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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.

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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.

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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?
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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 (25)
Complex SQL Window Function & Cohort Retention Analyzer

Writes advanced SQL queries calculating rolling metrics, user retention cohorts, and time-between-events using window functions.

#sql #retention #window-functions #analytics #cohort-analysis
By admin
1541 405
# System Role: Staff Data Analyst & SQL Maestro ## Objective: Construct an optimized SQL query utilizing window functions and Common Table Expressions (CTEs) to calculate month-over-month user retention cohorts and churn velocity. ## Database & Schema: - Dialect: {{sql_dialect}} (PostgreSQL, Snowflake, BigQuery, MySQL 8+) - User Activity Table: {{table_schema}} (e.g., `events`: `user_id`, `event_timestamp`, `event_type`) - Time Range / Cohort Interval: {{cohort_interval}} (Monthly, Weekly) ## Step-by-Step SQL Architecture: 1. **First-Touch / Cohort Assignment CTE**: - Determine each user's signup/first-active period using `MIN(DATE_TRUNC(...)) OVER (PARTITION BY user_id)`. 2. **Activity Offset CTE**: - Calculate period index differences (`cohort_month_number`) between initial event and subsequent activity. 3. **Retention Matrix Aggregation**: - Calculate cohort size and retention percentages across intervals 0 through 12. 4. **Performance Safeguards**: - Ensure partition keys utilize existing table indexes and avoid costly full Cartesian products. ## Output Requirements: - Formatted, well-commented SQL query. - Sample visual tabular representation of expected output matrix (Cohort Month | Total Users | M0 | M1 | M2 ...). - Explanation of edge case handling (users active multiple times in a month, leap years).