Intelligence Pipeline

Integrate multiple ranking systems with reinforcement learning for personalized search results.

Updated Apr 26, 2026

Search Tags:#search ranking #context awareness #retrieval augmented generation #search relevance #AI-driven search

Ready to run

npx skills add https://github.com/Renzo-Tognella/UniversalThingsForMyAgents --skill intelligence-pipeline

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Intelligence Pipeline
Source: https://github.com/Renzo-Tognella/UniversalThingsForMyAgents/tree/main/skills/39_intelligence_pipeline
Command: npx skills add https://github.com/Renzo-Tognella/UniversalThingsForMyAgents --skill intelligence-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires spreading_activation, hyde, composite_scorer, context_compaction, ebbinghaus_reinforcement, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill improves the search experience by enhancing the relevance, recency, and importance of search results, using advanced ranking techniques and context awareness.

Core Features & Use Cases

Relevance Fusion: Combines scores from multiple sources to achieve a balanced and accurate ranking.
Diversification: Ensures diverse results based on prefix matching and similarity thresholds.
Composite Scoring: Calculates a weighted score considering relevance, recency, and importance for each result.
Context Compaction: Returns compacted context results with summarized information for efficient browsing.
Ebbinghaus Reinforcement: Reinforces context with positive feedback to improve future searches.
Use Case: For applications requiring highly relevant and context-aware search results, such as enterprise knowledge bases or personal information management systems.

Quick Start

Activate the Intelligence Pipeline Skill and input your search query 'product release timeline'.

Frequently Asked Questions about Intelligence Pipeline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How does AI-driven search ranking improve retrieval augmented generation relevance?▼

AI-driven search ranking improves retrieval augmented generation by integrating multiple ranking systems and context-aware analysis to calculate weighted scores for relevance, recency, and importance. This ensures balanced and accurate search results for dynamic queries.

What is the best way to combine multiple search ranking scores for enterprise knowledge bases?▼

The best way to combine multiple search ranking scores is relevance fusion, which merges scores from multiple sources. This achieves a balanced ranking while ensuring diversification based on prefix matching and similarity thresholds for enterprise knowledge bases.

How do I reduce context size for AI-driven search results without losing key information?▼

You can reduce context size for AI-driven search results using context compaction, which returns compacted context results with summarized information. This enables efficient browsing while retaining the core information needed for processing.

Can I use reinforcement learning to improve search relevance over time?▼

Yes, you can improve search relevance over time using Ebbinghaus reinforcement, which reinforces search context with positive feedback. This technique adapts the ranking systems to provide personalized information retrieval for future queries.

Does context-aware search analysis require advanced AI models?▼

Yes, context-aware search analysis requires advanced AI models and context analysis capabilities. These models are necessary to process diverse and dynamic search queries and execute the underlying composite scoring and spreading activation techniques.