engineering-team/skills/tech-stack-evaluator/SKILL.md
Technology stack evaluation and comparison with TCO analysis, security assessment, and ecosystem health scoring. Use when comparing frameworks, evaluating technology stacks, calculating total cost of ownership, assessing migration paths, or analyzing ecosystem viability.
npx skillsauth add alirezarezvani/claude-skills tech-stack-evaluatorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Evaluate and compare technologies, frameworks, and cloud providers with data-driven analysis and actionable recommendations.
| Capability | Description | |------------|-------------| | Technology Comparison | Compare frameworks and libraries with weighted scoring | | TCO Analysis | Calculate 5-year total cost including hidden costs | | Ecosystem Health | Assess GitHub metrics, npm adoption, community strength | | Security Assessment | Evaluate vulnerabilities and compliance readiness | | Migration Analysis | Estimate effort, risks, and timeline for migrations | | Cloud Comparison | Compare AWS, Azure, GCP for specific workloads |
Compare React vs Vue for a SaaS dashboard.
Priorities: developer productivity (40%), ecosystem (30%), performance (30%).
Calculate 5-year TCO for Next.js on Vercel.
Team: 8 developers. Hosting: $2500/month. Growth: 40%/year.
Evaluate migrating from Angular.js to React.
Codebase: 50,000 lines, 200 components. Team: 6 developers.
The evaluator accepts three input formats:
Text - Natural language queries
Compare PostgreSQL vs MongoDB for our e-commerce platform.
YAML - Structured input for automation
comparison:
technologies: ["React", "Vue"]
use_case: "SaaS dashboard"
weights:
ecosystem: 30
performance: 25
developer_experience: 45
JSON - Programmatic integration
{
"technologies": ["React", "Vue"],
"use_case": "SaaS dashboard"
}
Compare technologies with customizable weighted criteria.
python scripts/stack_comparator.py --help
Calculate total cost of ownership over multi-year projections.
python scripts/tco_calculator.py --input assets/sample_input_tco.json
Analyze ecosystem health from GitHub, npm, and community metrics.
python scripts/ecosystem_analyzer.py --technology react
Evaluate security posture and compliance readiness.
python scripts/security_assessor.py --technology express --compliance soc2,gdpr
Estimate migration complexity, effort, and risks.
python scripts/migration_analyzer.py --from angular-1.x --to react
| Document | Content |
|----------|---------|
| references/metrics.md | Detailed scoring algorithms and calculation formulas |
| references/examples.md | Input/output examples for all analysis types |
| references/workflows.md | Step-by-step evaluation workflows |
| Level | Score | Interpretation | |-------|-------|----------------| | High | 80-100% | Clear winner, strong data | | Medium | 50-79% | Trade-offs present, moderate uncertainty | | Low | < 50% | Close call, limited data |
tools
Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin, C#, .NET, Java, C, C++, Rust, Ruby, PHP, and Dart/Flutter. Analyzes PRs for complexity and risk, checks code quality for SOLID violations and code smells, generates review reports. Use when reviewing pull requests, analyzing code quality, identifying issues, generating review checklists.
tools
Use when planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing/surveys, or product/user research. Triggers on "design this clinical study", "what sample size", "R&D budget", "burn rate", "capitalize or expense", "TAM SAM SOM", "market sizing", "survey design", "segment the market", "plan user interviews", "usability test", "synthesize research insights". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), and marketing-skill (campaign analytics).
development
Use when managing the money for an internal R&D program or portfolio — building a multi-period program budget with the F&A (indirect) split, tracking burn rate and runway against value-inflection milestones, or routing R&D cost items to a capitalize-vs-expense determination. Every budget output surfaces its assumptions block; capitalize-vs-expense is decision-support only and routes to a named finance owner — it never books an entry or decides accounting treatment. Distinct from finance/financial-analysis (corporate DCF, close, valuation) and research/grants (funding discovery — this manages money already won).
development
Use when planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.