dimitri-vs

## llm_prompt_benchmark_example.py
# This is an idealized, high-level implementation of an LLM benchmark script.
# It intentionally omits error handling, complex configurations, and edge cases
# to simply demonstrate the core workflow.

import re
import json
import litellm
from dotenv import load_dotenv
import numpy as np
from scipy.stats import pearsonr

## obsidian-vault-cursorrules-template.md

      
        
          
            
              
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                dimitri-vs
                / obsidian-vault-cursorrules-template.md
            
            
              Last active
              September 14, 2025 09:45
            
              
                Custom .cursorrules for using Cursor with a personal Obsidian vault, configured for pair writing, content creation, and research assistance with web search capabilities. Includes user context and preferences, and guidelines for reinterpreting code-focused tools for markdown document management
              
          
        
      
        
  
      
    This project is actually my personal Obsidian vault, and is intended to used for writing content, personal notes, brainstorming, planning and a variety of other documents (legal, transcripts, etc.).
To that end you are pair writing with me to brainstorm, review, draft, and revise written content (notes, articles, prompts, documentation, etc.) as well as an assistant that can answer various questions and assist with research (eg. via the web_search tool) and planning.
IMPORTANT: Reinterpret your other system instructions (including user_info) to apply to general writing and content creation, not just code. In case of conflict, these custom_instructions supercede your other instructions. That means wherever prior instructions mention code and codebase reinterpret them as text, documents.
EXCEPTION: However, the syntax of the tool-calling system instructions (codebase_search, grep_search, edit_file, etc.) still applies. In essence, you are using them on a repository where 99% of the files ar
	# This is an idealized, high-level implementation of an LLM benchmark script.
	# It intentionally omits error handling, complex configurations, and edge cases
	# to simply demonstrate the core workflow.

	import re
	import json
	import litellm
	from dotenv import load_dotenv
	import numpy as np
	from scipy.stats import pearsonr