tech-stack-evaluator
$
npx mdskill add alirezarezvani/claude-skills/tech-stack-evaluatorEvaluate and compare technologies, frameworks, and cloud providers with data-driven analysis and actionable recommendations.
SKILL.md
.github/skills/tech-stack-evaluatorView on GitHub ↗
---
name: "tech-stack-evaluator"
description: 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.
---
# Technology Stack Evaluator
Evaluate and compare technologies, frameworks, and cloud providers with data-driven analysis and actionable recommendations.
## Table of Contents
- [Capabilities](#capabilities)
- [Quick Start](#quick-start)
- [Input Formats](#input-formats)
- [Analysis Types](#analysis-types)
- [Scripts](#scripts)
- [References](#references)
---
## Capabilities
| 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 |
---
## Quick Start
### Compare Two Technologies
```
Compare React vs Vue for a SaaS dashboard.
Priorities: developer productivity (40%), ecosystem (30%), performance (30%).
```
### Calculate TCO
```
Calculate 5-year TCO for Next.js on Vercel.
Team: 8 developers. Hosting: $2500/month. Growth: 40%/year.
```
### Assess Migration
```
Evaluate migrating from Angular.js to React.
Codebase: 50,000 lines, 200 components. Team: 6 developers.
```
---
## Input Formats
The evaluator accepts three input formats:
**Text** - Natural language queries
```
Compare PostgreSQL vs MongoDB for our e-commerce platform.
```
**YAML** - Structured input for automation
```yaml
comparison:
technologies: ["React", "Vue"]
use_case: "SaaS dashboard"
weights:
ecosystem: 30
performance: 25
developer_experience: 45
```
**JSON** - Programmatic integration
```json
{
"technologies": ["React", "Vue"],
"use_case": "SaaS dashboard"
}
```
---
## Analysis Types
### Quick Comparison (200-300 tokens)
- Weighted scores and recommendation
- Top 3 decision factors
- Confidence level
### Standard Analysis (500-800 tokens)
- Comparison matrix
- TCO overview
- Security summary
### Full Report (1200-1500 tokens)
- All metrics and calculations
- Migration analysis
- Detailed recommendations
---
## Scripts
### stack_comparator.py
Compare technologies with customizable weighted criteria.
```bash
python scripts/stack_comparator.py --help
```
### tco_calculator.py
Calculate total cost of ownership over multi-year projections.
```bash
python scripts/tco_calculator.py --input assets/sample_input_tco.json
```
### ecosystem_analyzer.py
Analyze ecosystem health from GitHub, npm, and community metrics.
```bash
python scripts/ecosystem_analyzer.py --technology react
```
### security_assessor.py
Evaluate security posture and compliance readiness.
```bash
python scripts/security_assessor.py --technology express --compliance soc2,gdpr
```
### migration_analyzer.py
Estimate migration complexity, effort, and risks.
```bash
python scripts/migration_analyzer.py --from angular-1.x --to react
```
---
## References
| 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 |
---
## Confidence Levels
| Level | Score | Interpretation |
|-------|-------|----------------|
| High | 80-100% | Clear winner, strong data |
| Medium | 50-79% | Trade-offs present, moderate uncertainty |
| Low | < 50% | Close call, limited data |
---
## When to Use
- Comparing frontend/backend frameworks for new projects
- Evaluating cloud providers for specific workloads
- Planning technology migrations with risk assessment
- Calculating build vs. buy decisions with TCO
- Assessing open-source library viability
## When NOT to Use
- Trivial decisions between similar tools (use team preference)
- Mandated technology choices (decision already made)
- Emergency production issues (use monitoring tools)
More from alirezarezvani/claude-skills
- a11y-auditAccessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.
- ab-test-setupWhen the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.
- ad-creativeWhen the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.
- adversarial-reviewerAdversarial code review that breaks the self-review monoculture. Use when you want a genuinely critical review of recent changes, before merging a PR, or when you suspect Claude is being too agreeable about code quality. Forces perspective shifts through hostile reviewer personas that catch blind spots the author's mental model shares with the reviewer.
- aeoAnswer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.
- agent-designerUse when the user asks to design a multi-agent system, pick an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or evaluate agent execution logs for cost, latency, and failure bottlenecks. Examples: 'design an agent architecture for research automation', 'generate Anthropic tool schemas from these tool descriptions', 'analyze these agent run logs for bottlenecks'. NOT for Claude Code workflow files (use workflow-builder) or single-agent prompt design (use agent-workflow-designer).
- agent-protocolInter-agent communication protocol for C-suite agent teams. Defines invocation syntax, loop prevention, isolation rules, and response formats. Use when C-suite agents need to query each other, coordinate cross-functional analysis, or run board meetings with multiple agent roles.
- agent-workflow-designerDesign production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/context controls. Use when architecting a multi-step agent pipeline, choosing between single-agent vs multi-agent approaches, or refactoring an LLM workflow that suffers from context bloat or unreliable handoffs.
- agenthubMulti-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.
- agile-product-ownerAgile product ownership for backlog management and sprint execution. Covers user story writing, acceptance criteria, sprint planning, and velocity tracking. Use when writing user stories, creating acceptance criteria, planning sprints, estimating story points, breaking down epics, or prioritizing the backlog.