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tech-stack-evaluator

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Technology Stack Evaluator

技术栈评估工具

A comprehensive evaluation framework for comparing technologies, frameworks, cloud providers, and complete technology stacks. Provides data-driven recommendations with TCO analysis, security assessment, ecosystem health scoring, and migration path analysis.
这是一个用于对比技术、框架、云服务商及完整技术栈的综合性评估框架。它基于数据提供推荐建议，包含TCO分析、安全评估、生态系统健康评分及迁移路径分析功能。

Capabilities

核心功能

This skill provides eight comprehensive evaluation capabilities:
  • Technology Comparison: Head-to-head comparisons of frameworks, languages, and tools (React vs Vue, PostgreSQL vs MongoDB, Node.js vs Python)
  • Stack Evaluation: Assess complete technology stacks for specific use cases (real-time collaboration, API-heavy SaaS, data-intensive platforms)
  • Maturity & Ecosystem Analysis: Evaluate community health, maintenance status, long-term viability, and ecosystem strength
  • Total Cost of Ownership (TCO): Calculate comprehensive costs including licensing, hosting, developer productivity, and scaling
  • Security & Compliance: Analyze vulnerabilities, compliance readiness (GDPR, SOC2, HIPAA), and security posture
  • Migration Path Analysis: Assess migration complexity, risks, timelines, and strategies from legacy to modern stacks
  • Cloud Provider Comparison: Compare AWS vs Azure vs GCP for specific workloads with cost and feature analysis
  • Decision Reports: Generate comprehensive decision matrices with pros/cons, confidence scores, and actionable recommendations
本工具提供八项全面的评估能力：
  • 技术对比：框架、语言与工具的一对一对比（React vs Vue、PostgreSQL vs MongoDB、Node.js vs Python）
  • 栈评估：针对特定用例评估完整技术栈（实时协作、重度API的SaaS、数据密集型平台）
  • 成熟度与生态系统分析：评估社区活跃度、维护状态、长期生存能力及生态系统强度
  • 总拥有成本（TCO）：计算包含授权、托管、开发者生产力及扩展在内的综合成本
  • 安全与合规性：分析漏洞、合规准备情况（GDPR、SOC2、HIPAA）及安全态势
  • 迁移路径分析：评估从旧技术栈迁移到现代技术栈的复杂度、风险、时间线及策略
  • 云服务商对比：针对特定工作负载对比AWS、Azure与GCP，包含成本与功能分析
  • 决策报告：生成包含优缺点、置信度评分及可执行建议的综合决策矩阵

Input Requirements

输入要求

Flexible Input Formats (Automatic Detection)

灵活的输入格式（自动检测）

The skill automatically detects and processes multiple input formats:
Text/Conversational:
"Compare React vs Vue for building a SaaS dashboard"
"Evaluate technology stack for real-time collaboration platform"
"Should we migrate from MongoDB to PostgreSQL?"
Structured (YAML):
yaml
comparison:
  technologies:
    - name: "React"
    - name: "Vue"
  use_case: "SaaS dashboard"
  priorities:
    - "Developer productivity"
    - "Ecosystem maturity"
    - "Performance"
Structured (JSON):
json
{
  "comparison": {
    "technologies": ["React", "Vue"],
    "use_case": "SaaS dashboard",
    "priorities": ["Developer productivity", "Ecosystem maturity"]
  }
}
URLs for Ecosystem Analysis:
  • GitHub repository URLs (for health scoring)
  • npm package URLs (for download statistics)
  • Technology documentation URLs (for feature extraction)
本工具可自动检测并处理多种输入格式：
文本/对话式:
"Compare React vs Vue for building a SaaS dashboard"
"Evaluate technology stack for real-time collaboration platform"
"Should we migrate from MongoDB to PostgreSQL?"
结构化（YAML）:
yaml
comparison:
  technologies:
    - name: "React"
    - name: "Vue"
  use_case: "SaaS dashboard"
  priorities:
    - "Developer productivity"
    - "Ecosystem maturity"
    - "Performance"
结构化（JSON）:
json
{
  "comparison": {
    "technologies": ["React", "Vue"],
    "use_case": "SaaS dashboard",
    "priorities": ["Developer productivity", "Ecosystem maturity"]
  }
}
用于生态系统分析的URL:
  • GitHub仓库URL（用于健康评分）
  • npm包URL（用于下载统计）
  • 技术文档URL（用于功能提取）

