| 0 |
| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Automatically generates and maintains table of contents (TOC) files across the repository using a GitHub Actions workflow that triggers on main branch pushes and PR merges. The system uses Python scripts (idxtool.py, gptparser.py) to enumerate prompt files, parse their metadata, and rebuild TOC.md files in the root and all subdirectories under /prompts/, ensuring navigation links remain current as new prompts are added or modified without manual intervention.
Unique: Uses a dual-script approach (idxtool.py for orchestration, gptparser.py for metadata extraction) with GitHub Actions automation to maintain consistency across 1,100+ prompts organized in three separate collections (gpts, official-product, opensource-prj), each with its own TOC hierarchy. The rebuild_toc() and generate_toc_for_prompts_dirs() functions ensure both root-level and subdirectory TOCs stay synchronized.
vs alternatives: More automated than manual TOC maintenance and more scalable than static documentation, but less sophisticated than full-text search indices or semantic navigation systems that some larger documentation projects use.
Parses markdown prompt files using gptparser.py to extract and standardize metadata fields (name, description, author, tags, etc.) from YAML frontmatter and markdown headers. The parser maintains a dictionary of supported fields with display names and processing order, enabling consistent formatting across heterogeneous prompt sources (official OpenAI/Anthropic products, community GPTs, open-source projects) and enabling downstream indexing and search capabilities.
Unique: Implements a field-mapping dictionary that defines both display names and processing order for metadata fields, allowing flexible extraction from heterogeneous prompt sources (ChatGPT system prompts, Claude Code system, Grok jailbreak prompts, custom GPTs) without requiring source-specific parsers. The gptparser.py module handles both YAML frontmatter and markdown-embedded metadata.
vs alternatives: More flexible than regex-based extraction because it uses structured YAML parsing, but less robust than full AST-based markdown parsing (e.g., tree-sitter) which would handle edge cases like nested code blocks or escaped characters.
Documents patterns and system prompts for custom GPTs and development IDE assistants (including Grimoire Coding Assistant and other specialized tools) organized in /prompts/gpts/. The collection includes 1,100+ examples of how developers structure prompts for specific domains (coding, finance, education, etc.), providing a comprehensive reference for understanding custom GPT design patterns and specialized assistant architectures.
Unique: Aggregates 1,100+ custom GPT prompts organized by domain (coding, finance, education, etc.) with specific examples like Grimoire Coding Assistant, providing a comprehensive reference for understanding how developers structure prompts for specialized tasks. The scale (1,100+ examples) enables pattern analysis across diverse use cases.
vs alternatives: More comprehensive than individual GPT examples because it provides 1,100+ patterns in one place, but less curated than specialized prompt engineering courses or frameworks that provide guided learning paths.
Aggregates and organizes system prompts from three distinct sources (official-product: ChatGPT/Claude/Grok, gpts: 1,100+ community-created custom GPTs, opensource-prj: open-source AI projects) into a unified repository structure with separate TOC hierarchies. The architecture uses directory-based organization (/prompts/gpts/, /prompts/official-product/, /prompts/opensource-prj/) to maintain source separation while enabling cross-source discovery and comparison through unified indexing.
Unique: Maintains three parallel prompt collections (official-product with 141+ entries, gpts with 1,100+ entries, opensource-prj with 20+ entries) in separate directory hierarchies, each with its own TOC, enabling both source-specific browsing and cross-source comparison. The architecture preserves source identity while enabling unified discovery through the root-level TOC.md.
vs alternatives: More comprehensive than vendor-specific prompt collections (e.g., OpenAI's official docs alone) because it includes community contributions and competing vendors, but less curated than specialized prompt marketplaces that apply quality filters or user ratings.
Documents and catalogs prompt injection techniques, jailbreak methods, and prompt leaking knowledge as a research and educational resource. The repository includes specific files like GrokJailbreakPrompt.md and security-focused documentation (SECURITY.md) that explain how system prompts can be extracted, bypassed, or manipulated, serving as both a learning resource and a reference for understanding AI safety vulnerabilities.
Unique: Explicitly documents prompt injection and jailbreak techniques (e.g., GrokJailbreakPrompt.md) as part of the repository's educational mission, treating security vulnerabilities as learning opportunities rather than hiding them. The SECURITY.md file provides contribution guidelines for responsibly documenting vulnerabilities.
vs alternatives: More transparent and educational than vendor security advisories that often withhold technical details, but less systematic than academic security research papers that provide formal vulnerability taxonomies and impact assessments.
Enables discovery and browsing of 1,100+ community-created custom GPTs through hierarchical organization by category (coding, finance, education, etc.) with automated TOC generation and file enumeration. The enum_gpts() and find_gptfile() functions in idxtool.py support both directory-based browsing and ID/URL-based lookup, allowing users to search for GPTs by name, category, or functionality without requiring a database backend.
Unique: Implements enum_gpts() and find_gptfile() functions that enable both directory-based enumeration and ID/URL-based lookup of 1,100+ custom GPTs without requiring a database or search index. The file naming convention (e.g., tveXvXU5g_QuantFinance.md) embeds the GPT ID, enabling reverse lookup from URL to local file.
vs alternatives: More accessible than the official OpenAI GPT Store because it provides source-level access to system prompts and configuration, but less discoverable than the GPT Store's UI-based search and recommendation system.
Enables side-by-side comparison of system prompts from different AI vendors (OpenAI ChatGPT, Anthropic Claude, xAI Grok, Google AI tools) by organizing official product prompts in /prompts/official-product/ with vendor-specific subdirectories. Users can examine how different vendors structure instructions, handle edge cases, and implement safety guidelines by reading and comparing prompts like ChatGPT system.md, Claude Code System, and Grok2.md/Grok3.md files.
Unique: Maintains official product prompts from multiple competing vendors (OpenAI, Anthropic, xAI, Google) in a single repository, enabling direct comparison of instruction-following approaches. The /prompts/official-product/ directory includes vendor-specific subdirectories (chatwise, manus, xai) with multiple versions (e.g., Grok2.md, Grok3.md, Grok3WithDeepSearch.md) showing how vendors iterate on their system prompts.
vs alternatives: More comprehensive than individual vendor documentation because it aggregates multiple vendors in one place, but less authoritative than official vendor documentation and may lag behind actual deployed prompts.
Provides structured contribution guidelines (CONTRIBUTING.md) and security policies (SECURITY.md) that define how community members can submit new prompts, validate metadata, and ensure quality standards. The workflow integrates with GitHub's pull request system and automated TOC generation, enabling contributors to add new prompts without manually updating indices while maintaining repository integrity through validation checks.
Unique: Integrates contribution guidelines with automated TOC generation, allowing contributors to submit new prompts via pull requests without manually updating indices. The SECURITY.md file provides specific guidance for responsibly disclosing prompt injection and jailbreak techniques, treating security vulnerabilities as educational opportunities rather than suppressing them.
vs alternatives: More community-friendly than closed prompt collections because it enables open contributions, but less structured than platforms with automated quality checks, duplicate detection, or contributor reputation systems.
+3 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 chatgpt_system_prompt at 31/100.
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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