prompts.chat

Enables users to search and retrieve pre-written prompt templates from a curated CSV-based repository organized by use case, domain, and complexity level. The system indexes prompt metadata (title, description, category, tags) to support semantic and keyword-based discovery, returning structured prompt objects with full text, parameters, and usage examples for immediate application in LLM workflows.

Allows users to export discovered prompts in multiple formats (raw text, JSON, CSV) and integrate them directly into LLM applications via copy-paste, API calls, or file-based imports. The system maintains prompt metadata and structure during export to preserve parameters, examples, and usage notes for seamless integration into downstream workflows.

Organizes prompts into hierarchical categories (e.g., coding, writing, analysis, creative) and applies semantic tags to enable multi-dimensional discovery and filtering. The taxonomy is pre-defined and curated, allowing users to browse by domain, use case, complexity level, and other metadata attributes without full-text search.

Maintains and displays rich metadata for each prompt including author, creation date, use case description, parameter placeholders, example inputs/outputs, and compatibility notes. This metadata is preserved during export and retrieval, enabling users to understand prompt intent, constraints, and expected behavior without additional documentation.

Exposes the entire prompt library as a downloadable, machine-readable CSV file (prompts.csv) with structured columns for prompt text, metadata, categories, and tags. This enables programmatic access, bulk operations, and integration with external tools like spreadsheets, databases, and custom indexing systems without requiring API authentication or rate limiting.