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Explore Prompts

Browse and search thousands of community-verified AI prompts

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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.
  • Sub-Millisecond Speed: Backed by indexed query optimization and optional Redis caching.
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 ⭐ 4.9 (49)
Exploratory Data Analysis (EDA) & Anomaly Detection in Python

Generates complete Python pandas/polars EDA scripts with statistical distribution checks, outlier detection, and correlation matrices.

#python #statistics #eda #pandas #anomaly-detection
By admin
1410 458
# System Role: Senior Data Scientist & Statistical Analyst ## Objective: Write a comprehensive Python Exploratory Data Analysis (EDA) script using Pandas, NumPy, and Seaborn to uncover hidden patterns, skewness, and data anomalies in a dataset. ## Dataset Profile: - Business Domain: {{business_domain}} - Key Columns & Types: {{columns_summary}} - Target Variable (if applicable): {{target_variable}} - Known Data Quality Suspicions: {{suspected_issues}} (e.g. missing values, high cardinality, extreme outliers) ## Script Requirements: 1. **Data Health & Completeness Audit**: - Calculate missingness rates, duplicate rows, and data type mismatches. 2. **Univariate & Bivariate Distribution**: - Generate summary statistics (mean, median, IQR, kurtosis, skewness). - Flag columns with non-normal distributions requiring log/box-cox transformation. 3. **Outlier & Anomaly Detection**: - Implement both IQR-based fences and Z-score / Isolation Forest anomaly scoring. 4. **Correlation & Feature Interaction**: - Compute Spearman and Pearson correlation matrices, highlighting multicollinearity risks (VIF > 5). 5. **Visualization Blueprint**: - Clean Matplotlib/Seaborn code generating boxplots, histograms, and heatmaps. ## Output Format: Clean, PEP-8 compliant Python code with clear markdown commentary explaining statistical interpretations.