AI Development Standards

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)

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✨ 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

# 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

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

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🚀 Quick Start

📖 New to this repository? Check out our Installation Guide and Quick Start Guide for step-by-step instructions.

For New Projects

# 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

# 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:

   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

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📖 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

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💡 Key Features

⚡ Automated Validation

# 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:

{
  "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

# 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

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🗂️ 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...]

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🎯 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!)

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🔍 Finding Skills

By Task

# 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!

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⚙️ Validation System

Two-Tier Approach

Tier 1: Quick Validation (10-30 seconds)

npm run validate:quick

Checks:

• Registry consistency
• Documentation accurac