Borrowing it
Nothing to install: this file belongs to vsiwach/MCP-Resume-AWS. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/vsiwach/MCP-Resume-AWS/main/CLAUDE.mdgit clone --depth 1 https://github.com/vsiwach/MCP-Resume-AWSWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/vsiwach/mcp-resume-aws/claude-md)<a href="https://agentmods.dev/instructions/vsiwach/mcp-resume-aws/claude-md"><img src="https://agentmods.dev/badge/instructions/vsiwach/mcp-resume-aws/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/vsiwach/mcp-resume-aws/claude-md"><img src="https://agentmods.dev/badge/instructions/vsiwach/mcp-resume-aws/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00738 | $0.00738 |
| Opus 5.5 | $0.00295 | $0.00295 |
| Sonnet 5.5 | $0.00148 | $0.00148 |
| Haiku 4.5 | $0.00074 | $0.00074 |
Grade A, and why
MCP-Resume-AWS CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
Personal Resume Agent is a standalone AI agent that processes resume files and provides intelligent responses about professional background through MCP (Model Context Protocol). It uses RAG (Retrieval-Augmented Generation) with ChromaDB and sentence transformers to make resume information queryable through Claude Desktop.
Development Commands
Running the Application
- Test the agent directly:
cd src && python personal_resume_agent.py - Run as MCP server:
cd src && python mcp_resume_server.py - Example usage demo:
cd examples && python example_usage.py
Testing
- Run main test suite:
python tests/test_resume_agent.py - Tests use pytest framework and include temporary file handling
Dependencies
- Install dependencies:
pip install -r requirements.txt - Core requirements: chromadb, sentence-transformers, PyPDF2, python-docx, transformers, torch
Architecture
Core Components
- PersonalResumeAgent (
src/personal_resume_agent.py): Main agent logic with query processing, skill matching, and response generation - ResumeRAGSystem (
src/resume_rag.py): ChromaDB-based vector storage and retrieval system with document processing - MCP Server (
src/mcp_resume_server.py): JSON-RPC compliant MCP server for Claude Desktop integration
Data Processing Flow
- Resume files (PDF, DOCX, TXT, MD) placed in
data/directory - ResumeRAGSystem extracts content and splits into chunks
- Content embedded using sentence-transformers model ('all-MiniLM-L6-v2')
- Chunks stored in ChromaDB at
data/resume_vectordb/ - Agent processes queries via semantic search and response generation
MCP Integration
- Exposes
query_resumetool for Claude Desktop - Implements JSON-RPC 2.0 protocol over stdio transport
- Returns structured responses with content blocks
File Structure
personal-resume-agent/
├── src/ # Core source code
│ ├── personal_resume_agent.py # Main agent implementation
│ ├── resume_rag.py # RAG system with ChromaDB
│ └── mcp_resume_server.py # MCP server for Claude Desktop
├── data/ # Resume files and vector database (excluded from git)
├── tests/ # Test suite with sample data generation
├── examples/ # Usage examples and demonstrations
└── docs/ # Additional documentation
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 84 lines · 738 tokens per session scan A 3bf805edd4e6
MCP-Resume-AWS CLAUDE.md is an instructions file published in the GitHub repository vsiwach/MCP-Resume-AWS (0 stars, last pushed 1y ago), licensed MIT. It adds 738 tokens to every session, about $0.0030 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
MCP-Resume-AWS CLAUDE.md compared with instructions from other repositories
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