What is search intent in Bluera Knowledge?

Search intent specifies the goal of your query (e.g., finding an implementation vs. a definition) to help the engine rank the most relevant files higher based on your specific needs.

How can I reduce token usage during large searches?

Use the 'minimal' detail level for initial discovery to get summaries, then use 'get_full_context' only for the few results with the highest relevance scores.

When should I use the 'contextual' detail level?

Choose 'contextual' when you need to see function signatures, types, and imports to understand an interface without needing the full internal implementation logic.

Can I search multiple repositories simultaneously?

Yes, by using the 'stores' parameter, you can target specific indexed libraries or search across all available stores for a broader comparative perspective.

What is a good relevance score for a search result?

Scores between 0.9 and 1.0 are excellent matches, while 0.7 to 0.9 are very likely relevant. Results below 0.5 are typically weak matches and can often be ignored.

Bluera Search Optimization

Name: Bluera Search Optimization
Author: blueraai

byblueraai

Aprendizaje y Documentación

Optimizes Bluera Knowledge search queries by implementing intent-based ranking and token-efficient retrieval strategies.

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The Search Optimization skill provides a comprehensive framework for maximizing the effectiveness of the Bluera Knowledge search tool within Claude Code. It guides users through advanced parameter management, including the selection of specific search intents like implementation discovery, pattern finding, and API definitions. By employing a progressive context strategy—starting with minimal summaries and escalating to full code only when necessary—this skill helps developers navigate vast codebases and documentation sets while reducing token consumption by up to 70%. It is an essential utility for developers who need high-precision results from their local knowledge stores without overwhelming the LLM's context window.

Características Principales

Intent-based ranking for implementation, usage, and documentation queries
Balanced discovery strategies for both wide-net and high-precision searches
Progressive detail levels to minimize token overhead and context usage
Multi-store filtering for targeted search across specific repositories
Relevance score evaluation for data-driven result selection
0 GitHub stars

Casos de Uso

Exploring large unfamiliar codebases without exceeding context limits
Performing comparative analysis of implementations across different frameworks
Extracting specific API signatures and type definitions quickly

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Características Principales

Intent-based ranking for implementation, usage, and documentation queries
Balanced discovery strategies for both wide-net and high-precision searches
Progressive detail levels to minimize token overhead and context usage
Multi-store filtering for targeted search across specific repositories
Relevance score evaluation for data-driven result selection
0 GitHub stars

Casos de Uso

Exploring large unfamiliar codebases without exceeding context limits
Performing comparative analysis of implementations across different frameworks
Extracting specific API signatures and type definitions quickly