents) than Copilot Extensions (GitHub-only) or Claude Projects (Claude-only), with domain-organized skills reducing cognitive load vs flat plugin registries like OpenAI's plugin store.
Executes 68+ production-ready Python CLI tools embedded in skill packages that use only Python standard library (no external dependencies like requests, pandas, or numpy) to ensure portability across agent runtimes and reduce installation friction. Tools are invoked by agents as executable scripts (tool1.py, tool2.py) with stdin/stdout interfaces, enabling agents to chain tool outputs without requiring LLM calls between steps. Each tool is documented in scripts/README.md with usage examples and expected input/output formats.
Unique: Enforces standard library-only constraint across all 68+ tools to guarantee zero external dependencies, enabling tools to run in any Python environment (cloud functions, containers, restricted runtimes) without pip install or dependency resolution. CLI-first design with stdin/stdout interfaces allows agents to chain tools deterministically without LLM calls between steps, reducing latency and cost.
vs alternatives: More portable than Copilot Extensions (which require npm/Node.js ecosystem) or OpenAI plugins (which require external API hosting). Faster tool chaining than LLM-based orchestration (e.g., ReAct agents) because tools execute synchronously without LLM inference between steps.
Provides 2 production-ready C-level advisory skills (c-level-advisor/ domain) designed for executive decision-making and strategic planning: CEO advisor skill (business strategy, market analysis, competitive positioning, board reporting) and CTO advisor skill (technology strategy, architecture decisions, engineering team management, technical roadmap). Skills bundle Python CLI tools for business metrics calculation and analysis, reference frameworks for strategic planning methodologies (OKRs, balanced scorecard, technology strategy frameworks), and templates (board decks, strategic plans, technology roadmaps). cs-ceo-advisor and cs-cto-advisor agents are pre-configured to use C-level skills combined with project management and regulatory skills. C-level advisory is an emerging domain (2 skills) with planned expansion.
Unique: Provides 2 emerging C-level advisory skills (CEO advisor, CTO advisor) with Python CLI tools for business metrics and analysis, reference frameworks for strategic planning (OKRs, balanced scorecard, technology strategy), and pre-configured agents (cs-ceo-advisor, cs-cto-advisor) that combine C-level skills with project management and regulatory skills for holistic executive support.
vs alternatives: More structured than generic executive coaching (e.g., ChatGPT prompts) because it includes strategic planning frameworks and business metrics tools. More accessible than expensive consulting firms because agents provide 24/7 strategic advice at agent cost.
Provides 6 project management skills (project-management/ domain) and 12 regulatory/quality management skills (ra-qm-team/ domain) covering project planning, team coordination, regulatory compliance, quality assurance, and risk management. PM skills include: project planning skill (timeline creation, resource allocation, risk planning), agile/scrum skill (sprint planning, backlog management, velocity tracking), stakeholder management skill (communication plans, status reporting), and 3 additional PM skills. Regulatory/Quality skills include: compliance skill (regulatory requirement tracking, audit preparation), quality assurance skill (QA strategy, test planning, defect management), risk management skill (risk identification, mitigation planning), and 9 additional regulatory/quality skills. Each skill bundles Python CLI tools for project metrics and compliance tracking, reference frameworks (PMBOK, ISO standards, regulatory requirements), and templates (project plans, compliance checklists, audit reports).
Unique: Combines 6 project management skills with 12 regulatory/quality management skills (18 total) to provide comprehensive project and compliance oversight. PM skills focus on planning and coordination (PMBOK frameworks), while regulatory/quality skills focus on compliance and standards (ISO, regulatory requirements). Python CLI tools provide metrics calculation and compliance tracking, reference frameworks provide methodologies, and templates provide ready-to-use checklists and plans.
vs alternatives: More comprehensive than project management tools alone (e.g., Jira) because it includes compliance and quality management. More structured than generic compliance consulting (e.g., ChatGPT prompts) because it includes regulatory frameworks and audit templates.
Provides optional slash commands (/.claude/ directory) that enable quick access to skills and agents within Claude Code and compatible platforms. Slash commands are shortcuts that trigger skill execution or agent instantiation without explicit tool calling. For example, /marketing-content might trigger the content creator skill, /code-review might trigger the code review skill, /ceo-advisor might instantiate the CEO advisor agent. Slash commands are platform-specific (Claude Code, Cursor, VS Code) and optional — agents can also access skills via explicit tool calling. Slash commands improve user experience by reducing friction for common operations.
