tech-stack-evaluator by alirezarezvani/claude-skills
npx skills add https://github.com/alirezarezvani/claude-skills --skill tech-stack-evaluator通过数据驱动分析和可操作建议,评估和比较技术、框架及云服务提供商。
| 功能 | 描述 |
|---|---|
| 技术比较 | 使用加权评分比较框架和库 |
| 总拥有成本分析 | 计算包含隐性成本的 5 年总成本 |
| 生态系统健康度 | 评估 GitHub 指标、npm 采用率、社区强度 |
| 安全评估 | 评估漏洞和合规准备情况 |
| 迁移分析 | 估算迁移的工作量、风险和时间线 |
| 云服务比较 | 针对特定工作负载比较 AWS、Azure、GCP |
Compare React vs Vue for a SaaS dashboard.
Priorities: developer productivity (40%), ecosystem (30%), performance (30%).
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触达数万 AI 开发者,精准高效
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.
评估器接受三种输入格式:
文本 - 自然语言查询
Compare PostgreSQL vs MongoDB for our e-commerce platform.
YAML - 用于自动化的结构化输入
comparison:
technologies: ["React", "Vue"]
use_case: "SaaS dashboard"
weights:
ecosystem: 30
performance: 25
developer_experience: 45
JSON - 程序化集成
{
"technologies": ["React", "Vue"],
"use_case": "SaaS dashboard"
}
使用可定制的加权标准比较技术。
python scripts/stack_comparator.py --help
计算多年预测的总拥有成本。
python scripts/tco_calculator.py --input assets/sample_input_tco.json
从 GitHub、npm 和社区指标分析生态系统健康度。
python scripts/ecosystem_analyzer.py --technology react
评估安全状况和合规准备情况。
python scripts/security_assessor.py --technology express --compliance soc2,gdpr
估算迁移的复杂性、工作量和风险。
python scripts/migration_analyzer.py --from angular-1.x --to react
| 文档 | 内容 |
|---|---|
references/metrics.md | 详细的评分算法和计算公式 |
references/examples.md | 所有分析类型的输入/输出示例 |
references/workflows.md | 分步评估工作流程 |
| 级别 | 分数 | 解释 |
|---|---|---|
| 高 | 80-100% | 明确胜出,数据充分 |
| 中 | 50-79% | 存在权衡取舍,中等不确定性 |
| 低 | < 50% | 难分伯仲,数据有限 |
每周安装次数
207
代码仓库
GitHub 星标数
3.6K
首次出现
Jan 20, 2026
安全审计
安装于
claude-code171
opencode157
gemini-cli154
codex145
cursor132
github-copilot127
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 |
Weekly Installs
207
Repository
GitHub Stars
3.6K
First Seen
Jan 20, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykPass
Installed on
claude-code171
opencode157
gemini-cli154
codex145
cursor132
github-copilot127
Azure Data Explorer (Kusto) 查询技能:KQL数据分析、日志遥测与时间序列处理
133,300 周安装