Snack Prompt

Implements a taxonomy-based prompt discovery system where users browse curated collections organized by use case categories (writing, coding, analysis, etc.). The platform indexes prompts with metadata tags and category assignments, enabling hierarchical navigation without requiring keyword search. Users can filter by category, view prompt previews, and assess community engagement metrics (likes, usage counts) to identify high-performing templates before testing.

Provides a sandboxed prompt execution environment within the Snack Prompt interface that sends user input + selected prompt to the ChatGPT API and displays responses in real-time without requiring users to leave the platform. The system captures the full prompt text, user test input, and API response, allowing side-by-side comparison of prompt effectiveness before integration into external workflows. Testing state is ephemeral (not persisted) and isolated per session.

Enables users to submit custom prompts to the marketplace with metadata (title, description, category, tags) and share them publicly with attribution. The platform stores prompt text, creator information, and engagement metrics (views, likes, usage count) in a database indexed by category and creator. Community members can upvote/like prompts, and the system tracks creator reputation through contribution count and aggregate engagement. No explicit editorial review gate exists — prompts are published immediately upon submission.

Automatically or manually extracts structured metadata from prompt submissions (title, description, category, tags, use case, difficulty level) and indexes them in a searchable database. The system normalizes category assignments to a predefined taxonomy and enables filtering/sorting by metadata fields. Metadata is used to power discovery, search, and recommendation features without requiring full-text analysis of prompt content.

Tracks and displays community engagement signals for each prompt including view count, like/upvote count, and usage frequency. These metrics are aggregated per prompt and displayed prominently in browse results and prompt detail pages to surface high-performing templates. The system records engagement events (views, likes, test executions) in a database and updates metrics in real-time or near-real-time. Metrics are used to inform ranking and recommendation without explicit algorithmic curation.

Provides mechanisms to export or copy prompts from the marketplace into external tools (ChatGPT, text editors, API clients). Users can copy prompt text to clipboard, generate shareable prompt URLs, or potentially integrate via API/webhook for programmatic access. The system maintains prompt versioning through unique IDs and URLs, enabling stable references for external integrations. Export is stateless — no persistent connection or sync between marketplace and external tools.

Provides keyword-based search functionality that matches user queries against prompt titles, descriptions, and tags using basic string matching or full-text search. Search results are ranked by relevance (likely using simple TF-IDF or keyword frequency) and filtered by category if specified. The system does not use semantic search or embeddings — matching is purely lexical. Search is optional and complements category-based browsing.

Manages user registration, login, and profile management to enable prompt submission, engagement tracking (likes, usage history), and creator attribution. The system supports email-based registration or OAuth integration (likely Google, GitHub) for frictionless signup. User accounts store profile information (username, avatar, bio), submission history, and engagement history. Authentication is required for prompt submission but optional for browsing.