Technology Stack Evaluator — Security & Safety

The Technology Stack Evaluator skill provides data-driven analysis for comparing technologies, frameworks, and cloud providers. It produces weighted scoring comparisons, 5-year TCO calculations, ecosystem health assessments, security evaluations, and migration analyses, with outputs ranging from quick comparisons to full reports.

engineering claude-code

📂 View skill on GitHub ↗ 🚀 How to use

✨ What it does

Weighted scoring for technology comparisons
5-year TCO calculation including hidden costs
Ecosystem health scoring from GitHub and npm metrics
Security assessment with compliance readiness
Migration effort and risk estimation
Cloud provider comparison for workloads

🎯 When to use it

Comparing frontend or backend frameworks for a new project
Evaluating cloud providers for a specific workload
Planning a technology migration with risk assessment
Calculating total cost of ownership for build vs. buy decisions
Assessing the viability of an open-source library

🚀 How to use

Trigger the skill by asking to compare technologies, calculate TCO, assess migration, or evaluate ecosystem health. Provide natural language details or structured YAML/JSON input. Example prompts:

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.

📄 Output: The skill produces a comparison report with scores, TCO breakdown, security summary, and recommendations, with depth depending on the chosen analysis type.

📦 Add this skill to Claude Code

# 1. Get the skills repo
git clone --depth 1 https://github.com/alirezarezvani/claude-skills /tmp/claude-skills

# 2. Copy this skill into your project (or ~/.claude/skills for all projects)
mkdir -p .claude/skills
cp -r /tmp/claude-skills/engineering-team/skills/tech-stack-evaluator .claude/skills/tech-stack-evaluator

Skill source: engineering-team/skills/tech-stack-evaluator/SKILL.md

⚠️ Good to know

Not suitable for trivial decisions, mandated choices, or emergency production issues; requires sufficient data for high confidence.

❓ FAQ

What input formats does the skill accept?

It accepts natural language text, structured YAML, or JSON input.

What analysis types are available?

Quick Comparison (200-300 tokens), Standard Analysis (500-800 tokens), and Full Report (1200-1500 tokens).

How are confidence levels determined?

High confidence is 80-100%, medium is 50-79%, and low is below 50%, based on data strength and clarity of the winner.

🤖 Overview, features, install steps and FAQ were generated from the project's SKILL.md on Sep 4, 2026. Always check the original source before running commands.