Restructure skill to follow Progressive Disclosure Architecture: Structure Changes: - Move Python scripts to scripts/ directory - Move sample JSON files to assets/ directory - Create references/ directory with extracted content - Remove redundant HOW_TO_USE.md and README.md New Reference Files: - references/metrics.md: Detailed scoring algorithms and formulas - references/examples.md: Concrete input/output examples - references/workflows.md: Step-by-step evaluation workflows SKILL.md Improvements: - Reduced from 430 lines to ~180 lines - Added table of contents - Added trigger phrases in description - Consistent imperative voice - Points to references for details Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Alireza Rezvani
GitHub
383
engineering-team/tech-stack-evaluator/references/examples.md
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# Technology Evaluation Examples
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Concrete examples showing input formats and expected outputs.
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---
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## Table of Contents
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- [Quick Comparison Example](#quick-comparison-example)
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- [TCO Analysis Example](#tco-analysis-example)
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- [Ecosystem Analysis Example](#ecosystem-analysis-example)
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- [Migration Assessment Example](#migration-assessment-example)
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- [Multi-Technology Comparison](#multi-technology-comparison)
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---
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## Quick Comparison Example
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### Input (Text Format)
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```
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Compare React vs Vue for building a SaaS dashboard.
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Focus on: developer productivity, ecosystem maturity, performance.
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```
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### Output
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```
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TECHNOLOGY COMPARISON: React vs Vue for SaaS Dashboard
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=======================================================
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RECOMMENDATION: React
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Confidence: 78% (Medium-High)
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COMPARISON MATRIX
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-----------------
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| Category | Weight | React | Vue |
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|----------------------|--------|-------|------|
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| Performance | 15% | 82.0 | 85.0 |
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| Scalability | 15% | 88.0 | 80.0 |
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| Developer Experience | 20% | 85.0 | 90.0 |
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| Ecosystem | 15% | 92.0 | 78.0 |
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| Learning Curve | 10% | 70.0 | 85.0 |
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| Documentation | 10% | 88.0 | 82.0 |
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| Community Support | 10% | 90.0 | 75.0 |
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| Enterprise Readiness | 5% | 85.0 | 72.0 |
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|----------------------|--------|-------|------|
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| WEIGHTED TOTAL | 100% | 85.2 | 81.1 |
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KEY DECISION FACTORS
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--------------------
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1. Ecosystem (15%): React leads with 92.0 - larger npm ecosystem
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2. Developer Experience (20%): Vue leads with 90.0 - gentler learning curve
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3. Community Support (10%): React leads with 90.0 - more Stack Overflow resources
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PROS/CONS SUMMARY
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-----------------
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React:
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✓ Excellent ecosystem (92.0/100)
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✓ Strong community support (90.0/100)
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✓ Excellent scalability (88.0/100)
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✗ Steeper learning curve (70.0/100)
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Vue:
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✓ Excellent developer experience (90.0/100)
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✓ Good performance (85.0/100)
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✓ Easier learning curve (85.0/100)
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✗ Smaller enterprise presence (72.0/100)
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```
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---
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## TCO Analysis Example
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### Input (JSON Format)
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```json
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{
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"technology": "Next.js on Vercel",
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"team_size": 8,
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"timeline_years": 5,
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"initial_costs": {
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"licensing": 0,
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"training_hours_per_dev": 24,
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"developer_hourly_rate": 85,
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"migration": 15000,
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"setup": 5000
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},
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"operational_costs": {
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"monthly_hosting": 2500,
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"annual_support": 0,
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"maintenance_hours_per_dev_monthly": 16
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},
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"scaling_params": {
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"initial_users": 5000,
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"annual_growth_rate": 0.40,
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"initial_servers": 3,
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"cost_per_server_monthly": 150
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}
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}
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```
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### Output
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```
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TCO ANALYSIS: Next.js on Vercel (5-Year Projection)
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====================================================
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EXECUTIVE SUMMARY
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-----------------
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Total TCO: $1,247,320
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Net TCO (after productivity gains): $987,320
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Average Yearly Cost: $249,464
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INITIAL COSTS (One-Time)