Unique: Provides optional slash commands (/.claude/ directory) that enable quick skill and agent access within Claude Code and compatible platforms, improving UX by reducing friction for common operations. Slash commands are platform-specific shortcuts that trigger skill execution or agent instantiation without explicit tool calling.
vs alternatives: More discoverable than explicit tool calling (e.g., function_call JSON) because slash commands appear in platform autocomplete. More user-friendly than command-line tools because slash commands integrate with IDE UI.
Implements a 5-layer architecture (Distribution, Agent Orchestration, Skill Implementation, Governance, Automation) that decouples agents from skills using relative path resolution (../../) to enable agents to discover and load skills dynamically without hardcoding paths. Agents (cs-content-creator, cs-demand-gen-specialist, cs-product-manager, cs-ceo-advisor, cs-cto-advisor) live in agents/ directory and reference skills via relative paths, allowing the same agent definition to work across different installation contexts (local, cloud, container). Governance layer enforces standards (quality gates, testing, CI/CD) across all skills.
Unique: Uses relative path resolution (../../) to decouple agents from skills, enabling the same agent definition to work across different installation contexts without path hardcoding. 5-layer architecture (Distribution → Agent Orchestration → Skill Implementation → Governance → Automation) provides clear separation of concerns, with governance layer enforcing standards across all 48 skills via quality gates, testing, and CI/CD integration.
vs alternatives: More modular than monolithic agent frameworks (e.g., LangChain agents with hardcoded tools) because skills are independently versioned and deployed. Governance layer provides better quality control than plugin ecosystems without centralized oversight (e.g., OpenAI plugin store).
Defines 5 production agents (cs-content-creator, cs-demand-gen-specialist, cs-product-manager, cs-ceo-advisor, cs-cto-advisor) that bind to domain-specific skill subsets via agent definitions in agents/ directory. Each agent is configured with CLAUDE.md and plugin.json manifests that specify which skills to load (e.g., cs-ceo-advisor loads c-level-advisor skills + project-management skills). Agents are role-based (content creator, demand gen specialist, product manager, CEO, CTO) and can be instantiated independently or composed into multi-agent systems. Agent definitions include prompt templates, tool bindings, and execution constraints.
Unique: Implements role-based agent orchestration where each agent (cs-content-creator, cs-ceo-advisor, cs-cto-advisor) is bound to a curated subset of skills via agent definitions, enabling teams to create specialized agents without exposing irrelevant tools. Agent definitions include CLAUDE.md (prompt templates) and plugin.json (tool bindings), allowing agents to be version-controlled and deployed independently.
vs alternatives: More structured than ad-hoc agent creation (e.g., custom prompts in Claude) because skill bindings are explicit and version-controlled. Cleaner than monolithic agents with all tools available because role-based binding reduces cognitive load and prevents tool conflicts.
Generates comprehensive skill documentation via SKILL.md master documents (500-1500 lines per skill) that bundle domain expertise, executable Python tools, reference frameworks, and templates into self-contained packages. Each SKILL.md includes skill overview, tool documentation (scripts/README.md), reference frameworks (references/ directory with markdown files), and user-facing templates (assets/ directory). Documentation is human-readable (markdown) and machine-parseable (structured sections with consistent formatting), enabling agents to extract tool signatures, usage examples, and domain knowledge. Reference frameworks provide expert knowledge bases (e.g., marketing frameworks, engineering best practices) that agents can cite or extend.
Unique: Bundles domain expertise, executable tools, and reference frameworks into self-contained SKILL.md documents (500-1500 lines) with standardized structure (overview, tools, frameworks, templates), enabling both human understanding and machine parsing. Reference frameworks provide expert knowledge bases (marketing, engineering, compliance) that agents can cite, extending beyond simple tool documentation.
vs alternatives: More comprehensive than tool-only documentation (e.g., OpenAI function schemas) because it includes domain expertise and reference frameworks. More structured than free-form knowledge bases because SKILL.md follows a consistent template, enabling automated parsing and discovery.
+5 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 claude-skills at 36/100.
Need something different?
Search the match graph →© 2026 Unfragile. Stronger through disorder.
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