brand-designer
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Skill publicada por daffy0208/ai-dev-standards, com 1.208 instalações medidas pelo skills.sh. Instale com: npx skills add daffy0208/ai-dev-standards@brand-designer
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daffy0208/ai-dev-standards/brand-designer
README
# AI Development Standards
[](https://github.com/daffy0208/ai-dev-standards/actions)
[](LICENSE)
[](https://www.typescriptlang.org/)
[](https://nodejs.org/)
**Version 3.1.0** | Last Updated: 2025-11-24
> A comprehensive framework of specialized AI skills, MCP servers, and development tools for AI-assisted software development. Features automated validation, agent evaluation, and quality assurance.
## 📊 Current Resources
**Total: 199 Resources**
| Category | Count | Description |
| ---------------- | ----- | ------------------------------------------------- |
| **Skills** | 64 | Specialized AI methodologies and workflows |
| **MCPs** | 51 | Model Context Protocol servers (executable tools) |
| **Tools** | 4 | Core utility scripts |
| **Components** | 75 | Reusable UI and system components |
| **Integrations** | 5 | Third-party service connectors |
**MCP Coverage:** 79.7% (51 MCPs supporting 64 Skills)
---
## ✨ What's New in 3.1.0
### 🎯 Agent Evaluation System (Phase 5.12)
Implement Eval-Driven Development (EDD) for continuous agent quality assurance:
- Automated testing against golden datasets
- Multiple grading strategies (exact, regex, LLM-based)
- Performance metrics and regression tracking
- 100% test pass rate in validation suite
```bash
# Run agent evaluations
node scripts/run-agent-evals.js --dataset tests/fixtures/golden-dataset-example.json --mock
```
### ⚡ Two-Tier Validation System
- **Quick Validation** (10-30s): Registry consistency, documentation
- **Full Validation** (2-5min): Includes linting, type checking, tests, agent evals
```bash
npm run validate:quick # Fast feedback
npm run validate:full # Comprehensive checks
```
### 📚 Enhanced Documentation
- `.claude/CLAUDE.md` - Complete Claude Code configuration guide
- `FINAL-RESOURCE-COUNTS.md` - Resource tracking and metrics
- `docs/VALIDATION-SYSTEM.md` - Validation methodology
---
## 🚀 Quick Start
> 📖 **New to this repository?** Check out our [Installation Guide](INSTALL.md) and [Quick Start Guide](QUICK-START-GUIDE.md) for step-by-step instructions.
### For New Projects
```bash
# Clone the repository
git clone https://github.com/daffy0208/ai-dev-standards.git
cd ai-dev-standards
# Install dependencies
npm install
# Run validation to ensure everything works
npm run validate
```
### For Existing Projects
```bash
# Clone as a reference
git clone https://github.com/daffy0208/ai-dev-standards.git ~/ai-dev-standards
# Reference skills and patterns in your .cursorrules or .claude/claude.md
# See docs/EXISTING-PROJECTS.md for integration guide
```
### Using with Claude Code
1. Open your project in Claude Code
2. Reference this repository in your project instructions:
```markdown
You have access to ai-dev-standards at ~/ai-dev-standards
When needed, reference skills from skills/ and patterns from standards/
Use the skill-registry.json to find relevant skills for tasks
```
3. Claude will automatically discover and use appropriate skills
---
## 📖 What This Repository Does
Think of this as **a shared knowledge base** between you and Claude:
### 🎓 64 Specialized Skills
Methodologies Claude follows automatically:
- **Product**: mvp-builder, product-strategist, go-to-market-planner
- **AI/ML**: rag-implementer, multi-agent-architect, knowledge-graph-builder
- **Development**: frontend-builder, api-designer, backend-architect
- **Infrastructure**: deployment-advisor, security-engineer, performance-optimizer
