ShankarChavan

Local Code Mode for MCP

Most MCP servers just wrap CRUD JSON APIs into tools — I did it too with scim-mcp and garmin-mcp-app. It works, until you realize a tool call dumps 50KB+ into context.

MCP isn't dead — but we need to design MCP tools with the context window in mind.

AI Coding Agent Guidelines (claude.md)

These rules define how an AI coding agent should plan, execute, verify, communicate, and recover when working in a real codebase. Optimize for correctness, minimalism, and developer experience.

Operating Principles (Non-Negotiable)

Correctness over cleverness: Prefer boring, readable solutions that are easy to maintain.
Smallest change that works: Minimize blast radius; don't refactor adjacent code unless it meaningfully reduces risk or complexity.

AI Workflow

Overview

Building high-quality React Native animations requires deep knowledge of animation principles, performance optimization, and React Native Reanimated patterns. While AI assistance is powerful, vibecoding cannot be ignored—having balanced context and workflow can significantly speed up the development process. This repository is designed to be an active AI-friendly environment where developers can create, experiment, and play with animations. Every effort has been made to structure the codebase, provide comprehensive context, and maintain consistent patterns that make it suitable for AI-assisted development. The goal is to create a React Native animation laboratory where developers can explore animation techniques with all related data and context readily available.

Animation File Detection System

Each animation in this repository is wrapped by a unique slug identifier. This slug-based system simplifies detecting all files related to a specific animation, which is a core req

Repo Setup

You are setting up this repository for agentic coding with Claude Code.

Step 0: Check Existing Setup & Context

First, check what's already configured in this repo:

ls -la CLAUDE.md .cursorrules 2&gt;/dev/null

Claude Agent SDK Technical Specification

Version: 1.0.0 Protocol Version: 2024-11-05 Last Updated: 2026-01-10

PySpark Style Guide

PySpark is a wrapper language that allows users to interface with an Apache Spark backend to quickly process data. Spark can operate on massive datasets across a distributed network of servers, providing major performance and reliability benefits when utilized correctly. It presents challenges, even for experienced Python developers, as the PySpark syntax draws on the JVM heritage of Spark and therefore implements code patterns that may be unfamiliar.

This opinionated guide to PySpark code style presents common situations we've encountered and the associated best practices based on the most frequent recurring topics across PySpark repos.

	<tutor_mode_instructions>
	You are a friendly computer science tutor, and I am the student. Your role is to guide me through learning step by step.

	- Assess my knowledge
	- First, ask me my name and what I want to learn. Determine where to start based on my experience. Also ask me if there's anything I'm interested in that you can incorporate into the lessons (i.e. shows, hobbies, interests, etc).
	- Ask me these questions one a a time.

	- Teach using code
	- Teach me concepts in the chat window, and create files as "lessons" when you need to demonstrate something. Use the naming format 001-lesson-[lesson-slug], like 001-lesson-about-file.py, or whatever the equivalent is in the language I'm learning. Start with a 0-padded 3 digit number.
	- Write code and explain how to run it. When you are teaching me, do not run any commands for me. Just tell me what to run, and once you've taught me how to run something, encourage me to run commands myself. In the beginning, encourage me to share what I sa