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------------------------
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| Component | Cost |
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|----------------|-----------|
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| Licensing | $0 |
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| Training | $16,820 |
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| Migration | $15,000 |
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| Setup | $5,000 |
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|----------------|-----------|
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| TOTAL INITIAL | $36,820 |
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OPERATIONAL COSTS (Per Year)
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----------------------------
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| Year | Hosting | Maintenance | Total |
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|------|----------|-------------|-----------|
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| 1 | $30,000 | $130,560 | $160,560 |
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| 2 | $42,000 | $130,560 | $172,560 |
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| 3 | $58,800 | $130,560 | $189,360 |
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| 4 | $82,320 | $130,560 | $212,880 |
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| 5 | $115,248 | $130,560 | $245,808 |
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SCALING ANALYSIS
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----------------
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User Projections: 5,000 → 7,000 → 9,800 → 13,720 → 19,208
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Cost per User: $32.11 → $24.65 → $19.32 → $15.52 → $12.79
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Scaling Efficiency: Excellent - economies of scale achieved
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KEY COST DRIVERS
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----------------
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1. Developer maintenance time ($652,800 over 5 years)
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2. Infrastructure/hosting ($328,368 over 5 years)
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OPTIMIZATION OPPORTUNITIES
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--------------------------
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• Consider automation to reduce maintenance hours
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• Evaluate reserved capacity pricing for hosting
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```
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---
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## Ecosystem Analysis Example
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### Input
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```yaml
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technology: "Svelte"
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github:
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stars: 78000
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forks: 4100
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contributors: 680
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commits_last_month: 45
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avg_issue_response_hours: 36
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issue_resolution_rate: 0.72
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releases_per_year: 8
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active_maintainers: 5
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npm:
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weekly_downloads: 420000
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version: "4.2.8"
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dependencies_count: 0
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days_since_last_publish: 21
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community:
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stackoverflow_questions: 8500
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job_postings: 1200
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tutorials_count: 350
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forum_members: 25000
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corporate_backing:
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type: "community_led"
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funding_millions: 0
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```
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### Output
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```
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ECOSYSTEM ANALYSIS: Svelte
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==========================
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OVERALL HEALTH SCORE: 71.5/100
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HEALTH SCORE BREAKDOWN
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----------------------
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| Category | Score | Assessment |
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|--------------------|-------|-------------------|
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| GitHub Health | 78.0 | Strong |
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| npm Health | 72.0 | Good |
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| Community Health | 58.0 | Moderate |
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| Corporate Backing | 40.0 | Community-led |
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| Maintenance Health | 75.0 | Active |
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VIABILITY ASSESSMENT
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--------------------
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Overall: Good - Solid viability with minor concerns
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Risk Level: Low-Medium
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Strengths:
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• Strong GitHub presence with active community
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• Active maintenance with responsive issue resolution
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• High npm adoption with stable releases
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Risks:
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• Weak corporate backing - sustainability concerns
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• Smaller community - limited enterprise resources
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RECOMMENDATION
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--------------
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Suitable for adoption - monitor corporate backing situation.
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Consider alternative with stronger backing for mission-critical enterprise apps.
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KEY METRICS
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-----------
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GitHub: 78,000 stars, 680 contributors, 45 commits/month
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npm: 420,000 weekly downloads, v4.2.8
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Community: 8,500 SO questions, 1,200 job postings
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```
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---
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## Migration Assessment Example
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### Input
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```
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Assess migration from Angular.js (1.x) to React.
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Application: 50,000 lines of code, 200 components.
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Team: 6 developers, 2 have React experience.
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Timeline: Need completion within 8 months.