- **Design**: ux-designer, visual-designer, design-system-architect
- **Quality**: testing-strategist, quality-auditor, agent-evaluator
### 🔧 51 MCP Servers
Executable tools that extend Claude's capabilities:
- **Search**: semantic-search-mcp, dark-matter-analyzer-mcp
- **Quality**: code-quality-scanner-mcp, security-scanner-mcp, test-runner-mcp
- **AI/Data**: vector-database-mcp, embedding-generator-mcp, knowledge-base-mcp
- **Design**: figma-sync-mcp, design-token-manager-mcp, theme-builder-mcp
- **DevOps**: deployment-orchestrator-mcp, database-migration-mcp
### 📐 Architecture Patterns
Proven approaches for complex systems:
- RAG architectures (Naive, Advanced, Modular)
- Multi-agent coordination patterns
- Event-driven systems
- Real-time data pipelines
- Authentication patterns
### 🛡️ Quality Assurance
- Automated validation system (2-tier)
- Agent evaluation framework (EDD)
- Security best practices
- Performance standards
- Accessibility guidelines
---
## 💡 Key Features
### ⚡ Automated Validation
```bash
# Quick validation (10-30 seconds)
npm run validate:quick
# Full validation (2-5 minutes)
npm run validate:full
# Agent evaluation only
node scripts/run-agent-evals.js --dataset tests/fixtures/golden-dataset-example.json --mock
```
**Validates:**
- ✅ Registry consistency
- ✅ Documentation accuracy
- ✅ Code quality (ESLint)
- ✅ Type safety (TypeScript)
- ✅ Test coverage
- ✅ **Agent performance (NEW)**
### 🤖 Agent Evaluation System
Test AI agents against golden datasets to ensure consistent, high-quality outputs:
```javascript
{
"tests": [
{
"id": "T001",
"input": "Create a React button component with TypeScript",
"expected": "import React from 'react';",
"grading": { "type": "contains", "threshold": 0.8 }
}
]
}
```
**Features:**
- Multiple grading types (exact match, contains, regex, LLM-graded)
- Performance metrics (latency, success rate, score)
- Historical tracking and regression detection
- Custom dataset support
### 📊 Comprehensive Documentation
- **For Developers**: `docs/GETTING-STARTED.md`, `docs/QUICK-START.md`
- **For AI**: `meta/PROJECT-CONTEXT.md`, `meta/HOW-TO-USE.md`
- **Configuration**: `.claude/CLAUDE.md`, `FINAL-RESOURCE-COUNTS.md`
- **Validation**: `docs/VALIDATION-SYSTEM.md`
### 🎯 Smart Resource Discovery
```bash
# Find skills for a task
grep -r "mvp" meta/skill-registry.json
# Search all resources
grep -r "authentication" meta/
# View resource counts
cat FINAL-RESOURCE-COUNTS.md
```
---
## 🗂️ Repository Structure
```
ai-dev-standards/
├── skills/ # 64 specialized methodologies
│ ├── mvp-builder/ # MVP development & prioritization
│ ├── rag-implementer/ # RAG system implementation
│ ├── api-designer/ # API design patterns
│ └── [61 more...]
│
├── mcp-servers/ # 51 executable tools
│ ├── semantic-search-mcp/ # Semantic code search
│ ├── vector-database-mcp/ # Vector DB integration
│ ├── code-quality-scanner-mcp/
│ └── [48 more...]
│
├── standards/ # Architecture & best practices
│ ├── architecture-patterns/
│ ├── best-practices/
│ ├── coding-conventions/
│ └── project-structure/
│
├── meta/ # Resource registry & context
│ ├── registry.json # Master resource registry
│ ├── skill-registry.json # Skill catalog
│ ├── mcp-registry.json # MCP catalog
│ └── PROJECT-CONTEXT.md # For AI assistants
│
├── docs/ # Comprehensive documentation
│ ├── GETTING-STARTED.md
│ ├── VALIDATION-SYSTEM.md
│ ├── AGENT-VALIDATION.md # NEW!
│ └── [40+ more guides...]
│
├── scripts/ # Automation & validation
│ ├── run-agent-evals.js # NEW! Agent evaluation
│ ├── validate-full.sh # Full validation suite
│ └── [20+ more scripts...]
│
└── tests/ # Test suites & fixtures
├── fixtures/
│ └── golden-dataset-example.json # NEW!
└── [150+ test files...]
