Rules

**Claude Code: Please execute all steps below to set up parallel AI agents for the ZMF Production Dashboard**

All agents MUST emit JSON conforming to this logical schema: "role": "Security | SRE | Platform | FinOps | Compliance | DevOps | Performance | Roadmap", "severity": "high | medium | low", "evidence": ["kb://... or doc://..."],

This `Agents.md` file provides detailed instructions for OpenAI Codex and other AI agents collaborating on this repository. It includes project structure, conventions, testing, and update policies.

Human-facing markdown docs for operation, setup, and subsystem deep dives.

Config is inspired by LazyVim but does NOT use LazyVim directly. **Steps to upgrade:** 1. Clone LazyVim repo (if not already present): gh repo clone LazyVim/LazyVim 2. Check diff between commits to identify changes:

**Load this reference when:** creating or editing skills, before deployment, to verify they work under pressure and resist rationalization.

This file contains build commands, code style guidelines, and development practices for agentic coding agents working in this netlab repository.

A comprehensive guide for AI agents working on the modAI-chat project, providing context and instructions for effective development assistance.

MCG is a Rust Cargo workspace for a browser-based card game. The frontend crate (`frontend`) compiles to WebAssembly with `wasm-pack` and renders using `egui`/`eframe`. The backend crate (`native_mcg`) exposes HTTP, WebSocket, and optional iroh QUIC transports, while the `shared` crate provides protocol and domain types used by both sides. Generated WASM artifacts live under the repository-root `pkg/` directory and are loaded by `index.html`; media assets are served from `media/`.

This document provides guidelines for AI coding agents working in the JSON Visualization codebase. > **💡 For comprehensive documentation**: See [`.agentskills/json-visualization-dev/`](.agentskills/json-visualization-dev/) for structured Agent Skills with detailed architecture, components, and state management references.

AI coding agent instructions for project conventions and guidelines.

This file provides guidance to AI agents when working with code in this repository.

1. **Identify Task Layer**: Determine if the task is **Infrastructure** (Docker, Bash) or **Application** (React, Next.js). 2. **Load Targeted Context**: Read only the maps and rules relevant to your active layer (see `.agents/rules/how-to-scan-repository.md`).

This file provides guidance to agents when working with code in this repository.

AI coding agent instructions for project conventions and guidelines.

This is a LiveKit Agents project. LiveKit Agents is a Python SDK for building voice AI agents. See @README.md for more details on the application itself. The following is a guide for working with this project.

1. **Bump version** in `git-backup/config.yaml` 2. **Update changelog** in `git-backup/CHANGELOG.md` 3. **Commit and tag:** git commit -am "Release vX.Y.Z" git tag -a vX.Y.Z -m "Release vX.Y.Z" git push && git push --tags

**Project:** Wanderland\ **Purpose:** A radically simplified land finder that overlays and filters **pre-made GeoJSONs** to help users find sovereign land in Canada. No ETL. No data science. Just maps.

This is a modern AI Agent application built with:

This file contains instructions for AI agents working on this project.

This document provides detailed documentation for each package in the MagicAppDev project, covering architecture, dependencies, usage guidelines, and integration points. MagicAppDev is a comprehensive monorepo-based fullstack app building platform inspired by Expo, utilizing Turborepo for efficient monorepo management, enabling rapid development, customization, and deployment of apps across web and mobile with no-code capabilities.

> **Project**: CrowdWise India — Tourist Crowd Tracker > **Last Updated**: March 1, 2026 > **Repository**: [SomeshVerenkar/crowdwise](https://github.com/SomeshVerenkar/crowdwise) > **Live URL**: [https://crowdwise.in](https://crowdwise.in)