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```
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### Output
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```
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MIGRATION ASSESSMENT: Angular.js 1.x → React
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=============================================
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COMPLEXITY SCORE: 7.8/10 (High)
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COMPLEXITY BREAKDOWN
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--------------------
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| Factor | Score | Impact |
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|---------------------|-------|-------------|
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| Code Changes | 8/10 | 50K LOC |
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| Architecture Impact | 9/10 | Complete |
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| Data Migration | 5/10 | State only |
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| Downtime Risk | 6/10 | Mitigatable |
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EFFORT ESTIMATION
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-----------------
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| Phase | Hours | Duration |
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|--------------------------|--------|----------|
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| Planning & Prototyping | 240 | 3 weeks |
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| Core Migration | 2,400 | 16 weeks |
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| Testing & Validation | 480 | 5 weeks |
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| Deployment & Monitoring | 160 | 2 weeks |
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|--------------------------|--------|----------|
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| TOTAL | 3,280 | 26 weeks |
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Timeline Assessment: 26 weeks exceeds 8-month target (32 weeks)
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Recommendation: Feasible with buffer
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RISK ASSESSMENT
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---------------
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Technical Risks:
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• High - Two-way binding to unidirectional data flow
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• Medium - Dependency injection to hooks/context
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• Medium - Performance regression during transition
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Business Risks:
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• Medium - Feature freeze during migration
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• Low - Customer impact with phased approach
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Team Risks:
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• High - 4 of 6 developers need React training
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• Medium - Learning curve affects velocity initially
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MIGRATION STRATEGY
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------------------
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Recommended Approach: Strangler Fig Pattern
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Phase 1 (Weeks 1-8): Strangler setup
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• Create React shell application
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• Set up micro-frontend routing
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• Migrate shared utilities
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Phase 2 (Weeks 9-18): Component migration
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• Migrate components by feature area
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• Maintain Angular.js for untouched areas
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• Continuous integration testing
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Phase 3 (Weeks 19-24): Completion
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• Migrate remaining components
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• Remove Angular.js dependencies
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• Performance optimization
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Phase 4 (Weeks 25-26): Stabilization
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• Production deployment
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• Monitoring and fixes
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```
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---
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## Multi-Technology Comparison
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### Input
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```
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Compare: React, Vue, Svelte, Angular for enterprise SaaS.
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Use case: Large team (20+ developers), complex state management.
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Generate comprehensive decision matrix.
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```
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### Output
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```
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MULTI-TECHNOLOGY COMPARISON: Enterprise SaaS Platform
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======================================================
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RECOMMENDATION: React
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Confidence: 72% (Medium)
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Runner-up: Angular (close second for enterprise)
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DECISION MATRIX
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---------------
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| Category | Wt | React | Vue | Svelte | Angular |
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|----------------------|------|-------|------|--------|---------|
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| Performance | 15% | 82 | 85 | 95 | 78 |
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| Scalability | 15% | 90 | 82 | 75 | 92 |
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| Developer Experience | 20% | 85 | 90 | 88 | 75 |
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| Ecosystem | 15% | 95 | 80 | 65 | 88 |
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| Learning Curve | 10% | 70 | 85 | 80 | 60 |
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| Documentation | 10% | 90 | 85 | 75 | 92 |
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| Community Support | 10% | 92 | 78 | 55 | 85 |
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| Enterprise Readiness | 5% | 88 | 72 | 50 | 95 |
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|----------------------|------|-------|------|--------|---------|
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| WEIGHTED TOTAL | 100% | 86.3 | 83.1 | 76.2 | 83.0 |
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FRAMEWORK PROFILES
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------------------
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React: Best for large ecosystem, hiring pool
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Angular: Best for enterprise structure, TypeScript-first
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Vue: Best for developer experience, gradual adoption
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Svelte: Best for performance, smaller bundles
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RECOMMENDATION RATIONALE
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------------------------
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For 20+ developer team with complex state management:
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1. React (Recommended)
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• Largest talent pool for hiring
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• Extensive enterprise libraries (Redux, React Query)
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• Meta backing ensures long-term support
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• Most Stack Overflow resources
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2. Angular (Strong Alternative)
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• Built-in structure for large teams
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• TypeScript-first reduces bugs
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• Comprehensive CLI and tooling
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• Google enterprise backing
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3. Vue (Consider for DX)
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• Excellent documentation
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• Easier onboarding
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• Growing enterprise adoption
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• Consider if DX is top priority
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4. Svelte (Not Recommended for This Use Case)
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• Smaller ecosystem for enterprise
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• Limited hiring pool
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• State management options less mature
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• Better for smaller teams/projects
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```
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242
engineering-team/tech-stack-evaluator/references/metrics.md
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242
engineering-team/tech-stack-evaluator/references/metrics.md
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# Technology Evaluation Metrics
|
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Detailed metrics and calculations used in technology stack evaluation.