```
---
## 🎯 Usage Examples
### Example 1: Starting a New Project
```
User: "I want to build a SaaS product for invoice management"
Claude uses:
1. product-strategist → Validate problem-solution fit
2. mvp-builder → Identify P0 features (invoicing, payment tracking)
3. frontend-builder → React/Next.js structure
4. api-designer → REST API design
5. deployment-advisor → Vercel + Railway recommendation
6. security-engineer → Auth, data encryption, PCI compliance
```
### Example 2: Implementing AI Search
```
User: "Add AI-powered search to our documentation"
Claude uses:
1. rag-implementer → RAG methodology
2. rag-pattern.md → Advanced RAG architecture
3. vector-database-mcp → Pinecone integration
4. embedding-generator-mcp → OpenAI embeddings
5. semantic-search-mcp → Search implementation
```
### Example 3: Code Quality Audit
```
User: "Audit our codebase for quality issues"
Claude uses:
1. quality-auditor → Comprehensive audit methodology
2. code-quality-scanner-mcp → Static analysis
3. security-scanner-mcp → Vulnerability detection
4. performance-profiler-mcp → Performance bottlenecks
5. test-runner-mcp → Test coverage analysis
6. agent-evaluator → AI agent quality checks (NEW!)
```
---
## 🔍 Finding Skills
### By Task
```bash
# Search skills by keyword
grep -i "authentication" meta/skill-registry.json
grep -i "database" meta/skill-registry.json
grep -i "testing" meta/skill-registry.json
```
### By Category
View `meta/skill-registry.json` for complete categorization:
- **Product & Business** (8 skills)
- **AI & Machine Learning** (10 skills)
- **Frontend Development** (6 skills)
- **Backend Development** (8 skills)
- **Infrastructure & DevOps** (8 skills)
- **Design & UX** (12 skills)
- **Quality & Testing** (12 skills)
### Auto-Discovery
Skills activate automatically based on your conversation with Claude. Just describe what you want to build!
---
## ⚙️ Validation System
### Two-Tier Approach
#### Tier 1: Quick Validation (10-30 seconds)
```bash
npm run validate:quick
```
**Checks:**
- Registry consistency
- Documentation accuracy
- Configuration files
- Basic CLI functionality
**Use when:** Before commits, during rapid development
#### Tier 2: Full Validation (2-5 minutes)
```bash
npm run validate:full
```
**Checks:**
- Everything in Tier 1 +
- ESLint code quality
- TypeScript type checking
- Unit & integration tests
- **Agent Evaluation (Phase 5.12)** ✨ NEW
- Build verification
**Use when:** Before pushing, in CI/CD, before releases
### Agent Evaluation (Phase 5.12)
Test AI agents against golden datasets:
```bash
# Run with mock agent (for testing)
node scripts/run-agent-evals.js --dataset tests/fixtures/golden-dataset-example.json --mock
# Run with real agent (production)
node scripts/run-agent-evals.js --dataset tests/fixtures/golden-dataset-example.json
# Verbose output
node scripts/run-agent-evals.js --dataset tests/fixtures/golden-dataset-example.json --mock --verbose
```
**Output:**
```
📊 Summary
----------------------------------------
Total Tests: 10
Passed: 10
Failed: 0
Pass Rate: 100.0%
Avg Score: 0.96
Avg Latency: 47ms
----------------------------------------
✅ Agent Evaluations PASSED
```
See `docs/VALIDATION-SYSTEM.md` for complete methodology.
---
## 📚 Documentation
### Getting Started
- `docs/QUICK-START.md` - 5-minute quick start
- `docs/GETTING-STARTED.md` - Comprehensive setup guide
- `docs/EXISTING-PROJECTS.md` - Integration for existing projects
### Validation & Quality
- `docs/VALIDATION-SYSTEM.md` - Validation methodology
- `docs/AGENT-VALIDATION.md` - Agent evaluation guide (NEW!)
- `.claude/commands/validate.md` - Validation command reference
### Configuration
- `.claude/CLAUDE.md` - Claude Code configuration (NEW!)
- `FINAL-RESOURCE-COUNTS.md` - Resource metrics (NEW!)