Analysis Scope Selection

分析范围选择

Users can select which analyses to run:
  • Quick Comparison: Basic scoring and comparison (200-300 tokens)
  • Standard Analysis: Scoring + TCO + Security (500-800 tokens)
  • Comprehensive Report: All analyses including migration paths (1200-1500 tokens)
  • Custom: User selects specific sections (modular)
用户可选择要运行的分析类型：
  • 快速对比：基础评分与对比（200-300词）
  • 标准分析：评分 + TCO + 安全分析（500-800词）
  • 综合报告：包含迁移路径在内的所有分析（1200-1500词）
  • 自定义：用户选择特定模块（模块化）

Output Formats

输出格式

Context-Aware Output

上下文感知输出

The skill automatically adapts output based on environment:
Claude Desktop (Rich Markdown):
  • Formatted tables with color indicators
  • Expandable sections for detailed analysis
  • Visual decision matrices
  • Charts and graphs (when appropriate)
CLI/Terminal (Terminal-Friendly):
  • Plain text tables with ASCII borders
  • Compact formatting
  • Clear section headers
  • Copy-paste friendly code blocks
本工具会根据环境自动调整输出格式：
Claude桌面端（富文本Markdown）:
  • 带颜色标识的格式化表格
  • 可展开的详细分析章节
  • 可视化决策矩阵
  • 图表（适用时）
CLI/终端（终端友好型）:
  • 带ASCII边框的纯文本表格
  • 紧凑格式
  • 清晰的章节标题
  • 便于复制粘贴的代码块

Progressive Disclosure Structure

渐进式披露结构

Executive Summary (200-300 tokens):
  • Recommendation summary
  • Top 3 pros and cons
  • Confidence level (High/Medium/Low)
  • Key decision factors
Detailed Breakdown (on-demand):
  • Complete scoring matrices
  • Detailed TCO calculations
  • Full security analysis
  • Migration complexity assessment
  • All supporting data and calculations
执行摘要（200-300词）:
  • 建议摘要
  • 前3项优缺点
  • 置信度等级（高/中/低）
  • 关键决策因素
详细分解（按需查看）:
  • 完整评分矩阵
  • 详细TCO计算
  • 全面安全分析
  • 迁移复杂度评估
  • 所有支撑数据与计算过程

Report Sections (User-Selectable)

报告章节（用户可选）

Users choose which sections to include:
  1. Scoring & Comparison Matrix
    • Weighted decision scores
    • Head-to-head comparison tables
    • Strengths and weaknesses
  2. Financial Analysis
    • TCO breakdown (5-year projection)
    • ROI analysis
    • Cost per user/request metrics
    • Hidden cost identification
  3. Ecosystem Health
    • Community size and activity
    • GitHub stars, npm downloads
    • Release frequency and maintenance
    • Issue response times
    • Viability assessment
  4. Security & Compliance
    • Vulnerability count (CVE database)
    • Security patch frequency
    • Compliance readiness (GDPR, SOC2, HIPAA)
    • Security scoring
  5. Migration Analysis (when applicable)
    • Migration complexity scoring
    • Code change estimates
    • Data migration requirements
    • Downtime assessment
    • Risk mitigation strategies
  6. Performance Benchmarks
    • Throughput/latency comparisons
    • Resource usage analysis
    • Scalability characteristics
用户可选择要包含的章节：
  1. 评分与对比矩阵
    • 加权决策评分
    • 一对一对比表格
    • 优势与劣势
  2. 财务分析
    • TCO细分（5年预测）
    • ROI分析
    • 每用户/每请求成本指标
    • 隐性成本识别
  3. 生态系统健康度
    • 社区规模与活跃度
    • GitHub星标数、npm下载量
    • 发布频率与维护情况
    • 问题响应时间
    • 生存能力评估
  4. 安全与合规性
    • 漏洞数量（基于CVE数据库）
    • 安全补丁频率
    • 合规准备情况（GDPR、SOC2、HIPAA）
    • 安全评分
  5. 迁移分析（适用时）
    • 迁移复杂度评分
    • 代码变更预估
    • 数据迁移要求
    • 停机时间评估
    • 风险缓解策略
  6. 性能基准
    • 吞吐量/延迟对比
    • 资源使用分析
    • 可扩展性特征