|
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|
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---
|
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|
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## Table of Contents
|
||||
|
||||
- [Scoring and Comparison](#scoring-and-comparison)
|
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- [Financial Calculations](#financial-calculations)
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- [Ecosystem Health Metrics](#ecosystem-health-metrics)
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- [Security Metrics](#security-metrics)
|
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- [Migration Metrics](#migration-metrics)
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- [Performance Benchmarks](#performance-benchmarks)
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|
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---
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## Scoring and Comparison
|
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|
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### Technology Comparison Matrix
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|
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| Metric | Scale | Description |
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|--------|-------|-------------|
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| Feature Completeness | 0-100 | Coverage of required features |
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| Learning Curve | Easy/Medium/Hard | Time to developer proficiency |
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| Developer Experience | 0-100 | Tooling, debugging, workflow quality |
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| Documentation Quality | 0-10 | Completeness, clarity, examples |
|
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|
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### Weighted Scoring Algorithm
|
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|
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The comparator uses normalized weighted scoring:
|
||||
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```python
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# Default category weights (sum to 100%)
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weights = {
|
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"performance": 15,
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"scalability": 15,
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"developer_experience": 20,
|
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"ecosystem": 15,
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"learning_curve": 10,
|
||||
"documentation": 10,
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"community_support": 10,
|
||||
"enterprise_readiness": 5
|
||||
}
|
||||
|
||||
# Final score calculation
|
||||
weighted_score = sum(category_score * weight / 100 for each category)
|
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```
|
||||
|
||||
### Confidence Scoring
|
||||
|
||||
Confidence is calculated based on score gap between top options:
|
||||
|
||||
| Score Gap | Confidence Level |
|
||||
|-----------|------------------|
|
||||
| < 5 points | Low (40-50%) |
|
||||
| 5-15 points | Medium (50-70%) |
|
||||
| > 15 points | High (70-100%) |
|
||||
|
||||
---
|
||||
|
||||
## Financial Calculations
|
||||
|
||||
### TCO Components
|
||||
|
||||
**Initial Costs (One-Time)**
|
||||
- Licensing fees
|
||||
- Training: `team_size * hours_per_dev * hourly_rate + materials`
