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Discovery Velocity
Discovery Engine & Open Prompt Directory

Search, filter, and inspect verified AI prompts across system categories, tags, and creator showcases.

What is this?

The Explore Prompts directory is the public search engine of PromptVerse. It indexes all community-contributed public prompts across system categories (Coding, Copywriting, Marketing, Data Analysis, and Creative Writing) with full-text search and tag clouds.

Cards feature 1-click copy buttons, creator links, and engagement counters.

Why does it exist?

Finding high-performing prompts shouldn’t require scouring disorganized Twitter threads or forum posts. A dedicated discovery index allows developers, marketers, and researchers to locate battle-tested prompts tuned for ChatGPT, Claude, and Gemini in seconds.

It connects practitioners with proven prompt patterns.

How is it beneficial?
  • Instant Copy to Clipboard: Copy complete prompt instructions directly into your active LLM session.
  • Deep Taxonomy Filtering: Drill down by category, tag keywords, or sort by Most Copied or Latest.
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Pro Tip: Sort by "Most Copied" to immediately find the community’s highest-rated prompts for any workflow.
Popular Tags: #python #flask #architecture #refactoring #seo #copywriting #sql #analysis #docker #ai #phd #academic-research #thesis-proposal #methodology #dissertation
Data Science & SQL Analytics ⭐ 4.7 (27)
Data Pipeline ETL/ELT & Data Quality Testing Framework (dbt / Great Expectations)

Designs modern dbt data transformation models and automated data quality assertions to eliminate data corruption in production data lakes.

#dbt #data-engineering #elt #data-quality #snowflake
By admin
1134 398
# System Role: Principal Data Platform Architect & Analytics Engineer ## Objective: Architect a robust modular data transformation pipeline using dbt (data build tool) and automated schema testing to ensure data warehouse integrity. ## Pipeline Parameters: - Raw Source Tables: {{raw_sources}} - Destination Dimensional Model: {{target_model}} (Star Schema Fact/Dimension) - Data Warehouse Engine: {{warehouse_engine}} (Snowflake, BigQuery, Databricks) ## Architecture Requirements: 1. **Staging Layer (`stg_`)**: - Cleansing, explicit casting, column renaming, and surrogate key generation. 2. **Intermediate Layer (`int_`)**: - Business logic joins, window deduplication, and ephemeral aggregations. 3. **Marts Layer (`fct_` and `dim_`)**: - Production fact and dimension tables optimized with clustering/partitioning keys. 4. **Data Quality Tests (`schema.yml`)**: - Implement core tests (`unique`, `not_null`, `accepted_values`, `relationships`). - Add singular custom data tests or Great Expectations checks for business logic invariants. ## Deliverables: - Complete SQLX/SQL model files across staging and marts. - `schema.yml` configuration with documentation and comprehensive test blocks. - Incremental materialization configuration strategy.