How to Use

使用方法

Basic Invocations

基础调用

Quick Comparison:
"Compare React vs Vue for our SaaS dashboard project"
"PostgreSQL vs MongoDB for our application"
Stack Evaluation:
"Evaluate technology stack for real-time collaboration platform:
Node.js, WebSockets, Redis, PostgreSQL"
TCO Analysis:
"Calculate total cost of ownership for AWS vs Azure for our workload:
- 50 EC2/VM instances
- 10TB storage
- High bandwidth requirements"
Security Assessment:
"Analyze security posture of our current stack:
Express.js, MongoDB, JWT authentication.
Need SOC2 compliance."
Migration Path:
"Assess migration from Angular.js (1.x) to React.
Application has 50,000 lines of code, 200 components."
快速对比:
"Compare React vs Vue for our SaaS dashboard project"
"PostgreSQL vs MongoDB for our application"
栈评估:
"Evaluate technology stack for real-time collaboration platform:
Node.js, WebSockets, Redis, PostgreSQL"
TCO分析:
"Calculate total cost of ownership for AWS vs Azure for our workload:
- 50 EC2/VM instances
- 10TB storage
- High bandwidth requirements"
安全评估:
"Analyze security posture of our current stack:
Express.js, MongoDB, JWT authentication.
Need SOC2 compliance."
迁移路径:
"Assess migration from Angular.js (1.x) to React.
Application has 50,000 lines of code, 200 components."

Advanced Invocations

高级调用

Custom Analysis Sections:
"Compare Next.js vs Nuxt.js.
Include: Ecosystem health, TCO, and performance benchmarks.
Skip: Migration analysis, compliance."
Weighted Decision Criteria:
"Compare cloud providers for ML workloads.
Priorities (weighted):
- GPU availability (40%)
- Cost (30%)
- Ecosystem (20%)
- Support (10%)"
Multi-Technology Comparison:
"Compare: React, Vue, Svelte, Angular for enterprise SaaS.
Use case: Large team (20+ developers), complex state management.
Generate comprehensive decision matrix."
自定义分析章节:
"Compare Next.js vs Nuxt.js.
Include: Ecosystem health, TCO, and performance benchmarks.
Skip: Migration analysis, compliance."
加权决策标准:
"Compare cloud providers for ML workloads.
Priorities (weighted):
- GPU availability (40%)
- Cost (30%)
- Ecosystem (20%)
- Support (10%)"
多技术对比:
"Compare: React, Vue, Svelte, Angular for enterprise SaaS.
Use case: Large team (20+ developers), complex state management.
Generate comprehensive decision matrix."

