tech-stack-evaluator

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.

1,864 stars

Best use case

tech-stack-evaluator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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.

Teams using tech-stack-evaluator should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/tech-stack-evaluator/SKILL.md --create-dirs "https://raw.githubusercontent.com/LeoYeAI/openclaw-master-skills/main/skills/tech-stack-evaluator/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/tech-stack-evaluator/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How tech-stack-evaluator Compares

Feature / Agenttech-stack-evaluatorStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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)

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