Query Qdrant and code-index-mcp to retrieve architectural notes and code snippets.
npx skills add https://github.com/ZeiZel/dotfiles --skill rag-context-zeizel
Please help me install this Agent Skill. Skill: rag-context Source: https://github.com/ZeiZel/dotfiles/tree/main/.claude/skills/rag-context Command: npx skills add https://github.com/ZeiZel/dotfiles --skill rag-context-zeizel
RAG-context retrieval enables fast, scalable access to code and architectural knowledge in large projects by combining vector search with code-index summaries, reducing prompt length while preserving key insights.
Run the rag-context skill to automatically configure and perform a context pull for your current project.
High-intent search queries and answers about installing and using this skill.
RAG-based code context retrieval uses vector search and code-index summaries to extract relevant architectural notes and snippets, reducing prompt length while preserving key insights for large codebases.
Semantic search across code and architecture documents is performed using a hybrid approach, querying Qdrant and code-index-mcp to surface relevant patterns, ADRs, and design decisions.
Yes, rag-context interfaces directly with mcp__qdrant and mcp__code-index tools, performing discovery of project path, building indexes, and retrieving file summaries.
The retrieval strategy is auto-selected by evaluating repository size and the health of the Qdrant index, choosing repomix or RAG to optimize context retrieval results.
Yes, rag-context supports agent spawning and context packing by providing pre-loaded context, enabling complex tasks to execute with relevant architectural knowledge.
RAG-based context retrieval is specifically applied to large codebases where token budgets are a concern; smaller repositories may not require this hybrid search and indexing strategy.
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