Scripts

脚本模块

Core Modules

核心模块

  • stack_comparator.py
    : Main comparison engine with weighted scoring algorithms
  • tco_calculator.py
    : Total Cost of Ownership calculations (licensing, hosting, developer productivity, scaling)
  • ecosystem_analyzer.py
    : Community health scoring, GitHub/npm metrics, viability assessment
  • security_assessor.py
    : Vulnerability analysis, compliance readiness, security scoring
  • migration_analyzer.py
    : Migration complexity scoring, risk assessment, effort estimation
  • format_detector.py
    : Automatic input format detection (text, YAML, JSON, URLs)
  • report_generator.py
    : Context-aware report generation with progressive disclosure
  • stack_comparator.py
    : 带有加权评分算法的主对比引擎
  • tco_calculator.py
    : 总拥有成本计算（授权、托管、开发者生产力、扩展）
  • ecosystem_analyzer.py
    : 社区健康评分、GitHub/npm指标、生存能力评估
  • security_assessor.py
    : 漏洞分析、合规准备情况、安全评分
  • migration_analyzer.py
    : 迁移复杂度评分、风险评估、工作量预估
  • format_detector.py
    : 自动输入格式检测（文本、YAML、JSON、URL）
  • report_generator.py
    : 带渐进式披露的上下文感知报告生成器

Utility Modules

工具模块

  • data_fetcher.py
    : Fetch real-time data from GitHub, npm, CVE databases
  • benchmark_processor.py
    : Process and normalize performance benchmark data
  • confidence_scorer.py
    : Calculate confidence levels for recommendations
  • data_fetcher.py
    : 从GitHub、npm、CVE数据库获取实时数据
  • benchmark_processor.py
    : 处理与标准化性能基准数据
  • confidence_scorer.py
    : 计算建议的置信度等级

Metrics and Calculations

指标与计算

1. Scoring & Comparison Metrics

1. 评分与对比指标

Technology Comparison Matrix:
  • Feature completeness (0-100 scale)
  • Learning curve assessment (Easy/Medium/Hard)
  • Developer experience scoring
  • Documentation quality (0-10 scale)
  • Weighted total scores
Decision Scoring Algorithm:
  • User-defined weights for criteria
  • Normalized scoring (0-100)
  • Confidence intervals
  • Sensitivity analysis
技术对比矩阵:
  • 功能完整性（0-100分）
  • 学习曲线评估（简单/中等/困难）
  • 开发者体验评分
  • 文档质量（0-10分）
  • 加权总分
决策评分算法:
  • 用户定义的标准权重
  • 标准化评分（0-100）
  • 置信区间
  • 敏感性分析

2. Financial Calculations

2. 财务计算

TCO Components:
  • Initial Costs: Licensing, training, migration
  • Operational Costs: Hosting, support, maintenance (monthly/yearly)
  • Scaling Costs: Per-user costs, infrastructure scaling projections
  • Developer Productivity: Time-to-market impact, development speed multipliers
  • Hidden Costs: Technical debt, vendor lock-in risks
ROI Calculations:
  • Cost savings projections (3-year, 5-year)
  • Productivity gains (developer hours saved)
  • Break-even analysis
  • Risk-adjusted returns
Cost Per Metric:
  • Cost per user (monthly/yearly)
  • Cost per API request
  • Cost per GB stored/transferred
  • Cost per compute hour
TCO组成部分:
  • 初始成本: 授权、培训、迁移
  • 运营成本: 托管、支持、维护（月度/年度）
  • 扩展成本: 每用户成本、基础设施扩展预测
  • 开发者生产力: 上市时间影响、开发速度乘数
  • 隐性成本: 技术债务、供应商锁定风险
ROI计算:
  • 成本节约预测（3年、5年）
  • 生产力提升（节省的开发者工时）
  • 收支平衡分析
  • 风险调整后收益
单位成本指标:
  • 每用户成本（月度/年度）
  • 每API请求成本
  • 每GB存储/传输成本
  • 每计算小时成本

