Awesome-omni-skill project-overview

Background knowledge about CaCrFeedFormula project architecture, features, and context. Automatically loaded for AI reference, not directly user-invocable.

install
source · Clone the upstream repo
git clone https://github.com/diegosouzapw/awesome-omni-skill
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/development/project-overview-majiayu000" ~/.claude/skills/diegosouzapw-awesome-omni-skill-project-overview-024c38 && rm -rf "$T"
manifest: skills/development/project-overview-majiayu000/SKILL.md
source content

CaCrFeedFormula Project Overview

Intelligent Feed Formula Optimization System built with Tauri + Rust + React TypeScript.

Project Context

CaCrFeedFormula is an industrial-grade desktop application for feed formula optimization, integrating AI-assisted optimization, linear programming (HiGHS solver), and comprehensive feed management capabilities.

Core Technology Stack

Backend (Rust 2021):

  • Framework: Tauri 2.9.0 + Tokio 1.37 (full async runtime)
  • Database: SQLite + SQLx 0.7 (compile-time type safety)
  • Optimization: HiGHS 1.12 (industrial-grade LP solver)
  • Caching: Moka 0.12 (high-performance concurrent cache)
  • Type Binding: specta 2.0 + tauri-specta 2.0 (auto TypeScript generation)
  • AI Integration: reqwest + eventsource-stream (streaming responses)
  • Parallel Compute: Rayon 1.8

Frontend (React 19.1 + TypeScript 5.8):

  • UI Framework: Ant Design 5.26 + Tailwind CSS 4.1
  • State Management: TanStack Query 5.17
  • Build Tool: Vite 7.0
  • Visualization: Recharts 2.15
  • Animation: Framer Motion 11

Project Structure

cacrfeedformula/
├── src/                          # Rust backend source
│   ├── ai/                       # AI service module
│   ├── database/                 # Database connection
│   ├── formula/                  # Formula optimization core
│   ├── material/                 # Material management
│   ├── species/                  # Species management
│   ├── factory/                  # Factory management
│   ├── premix/                   # Premix design
│   ├── profit/                   # Profit/loss analysis
│   ├── prediction/               # Nutrition prediction
│   ├── production_batch/         # Production batch management
│   └── system/                   # System services
├── frontend/                     # React frontend source
│   └── src/
│       ├── components/           # React components
│       │   ├── AIChat/          # AI chat component
│       │   └── common/          # Common components
│       └── bindings.ts          # Auto-generated type bindings
├── migrations/                   # Database migrations
└── .claude/
    ├── skills/                   # Custom Claude Code skills
    └── rules/                    # Detailed development standards

Core Features

1. Formula Optimization System

  • Linear programming optimization (cost minimization)
  • Manual formula design
  • Premix reverse calculation design
  • Formula version management
  • Formula analysis and reporting
  • 167 Tauri commands for comprehensive formula operations

2. Data Management

  • Material management (built-in China Feed Composition & Nutrition Value Database)
  • Species management (multiple species, growth stages, nutrition standards)
  • Factory management (multi-factory data isolation)
  • Production batch management (batch lifecycle, material requirement calculation)
  • Inventory management (stock check, variance analysis, purchase planning)

3. Analysis & Decision Support

  • Profit/loss analysis (comprehensive cost accounting, real-time P&L)
  • Nutrition prediction (NRC-based energy prediction)
  • Sensitivity analysis (shadow prices, bottleneck constraint identification)

4. AI Intelligent Assistant

  • Context-aware professional feed formula consultation
  • Streaming responses with typewriter effect
  • Multi-turn conversations
  • Supports OpenAI, DeepSeek, OpenRouter platforms

Project Characteristics

  1. Desktop Application: Not a web app; cross-platform desktop app built with Tauri
  2. High-Performance Computing: Rust backend ensures speed and stability
  3. Type Safety: specta auto-generates TypeScript types, ensuring frontend-backend type consistency
  4. Async-First: Comprehensive use of Tokio async runtime
  5. Industrial-Grade Optimization: HiGHS solver supports large-scale formula optimization
  6. AI Integration: Streaming AI responses with real-time typewriter effect

Development Workflow Context

Typical Development Scenarios:

  • Formula Engine: Implementing complex linear programming algorithms with HiGHS
  • Material Database: Managing large datasets with SQLite + SQLx
  • Desktop UI: Building responsive Ant Design interfaces with React 19
  • Tauri Commands: Creating type-safe Rust ↔ TypeScript communication
  • AI Features: Integrating streaming AI responses into desktop workflows
  • Batch Processing: Handling production batch calculations and scheduling

Key Integration Points:

  • Rust ↔ TypeScript: specta generates bindings.ts after every Rust change
  • Database ↔ Business Logic: SQLx macros provide compile-time SQL validation
  • Frontend ↔ Backend: TanStack Query manages server state via Tauri commands
  • AI ↔ User: Streaming SSE responses with real-time UI updates

Project Scale

  • 167 Tauri commands across 10 modules
  • Comprehensive feed database with 1000+ materials
  • Multi-tenancy support with factory-level data isolation
  • Complex optimization handling 100+ variables and 50+ constraints

Development Standards

The project follows strict development standards documented in

.claude/rules/
:

  • Rust backend standards (02-rust-backend-standards.md)
  • React frontend standards (03-react-frontend-standards.md)
  • Database standards (04-database-standards.md)
  • LSP usage standards (05-lsp-usage-standards.md)

All these standards are enforced via automated hooks and skills.

When This Context Is Useful

This project overview is automatically loaded to help Claude understand:

  • Architecture decisions when proposing changes
  • Technology choices when solving problems
  • Integration patterns when adding features
  • Scale considerations when optimizing performance
  • Domain context (feed formulation, nutrition, optimization)

This knowledge enables Claude to make more informed, project-appropriate recommendations without requiring repeated explanations of the project's nature and structure.