- `meta/PROJECT-CONTEXT.md` - For AI assistants
- `meta/HOW-TO-USE.md` - Navigation guide
### Development
- `CONTRIBUTING.md` - Contribution guidelines
- `docs/MCP-DEVELOPMENT-ROADMAP.md` - MCP development guide
- `docs/TROUBLESHOOTING.md` - Common issues
---
## 🛠️ Development
### Running Tests
```bash
# Run all tests
npm test
# Run specific test suites
npm run test:unit # Unit tests only
npm run test:registry # Registry validation
npm run test:cli # CLI tests
# Run agent evaluations
npm run test:agent-eval # Agent evaluation suite
```
### Validation Commands
```bash
# Linting
npm run lint # Check code quality
npm run lint:fix # Auto-fix issues
# Type Checking
npm run typecheck # TypeScript validation
# Formatting
npm run format # Format code with Prettier
npm run format:check # Check formatting
# Registry
npm run validate:registries # Validate resource registries
npm run generate:registries # Regenerate registries
```
### Creating Custom Datasets
Create your own agent evaluation datasets:
```json
{
"version": "1.0.0",
"description": "Your custom test dataset",
"tests": [
{
"id": "T001",
"category": "code-generation",
"description": "Test description",
"input": "Your test prompt",
"expected": "Expected output or pattern",
"grading": {
"type": "contains", // or "exact", "regex", "llm-graded"
"threshold": 0.8
},
"tags": ["category", "feature"]
}
]
}
```
---
## 📊 Quality Metrics
### Resource Coverage
- **Skills**: 64 specialized methodologies
- **MCPs**: 51 executable tools
- **MCP Coverage**: 79.7% (51 MCPs / 64 Skills)
- **Documentation**: 100% of skills documented
### Validation Status
- ✅ **Registry Validation**: Passing
- ✅ **Type Checking**: Passing
- ✅ **Linting**: Passing (790 warnings, 0 errors)
- ✅ **Agent Evaluation**: Passing (100% success rate)
- ✅ **Test Coverage**: 78%
### Performance
- **Agent Evaluation**: 47ms avg latency
- **Quick Validation**: 10-30 seconds
- **Full Validation**: 2-5 minutes
---
## 🗺️ Roadmap
### ✅ Completed
- **v3.1.0** (2025-11-24): Agent Evaluation System
- Phase 5.12 implementation
- Golden dataset support
- Multiple grading strategies
- Performance metrics
- **v3.0.3** (2025-11-14): Validation System
- Two-tier validation
- Registry automation
- Documentation consolidation
- **v2.1.0** (2025-10-29): Orchestration
- Claude Code integration
- Registry validation
- 100% resource discovery
### 🔜 Planned
- **v3.2.0**: Enhanced Agent Evaluation
- Real agent integration
- Advanced LLM grading
- Regression tracking dashboard
- **v3.3.0**: MCP Expansion
- Additional development MCPs
- Better skill-MCP coverage
- Performance improvements
- **v4.0.0**: Ecosystem Integration
- GitHub Actions workflows
- VSCode extension
- Web dashboard
---
## 🤝 Contributing
We welcome contributions! See `CONTRIBUTING.md` for guidelines.
### Ways to Contribute
1. **Add Skills**: Create new specialized methodologies
2. **Add MCPs**: Build executable tools
3. **Improve Documentation**: Clarify guides and examples
4. **Report Issues**: Help us find and fix bugs
5. **Create Datasets**: Expand agent evaluation coverage
### Development Setup
```bash
# Clone the repository
git clone https://github.com/daffy0208/ai-dev-standards.git
cd ai-dev-standards
# Install dependencies
npm install
# Run validation
npm run validate:quick
# Make changes and test
npm test
# Submit PR
```
---
## 📝 License
MIT License - see [LICENSE](LICENSE) for details
---
## 🙏 Acknowledgments
This repository synthesizes best practices from:
- Claude Code official patterns
- Production software development
- AI-assisted development research
- Community feedback and contributions
**Maintained by:** [@daffy0208](https://github.com/daffy0208)
---
## 📞 Support
- **Documentation**: `docs/` directory
- **Issues**: [GitHub Issues](https://github.com/daffy0208/ai-dev-standards/issues)
- **Discussions**: [GitHub Discussions](https://github.com/daffy0208/ai-dev-standards/discussions)
---
## 🔗 Quick Links
### For Developers
- [Quick Start](docs/QUICK-START.md)
- [Getting Started](docs/GETTING-STARTED.md)
- [Validation System](docs/VALIDATION-SYSTEM.md)
- [Troubleshooting](docs/TROUBLESHOOTING.md)
### For AI Assistants
- [Project Context](meta/PROJECT-CONTEXT.md)
- [How to Use](meta/HOW-TO-USE.md)
- [Skill Registry](meta/skill-registry.json)
- [MCP Registry](meta/mcp-registry.json)
### Configuration
- [Claude Code Config](.claude/CLAUDE.md)
- [Resource Counts](FINAL-RESOURCE-COUNTS.md)
- [Validation Command](.claude/commands/validate.md)
---
**Built for excellence in AI-assisted development** 🚀