3. Maturity & Ecosystem Metrics

3. 成熟度与生态系统指标

Health Scoring (0-100 scale):
  • GitHub Metrics: Stars, forks, contributors, commit frequency
  • npm Metrics: Weekly downloads, version stability, dependency count
  • Release Cadence: Regular releases, semantic versioning adherence
  • Issue Management: Response time, resolution rate, open vs closed issues
Community Metrics:
  • Active maintainers count
  • Contributor growth rate
  • Stack Overflow question volume
  • Job market demand (job postings analysis)
Viability Assessment:
  • Corporate backing strength
  • Community sustainability
  • Alternative availability
  • Long-term risk scoring
健康评分（0-100分）:
  • GitHub指标: 星标数、复刻数、贡献者数、提交频率
  • npm指标: 周下载量、版本稳定性、依赖数量
  • 发布节奏: 定期发布、语义化版本遵循情况
  • 问题管理: 响应时间、解决率、已关闭vs未关闭问题
社区指标:
  • 活跃维护者数量
  • 贡献者增长率
  • Stack Overflow问题量
  • 就业市场需求（招聘信息分析）
生存能力评估:
  • 企业支持力度
  • 社区可持续性
  • 替代方案可用性
  • 长期风险评分

4. Security & Compliance Metrics

4. 安全与合规性指标

Security Scoring:
  • CVE Count: Known vulnerabilities (last 12 months, last 3 years)
  • Severity Distribution: Critical/High/Medium/Low vulnerability counts
  • Patch Frequency: Average time to patch (days)
  • Security Track Record: Historical security posture
Compliance Readiness:
  • GDPR: Data privacy features, consent management, data portability
  • SOC2: Access controls, encryption, audit logging
  • HIPAA: PHI handling, encryption standards, access controls
  • PCI-DSS: Payment data security (if applicable)
Compliance Scoring (per standard):
  • Ready: 90-100% compliant
  • Mostly Ready: 70-89% (minor gaps)
  • Partial: 50-69% (significant work needed)
  • Not Ready: <50% (major gaps)
安全评分:
  • CVE数量: 已知漏洞（过去12个月、过去3年）
  • 严重程度分布: 关键/高/中/低漏洞数量
  • 补丁频率: 平均补丁修复时间（天）
  • 安全记录: 历史安全态势
合规准备情况:
  • GDPR: 数据隐私功能、同意管理、数据可移植性
  • SOC2: 访问控制、加密、审计日志
  • HIPAA: PHI处理、加密标准、访问控制
  • PCI-DSS: 支付数据安全（适用时）
合规评分（按标准）:
  • 已就绪: 90-100%合规
  • 基本就绪: 70-89%（微小差距）
  • 部分就绪: 50-69%（需大量工作）
  • 未就绪: <50%（重大差距）

5. Migration Analysis Metrics

5. 迁移分析指标

Complexity Scoring (1-10 scale):
  • Code Changes: Estimated lines of code affected
  • Architecture Impact: Breaking changes, API compatibility
  • Data Migration: Schema changes, data transformation complexity
  • Downtime Requirements: Zero-downtime possible vs planned outage
Effort Estimation:
  • Development hours (by component)
  • Testing hours
  • Training hours
  • Total person-months
Risk Assessment:
  • Technical Risks: API incompatibilities, performance regressions
  • Business Risks: Downtime impact, feature parity gaps
  • Team Risks: Learning curve, skill gaps
  • Mitigation Strategies: Risk-specific recommendations
Migration Phases:
  • Phase 1: Planning and prototyping (timeline, effort)
  • Phase 2: Core migration (timeline, effort)
  • Phase 3: Testing and validation (timeline, effort)
  • Phase 4: Deployment and monitoring (timeline, effort)
复杂度评分（1-10分）:
  • 代码变更: 预估受影响的代码行数
  • 架构影响: 破坏性变更、API兼容性
  • 数据迁移: schema变更、数据转换复杂度
  • 停机要求: 是否支持零停机 vs 计划内停机
工作量预估:
  • 开发工时（按组件）
  • 测试工时
  • 培训工时
  • 总人月数
风险评估:
  • 技术风险: API不兼容、性能退化
  • 业务风险: 停机影响、功能一致性差距
  • 团队风险: 学习曲线、技能差距
  • 缓解策略: 针对特定风险的建议
迁移阶段:
  • 阶段1: 规划与原型设计（时间线、工作量）
  • 阶段2: 核心迁移（时间线、工作量）
  • 阶段3: 测试与验证（时间线、工作量）
  • 阶段4: 部署与监控（时间线、工作量）