|
||||
- Migration costs
|
||||
- Setup and tooling
|
||||
|
||||
**Operational Costs (Annual)**
|
||||
- Licensing renewals
|
||||
- Hosting: `base_cost * (1 + growth_rate)^(year - 1)`
|
||||
- Support contracts
|
||||
- Maintenance: `team_size * hours_per_dev_monthly * hourly_rate * 12`
|
||||
|
||||
**Scaling Costs**
|
||||
- Infrastructure: `servers * cost_per_server * 12`
|
||||
- Cost per user: `total_yearly_cost / user_count`
|
||||
|
||||
### ROI Calculations
|
||||
|
||||
```
|
||||
productivity_value = additional_features_per_year * avg_feature_value
|
||||
net_tco = total_cost - (productivity_value * years)
|
||||
roi_percentage = (benefits - costs) / costs * 100
|
||||
```
|
||||
|
||||
### Cost Per Metric Reference
|
||||
|
||||
| Metric | Description |
|
||||
|--------|-------------|
|
||||
| Cost per user | Monthly or yearly per active user |
|
||||
| Cost per API request | Average cost per 1000 requests |
|
||||
| Cost per GB | Storage and transfer costs |
|
||||
| Cost per compute hour | Processing time costs |
|
||||
|
||||
---
|
||||
|
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## Ecosystem Health Metrics
|
||||
|
||||
### GitHub Health Score (0-100)
|
||||
|
||||
| Metric | Max Points | Thresholds |
|
||||
|--------|------------|------------|
|
||||
| Stars | 30 | 50K+: 30, 20K+: 25, 10K+: 20, 5K+: 15, 1K+: 10 |
|
||||
| Forks | 20 | 10K+: 20, 5K+: 15, 2K+: 12, 1K+: 10 |
|
||||
| Contributors | 20 | 500+: 20, 200+: 15, 100+: 12, 50+: 10 |
|
||||
| Commits/month | 30 | 100+: 30, 50+: 25, 25+: 20, 10+: 15 |
|
||||
|
||||
### npm Health Score (0-100)
|
||||
|
||||
| Metric | Max Points | Thresholds |
|
||||
|--------|------------|------------|
|
||||
| Weekly downloads | 40 | 1M+: 40, 500K+: 35, 100K+: 30, 50K+: 25, 10K+: 20 |
|
||||
| Major version | 20 | v5+: 20, v3+: 15, v1+: 10 |
|
||||
| Dependencies | 20 | ≤10: 20, ≤25: 15, ≤50: 10 (fewer is better) |
|
||||
| Days since publish | 20 | ≤30: 20, ≤90: 15, ≤180: 10, ≤365: 5 |
|
||||
|
||||
### Community Health Score (0-100)
|
||||
|
||||
| Metric | Max Points | Thresholds |
|
||||
|--------|------------|------------|
|
||||
| Stack Overflow questions | 25 | 50K+: 25, 20K+: 20, 10K+: 15, 5K+: 10 |
|
||||
| Job postings | 25 | 5K+: 25, 2K+: 20, 1K+: 15, 500+: 10 |
|
||||
| Tutorials | 25 | 1K+: 25, 500+: 20, 200+: 15, 100+: 10 |
|
||||
| Forum/Discord members | 25 | 50K+: 25, 20K+: 20, 10K+: 15, 5K+: 10 |
|
||||
|
||||
### Corporate Backing Score
|
||||
|
||||
| Backing Type | Score |
|
||||
|--------------|-------|
|
||||
| Major tech company (Google, Microsoft, Meta) | 100 |
|
||||
| Established company (Vercel, HashiCorp) | 80 |
|
||||
| Funded startup | 60 |
|
||||
| Community-led (strong community) | 40 |
|
||||
| Individual maintainers | 20 |
|
||||
|
||||
---
|
||||
|
||||
## Security Metrics
|
||||
|
||||
### Security Scoring Components
|
||||
|
||||
| Metric | Description |
|
||||
|--------|-------------|
|
||||
| CVE Count (12 months) | Known vulnerabilities in last year |
|
||||
| CVE Count (3 years) | Longer-term vulnerability history |
|
||||
| Severity Distribution | Critical/High/Medium/Low counts |
|
||||
| Patch Frequency | Average days to patch vulnerabilities |
|
||||
|
||||
### Compliance Readiness Levels
|
||||
|
||||
| Level | Score Range | Description |
|
||||
|-------|-------------|-------------|
|
||||
| Ready | 90-100% | Meets compliance requirements |
|
||||
| Mostly Ready | 70-89% | Minor gaps to address |
|
||||
| Partial | 50-69% | Significant work needed |
|
||||
| Not Ready | < 50% | Major gaps exist |
|
||||
|
||||
### Compliance Framework Coverage
|
||||
