tech-stack-evaluator

Technology Stack Evaluator

A comprehensive evaluation framework for comparing technologies, frameworks, cloud providers, and complete technology stacks. Provides data-driven recommendations with TCO analysis, security assessment, ecosystem health scoring, and migration path analysis.

Capabilities

This skill provides eight comprehensive evaluation capabilities:

• Technology Comparison: Head-to-head comparisons of frameworks, languages, and tools (React vs Vue, PostgreSQL vs MongoDB, Node.js vs Python)
• Stack Evaluation: Assess complete technology stacks for specific use cases (real-time collaboration, API-heavy SaaS, data-intensive platforms)
• Maturity & Ecosystem Analysis: Evaluate community health, maintenance status, long-term viability, and ecosystem strength
• Total Cost of Ownership (TCO): Calculate comprehensive costs including licensing, hosting, developer productivity, and scaling
• Security & Compliance: Analyze vulnerabilities, compliance readiness (GDPR, SOC2, HIPAA), and security posture
• Migration Path Analysis: Assess migration complexity, risks, timelines, and strategies from legacy to modern stacks
• Cloud Provider Comparison: Compare AWS vs Azure vs GCP for specific workloads with cost and feature analysis
• Decision Reports: Generate comprehensive decision matrices with pros/cons, confidence scores, and actionable recommendations

Input Requirements

Flexible Input Formats (Automatic Detection)

The skill automatically detects and processes multiple input formats:

Text/Conversational:

"Compare React vs Vue for building a SaaS dashboard"
"Evaluate technology stack for real-time collaboration platform"
"Should we migrate from MongoDB to PostgreSQL?"

Structured (YAML):

comparison:
  technologies:
    - name: "React"
    - name: "Vue"
  use_case: "SaaS dashboard"
  priorities:
    - "Developer productivity"
    - "Ecosystem maturity"
    - "Performance"

Structured (JSON):

{
  "comparison": {
    "technologies": ["React", "Vue"],
    "use_case": "SaaS dashboard",
    "priorities": ["Developer productivity", "Ecosystem maturity"]
  }
}

URLs for Ecosystem Analysis:

• GitHub repository URLs (for health scoring)
• npm package URLs (for download statistics)
• Technology documentation URLs (for feature extraction)

Analysis Scope Selection

Users can select which analyses to run:

• Quick Comparison: Basic scoring and comparison (200-300 tokens)
• Standard Analysis: Scoring + TCO + Security (500-800 tokens)
• Comprehensive Report: All analyses including migration paths (1200-1500 tokens)
• Custom: User selects specific sections (modular)

Output Formats

Context-Aware Output

The skill automatically adapts output based on environment:

Claude Desktop (Rich Markdown):

• Formatted tables with color indicators
• Expandable sections for detailed analysis
• Visual decision matrices
• Charts and graphs (when appropriate)

CLI/Terminal (Terminal-Friendly):

• Plain text tables with ASCII borders
• Compact formatting
• Clear section headers
• Copy-paste friendly code blocks

Progressive Disclosure Structure

Executive Summary (200-300 tokens):

• Recommendation summary
• Top 3 pros and cons
• Confidence level (High/Medium/Low)
• Key decision factors

Detailed Breakdown (on-demand):

• Complete scoring matrices
• Detailed TCO calculations
• Full security analysis
• Migration complexity assessment
• All supporting data and calculations

Report Sections (User-Selectable)

Users choose which sections to include:

1. Scoring & Comparison Matrix
2. Weighted decision scores
3. Head-to-head comparison tables
4. Strengths and weaknesses

1. Financial Analysis
2. TCO breakdown (5-year projection)
3. ROI analysis
4. Cost per user/request metrics
5. Hidden cost identification

1. Ecosystem Health
2. Community size and activity
3. GitHub stars, npm downloads
4. Release frequency and maintenance
5. Issue response times
6. Viability assessment

1. Security & Compliance
2. Vulnerability count (CVE database)
3. Security patch frequency
4. Compliance readiness (GDPR, SOC2, HIPAA)
5. Security scoring

1. Migration Analysis (when applicable)
2. Migration complexity scoring
3. Code change estimates
4. Data migration requirements
5. Downtime assessment
6. Risk mitigation strategies

1. Performance Benchmarks
2. Throughput/latency comparisons
3. Resource usage analysis
4. Scalability characteristics

How to Use

Basic Invocations

Quick Comparison:

"Compare React vs Vue for our SaaS dashboard project"
"PostgreSQL vs MongoDB for our application"

Stack Evaluation:

"Evaluate technology stack for real-time collaboration platform:
Node.js, WebSockets, Redis, PostgreSQL"

TCO Analysis:

"Calculate total cost of ownership for AWS vs Azure for our workload:
- 50 EC2/VM instances
- 10TB storage
- High bandwidth requirements"

Security Assessment:

"Analyze security posture of our current stack:
Express.js, MongoDB, JWT authentication.
Need SOC2 compliance."

Migration Path:

"Assess migration from Angular.js (1.x) to React.
Application has 50,000 lines of code, 200 components."

Advanced Invocations

Custom Analysis Sections:

"Compare Next.js vs Nuxt.js.
Include: Ecosystem health, TCO, and performance benchmarks.
Skip: Migration analysis, compliance."

Weighted Decision Criteria:

"Compare cloud providers for ML workloads.
Priorities (weighted):
- GPU availability (40%)
- Cost (30%)
- Ecosystem (20%)
- Support (10%)"

Multi-Technology Comparison:

"Compare: React, Vue, Svelte, Angular for enterprise SaaS.
Use case: Large team (20+ developers), complex state management.
Generate comprehensive decision matrix."

Scripts

Core Modules

• stack_comparator.py: Main comparison engine with weighted scoring algorithms
• tco_calculator.py: Total Cost of Ownership calculations (licensing, hosting, developer productivity, scaling)
• ecosystem_analyzer.py: Community health scoring, GitHub/npm metrics, viability assessment
• security_assessor.py: Vulnerability analysis, compliance readiness, security scoring
• migration_analyzer.py: Migration complexity scoring, risk assessment, effort estimation
• format_detector.py: Automatic input format detection (text, YAML, JSON, URLs)
• report_generator.py: Context-aware report generation with progressive disclosure

Utility Modules

• data_fetcher.py: Fetch real-time data from GitHub, npm, CVE databases
• benchmark_processor.py: Process and normalize performance benchmark data
• confidence_scorer.py: Calculate confidence levels for recommendations

Metrics and Calculations

1. Scoring & Comparison Metrics

Technology Comparison Matrix:

• Feature completeness (0-100 scale)
• Learning curve assessment (Easy/Medium/Hard)
• Developer experience scoring
• Documentation quality (0-10 scale)
• Weighted total scores

Decision Scoring Algorithm:

• User-defined weights for criteria
• Normalized scoring (0-100)
• Confidence intervals
• Sensitivity analysis

2. Financial Calculations

TCO Components:

• Initial Costs: Licensing, training, migration
• Operational Costs: Hosting, support, maintenance (monthly/yearly)
• Scaling Costs: Per-user costs, infrastructure scaling projections
• Developer Productivity: Time-to-market impact, development speed multipliers
• Hidden Costs: Technical debt, vendor lock-in risks

ROI Calculations:

• Cost savings projections (3-year, 5-year)
• Productivity gains (developer hours saved)
• Break-even analysis
• Risk-adjusted returns

Cost Per Metric:

• Cost per user (monthly/yearly)
• Cost per API request
• Cost per GB stored/transferred
• Cost per compute hour

3. Maturity & Ecosystem Metrics