Skill Directory

Generative Skills

Skill	Description	Mini Chat	Full Chat
AI Search - Column Search	Searches a specific column based on the user’s input to find relevant data. Example: "Find me confused inputs."	✅	❌
AI Search - Table Search	Searches across the entire table to identify patterns, anomalies, or outliers. Example: "Find inputs that reference pricing that are hallucinated."	✅	❌
AI Search - Text to Filter	Generates query filters based on natural language commands. Example: "Filter by input contains SDK."	✅	❌
AI Search - LLM Analysis Lite	Provides suggestions for search results and finds patterns in the data. Example: "What are the top 5 types of questions asked?"	✅	❌
Create Custom Evaluations	Writes a tailored eval for your application based on specified goals or data analysis.	✅	✅
Diagnose RAG Issues	Analyzes responses in the retrieval process, ensuring relevance and accuracy, offering improvements.	✅	✅
Optimize Prompts	Optimizes prompts to enhance response quality or address specific issues.	❌	✅
Summarize Evaluation Metrics	Assesses and summarizes evaluation metrics, providing suggestions for enhancing performance.	❌	✅

Tabular Skills

Skill	Description	Full Chat	Mini Chat
Get Model Insights	Provides a high-level analysis of model performance, including trends over time, prediction volumes, and drift. Best for general inquiries, not suited for detailed debugging.	✅	❌
Cohort Performance Analysis	Analyzes model performance across different cohorts or slices of data, identifying poorly performing segments. Provides insights into behavior over a specific period.	✅	❌
Detect Data Drift	Pinpoints sudden input quality issues by examining features and tags for drift. Compares current distributions to a baseline to detect significant shifts.	✅	❌
Check for Missing Data	Analyzes input data to report the percentage of missing data in features and tags, highlighting any sudden spikes or changes that could impact model inputs.	✅	❌
Assess Feature Data Quality	Assists in debugging issues by analyzing dataset metrics and focusing on specific dimensions. Identifies critical changes and provides actionable suggestions.	✅	❌
Evaluate Distribution Shifts	Analyzes a dimension’s distribution to understand shifts in percentage over time.	✅	❌
Review Cardinality Trends	Analyzes changes in the cardinality of features and tags over time, highlighting unusual variations that may indicate data quality issues.	✅	❌