|
||||
**GDPR**
|
||||
- Data privacy features
|
||||
- Consent management
|
||||
- Data portability
|
||||
- Right to deletion
|
||||
|
||||
**SOC2**
|
||||
- Access controls
|
||||
- Encryption at rest/transit
|
||||
- Audit logging
|
||||
- Change management
|
||||
|
||||
**HIPAA**
|
||||
- PHI handling
|
||||
- Encryption standards
|
||||
- Access controls
|
||||
- Audit trails
|
||||
|
||||
---
|
||||
|
||||
## Migration Metrics
|
||||
|
||||
### Complexity Scoring (1-10 Scale)
|
||||
|
||||
| Factor | Weight | Description |
|
||||
|--------|--------|-------------|
|
||||
| Code Changes | 30% | Lines of code affected |
|
||||
| Architecture Impact | 25% | Breaking changes, API compatibility |
|
||||
| Data Migration | 25% | Schema changes, data transformation |
|
||||
| Downtime Requirements | 20% | Zero-downtime possible vs planned outage |
|
||||
|
||||
### Effort Estimation
|
||||
|
||||
| Phase | Components |
|
||||
|-------|------------|
|
||||
| Development | Hours per component * complexity factor |
|
||||
| Testing | Unit + integration + E2E hours |
|
||||
| Training | Team size * learning curve hours |
|
||||
| Buffer | 20-30% for unknowns |
|
||||
|
||||
### Risk Assessment Matrix
|
||||
|
||||
| Risk Category | Factors Evaluated |
|
||||
|---------------|-------------------|
|
||||
| Technical | API incompatibilities, performance regressions |
|
||||
| Business | Downtime impact, feature parity gaps |
|
||||
| Team | Learning curve, skill gaps |
|
||||
|
||||
---
|
||||
|
||||
## Performance Benchmarks
|
||||
|
||||
### Throughput/Latency Metrics
|
||||
|
||||
| Metric | Description |
|
||||
|--------|-------------|
|
||||
| RPS | Requests per second |
|
||||
| Avg Response Time | Mean response latency (ms) |
|
||||
| P95 Latency | 95th percentile response time |
|
||||
| P99 Latency | 99th percentile response time |
|
||||
| Concurrent Users | Maximum simultaneous connections |
|
||||
|
||||
### Resource Usage Metrics
|
||||
|
||||
| Metric | Unit |
|
||||
|--------|------|
|
||||
| Memory | MB/GB per instance |
|
||||
| CPU | Utilization percentage |
|
||||
| Storage | GB required |
|
||||
| Network | Bandwidth MB/s |
|
||||
|
||||
### Scalability Characteristics
|
||||
|
||||
| Type | Description |
|
||||
|------|-------------|
|
||||
| Horizontal | Add more instances, efficiency factor |
|
||||
| Vertical | CPU/memory limits per instance |
|
||||
| Cost per Performance | Dollar per 1000 RPS |
|
||||
| Scaling Inflection | Point where cost efficiency changes |
|
||||
362
engineering-team/tech-stack-evaluator/references/workflows.md
Normal file
362
engineering-team/tech-stack-evaluator/references/workflows.md
Normal file
@@ -0,0 +1,362 @@
|
||||
# Technology Evaluation Workflows
|
||||
|
||||
Step-by-step workflows for common evaluation scenarios.
|
||||
|
||||
---
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [Framework Comparison Workflow](#framework-comparison-workflow)
|
||||
- [TCO Analysis Workflow](#tco-analysis-workflow)
|
||||
- [Migration Assessment Workflow](#migration-assessment-workflow)
|
||||
- [Security Evaluation Workflow](#security-evaluation-workflow)
|
||||
- [Cloud Provider Selection Workflow](#cloud-provider-selection-workflow)
|
||||
|
||||
---
|
||||
|
||||
## Framework Comparison Workflow
|
||||
|
||||
Use this workflow when comparing frontend/backend frameworks or libraries.
|
||||
|
||||
### Step 1: Define Requirements
|
||||
|
||||
1. Identify the use case:
|
||||
- What type of application? (SaaS, e-commerce, real-time, etc.)
|
||||
- What scale? (users, requests, data volume)
|
||||
- What team size and skill level?
