local-file-rag-basic
VerifiedHigh-performance local File RAG suite (Basic Edition).
$ Add to .claude/skills/ About This Skill
- # Skill: Local File RAG Search (Basic Edition)
- ## Description
- This is the Basic Edition of the high-performance local RAG suite, providing efficient code and document retrieval within constraints.
- Constraints:
- - Only indexes files under 20MB.
- - Uses single-threaded (sequential) indexing for lower resource usage.
- Support: JS/TS, Python, C++, Go, Markdown, PDF, DOCX, XLSX, etc.
- ## Tools
- ### local_file_rag_search
- Efficiently searches the local workspace.
- Parameters:
- `query` (string, required): Search terms or function names.
- `targetFile` (string, optional): Specific file path to restrict the search.
- `rootDir` (string, optional): Root directory to scan.
- Output Protocol:
- Returns a structured result with Skeletons, Metadata, and Clustered Code Snippets.
Use Cases
- Build high-performance local file RAG systems for document question-answering
- Index and search local documents using retrieval-augmented generation
- Query knowledge bases built from local files without cloud dependencies
- Create searchable document collections from PDFs, text, and markdown files
- Enable AI agents to answer questions grounded in local document contents
Pros & Cons
Pros
- +Compatible with multiple platforms including claude-code, openclaw
- +Well-documented with detailed usage instructions and examples
- +Strong community adoption with a large number of downloads
Cons
- -No built-in analytics or usage metrics dashboard
- -Configuration may require familiarity with ai & machine learning concepts
FAQ
What does local-file-rag-basic do?
High-performance local File RAG suite (Basic Edition).
What platforms support local-file-rag-basic?
local-file-rag-basic is available on Claude Code, OpenClaw.
What are the use cases for local-file-rag-basic?
Build high-performance local file RAG systems for document question-answering. Index and search local documents using retrieval-augmented generation. Query knowledge bases built from local files without cloud dependencies.
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