6. Performance Benchmark Metrics

6. 性能基准指标

Throughput/Latency:
  • Requests per second (RPS)
  • Average response time (ms)
  • P95/P99 latency percentiles
  • Concurrent user capacity
Resource Usage:
  • Memory consumption (MB/GB)
  • CPU utilization (%)
  • Storage requirements
  • Network bandwidth
Scalability Characteristics:
  • Horizontal scaling efficiency
  • Vertical scaling limits
  • Cost per performance unit
  • Scaling inflection points
吞吐量/延迟:
  • 每秒请求数（RPS）
  • 平均响应时间（毫秒）
  • P95/P99延迟百分位
  • 并发用户容量
资源使用:
  • 内存消耗（MB/GB）
  • CPU利用率（%）
  • 存储需求
  • 网络带宽
可扩展性特征:
  • 水平扩展效率
  • 垂直扩展限制
  • 每性能单位成本
  • 扩展拐点

Best Practices

最佳实践

For Accurate Evaluations

确保评估准确

  1. Define Clear Use Case: Specify exact requirements, constraints, and priorities
  2. Provide Complete Context: Team size, existing stack, timeline, budget constraints
  3. Set Realistic Priorities: Use weighted criteria (total = 100%) for multi-factor decisions
  4. Consider Team Skills: Factor in learning curve and existing expertise
  5. Think Long-Term: Evaluate 3-5 year outlook, not just immediate needs
  1. 明确定义用例: 详细说明需求、约束与优先级
  2. 提供完整上下文: 团队规模、现有技术栈、时间线、预算约束
  3. 设置现实优先级: 对多因素决策使用加权标准（总分=100%）
  4. 考虑团队技能: 纳入学习曲线与现有专业知识
  5. 着眼长期: 评估3-5年前景，而非仅眼前需求

For TCO Analysis

TCO分析最佳实践

  1. Include All Cost Components: Don't forget training, migration, technical debt
  2. Use Realistic Scaling Projections: Base on actual growth metrics, not wishful thinking
  3. Account for Developer Productivity: Time-to-market and development speed are critical costs
  4. Consider Hidden Costs: Vendor lock-in, exit costs, technical debt accumulation
  5. Validate Assumptions: Document all TCO assumptions for review
  1. 包含所有成本组成: 不要忘记培训、迁移、技术债务
  2. 使用现实的扩展预测: 基于实际增长指标，而非主观愿望
  3. 考虑开发者生产力: 上市时间与开发速度是关键成本因素
  4. 关注隐性成本: 供应商锁定、退出成本、技术债务累积
  5. 验证假设: 记录所有TCO假设以供审核

For Migration Decisions

迁移决策最佳实践

  1. Start with Risk Assessment: Identify showstoppers early
  2. Plan Incremental Migration: Avoid big-bang rewrites when possible
  3. Prototype Critical Paths: Test complex migration scenarios before committing
  4. Build Rollback Plans: Always have a fallback strategy
  5. Measure Baseline Performance: Establish current metrics before migration
  1. 从风险评估开始: 尽早识别关键障碍
  2. 规划增量迁移: 尽可能避免大规模重写
  3. 原型化关键路径: 在投入前测试复杂迁移场景
  4. 制定回滚计划: 始终要有 fallback 策略
  5. 测量基线性能: 迁移前建立当前指标

For Security Evaluation

安全评估最佳实践

  1. Check Recent Vulnerabilities: Focus on last 12 months for current security posture
  2. Review Patch Response Time: Fast patching is more important than zero vulnerabilities
  3. Validate Compliance Claims: Vendor claims ≠ actual compliance readiness
  4. Consider Supply Chain: Evaluate security of all dependencies
  5. Test Security Features: Don't assume features work as documented
  1. 检查近期漏洞: 关注过去12个月的当前安全态势
  2. 审查补丁响应时间: 快速补丁修复比零漏洞更重要
  3. 验证合规声明: 供应商声明 ≠ 实际合规准备情况
  4. 考虑供应链: 评估所有依赖项的安全性
  5. 测试安全功能: 不要假设功能如文档所述正常工作

Limitations

局限性

Data Accuracy

数据准确性

  • Ecosystem metrics are point-in-time snapshots (GitHub stars, npm downloads change rapidly)
  • TCO calculations are estimates based on provided assumptions and market rates
  • Benchmark data may not reflect your specific use case or configuration
  • Security vulnerability counts depend on public CVE database completeness
  • 生态系统指标是时间点快照（GitHub星标、npm下载量变化迅速）
  • TCO计算是基于提供的假设与市场费率的估算
  • 基准数据可能无法反映你的特定用例或配置
  • 安全漏洞数量取决于公开CVE数据库的完整性

Scope Boundaries

范围边界

  • Industry-Specific Requirements: Some specialized industries may have unique constraints not covered by standard analysis
  • Emerging Technologies: Very new technologies (<1 year old) may lack sufficient data for accurate assessment
  • Custom/Proprietary Solutions: Cannot evaluate closed-source or internal tools without data
  • Political/Organizational Factors: Cannot account for company politics, vendor relationships, or legacy commitments
  • 行业特定需求: 某些特殊行业可能有标准分析未覆盖的独特约束
  • 新兴技术: 非常新的技术（<1年）可能缺乏足够数据进行准确评估
  • 自定义/专有解决方案: 若无数据，无法评估闭源或内部工具
  • 政治/组织因素: 无法考虑公司政治、供应商关系或遗留承诺

Contextual Limitations

上下文局限性

  • Team Skill Assessment: Cannot directly evaluate your team's specific skills and learning capacity
  • Existing Architecture: Recommendations assume greenfield unless migration context provided
  • Budget Constraints: TCO analysis provides costs but cannot make budget decisions for you
  • Timeline Pressure: Cannot account for business deadlines and time-to-market urgency
  • 团队技能评估: 无法直接评估你的团队的特定技能与学习能力
  • 现有架构: 除非提供迁移上下文，否则建议假设为全新项目
  • 预算约束: TCO分析提供成本数据，但无法为你做出预算决策
  • 时间线压力: 无法考虑业务截止日期与上市时间紧迫性

When NOT to Use This Skill

不适用场景

  • Trivial Decisions: Choosing between nearly-identical tools (use team preference)
  • Mandated Solutions: When technology choice is already decided by management/policy
  • Insufficient Context: When you don't know your requirements, priorities, or constraints
  • Real-Time Production Decisions: Use for planning, not emergency production issues
  • Non-Technical Decisions: Business strategy, hiring, organizational issues
  • 琐碎决策: 选择几乎相同的工具（使用团队偏好）
  • 强制解决方案: 技术选择已由管理层/政策决定
  • 上下文不足: 不清楚自身需求、优先级或约束
  • 实时生产决策: 用于规划，而非紧急生产问题
  • 非技术决策: 业务战略、招聘、组织问题

Confidence Levels

置信度等级

The skill provides confidence scores with all recommendations:
  • High Confidence (80-100%): Strong data, clear winner, low risk
  • Medium Confidence (50-79%): Good data, trade-offs present, moderate risk
  • Low Confidence (<50%): Limited data, close call, high uncertainty
  • Insufficient Data: Cannot make recommendation without more information
Confidence is based on:
  • Data completeness and recency
  • Consensus across multiple metrics
  • Clarity of use case requirements
  • Industry maturity and standards
本工具会为所有建议提供置信度评分：
  • 高置信度（80-100%）: 数据充分，结果明确，风险低
  • 中置信度（50-79%）: 数据良好，存在权衡，风险中等
  • 低置信度（<50%）: 数据有限，结果接近，不确定性高
  • 数据不足: 若无更多信息，无法给出建议
置信度基于以下因素：
  • 数据完整性与时效性
  • 多指标共识
  • 用例需求清晰度
  • 行业成熟度与标准