|
||||
|
||||
2. Set priorities (weights must sum to 100%):
|
||||
- Performance: ____%
|
||||
- Scalability: ____%
|
||||
- Developer Experience: ____%
|
||||
- Ecosystem: ____%
|
||||
- Learning Curve: ____%
|
||||
- Other: ____%
|
||||
|
||||
3. List constraints:
|
||||
- Budget limitations
|
||||
- Timeline requirements
|
||||
- Compliance needs
|
||||
- Existing infrastructure
|
||||
|
||||
### Step 2: Run Comparison
|
||||
|
||||
```bash
|
||||
python scripts/stack_comparator.py \
|
||||
--technologies "React,Vue,Angular" \
|
||||
--use-case "enterprise-saas" \
|
||||
--weights "performance:20,ecosystem:25,scalability:20,developer_experience:35"
|
||||
```
|
||||
|
||||
### Step 3: Analyze Results
|
||||
|
||||
1. Review weighted total scores
|
||||
2. Check confidence level (High/Medium/Low)
|
||||
3. Examine strengths and weaknesses for each option
|
||||
4. Review decision factors
|
||||
|
||||
### Step 4: Validate Recommendation
|
||||
|
||||
1. Match recommendation to your constraints
|
||||
2. Consider team skills and hiring market
|
||||
3. Evaluate ecosystem for your specific needs
|
||||
4. Check corporate backing and long-term viability
|
||||
|
||||
### Step 5: Document Decision
|
||||
|
||||
Record:
|
||||
- Final selection with rationale
|
||||
- Trade-offs accepted
|
||||
- Risks identified
|
||||
- Mitigation strategies
|
||||
|
||||
---
|
||||
|
||||
## TCO Analysis Workflow
|
||||
|
||||
Use this workflow for comprehensive cost analysis over multiple years.
|
||||
|
||||
### Step 1: Gather Cost Data
|
||||
|
||||
**Initial Costs:**
|
||||
- [ ] Licensing fees (if any)
|
||||
- [ ] Training hours per developer
|
||||
- [ ] Developer hourly rate
|
||||
- [ ] Migration costs
|
||||
- [ ] Setup and tooling costs
|
||||
|
||||
**Operational Costs:**
|
||||
- [ ] Monthly hosting costs
|
||||
- [ ] Annual support contracts
|
||||
- [ ] Maintenance hours per developer per month
|
||||
|
||||
**Scaling Parameters:**
|
||||
- [ ] Initial user count
|
||||
- [ ] Expected annual growth rate
|
||||
- [ ] Infrastructure scaling approach
|
||||
|
||||
### Step 2: Run TCO Calculator
|
||||
|
||||
```bash
|
||||
python scripts/tco_calculator.py \
|
||||
--input assets/sample_input_tco.json \
|
||||
--years 5 \
|
||||
--output tco_report.json
|
||||
```
|
||||
|
||||
### Step 3: Analyze Cost Breakdown
|
||||
|
||||
1. Review initial vs. operational costs ratio
|
||||
2. Examine year-over-year cost growth
|
||||
3. Check cost per user trends
|
||||
4. Identify scaling efficiency
|
||||
|
||||
### Step 4: Identify Optimization Opportunities
|
||||
|
||||
Review:
|
||||
- Can hosting costs be reduced with reserved pricing?
|
||||
- Can automation reduce maintenance hours?
|
||||
- Are there cheaper alternatives for specific components?
|
||||
|
||||
### Step 5: Compare Multiple Options
|
||||
|
||||
Run TCO analysis for each technology option:
|
||||
1. Current state (baseline)
|
||||
2. Option A
|
||||
3. Option B
|
||||
|
||||
Compare:
|
||||
- 5-year total cost
|
||||
- Break-even point
|
||||
- Risk-adjusted costs
|
||||
|
||||
---
|
||||
|
||||
## Migration Assessment Workflow
|
||||
|
||||
Use this workflow when planning technology migrations.
|
||||
|
||||
### Step 1: Document Current State
|
||||
|
||||
1. Count lines of code
|
||||
2. List all components/modules
|
||||
3. Identify dependencies
|
||||
4. Document current architecture
|
||||
5. Note existing pain points
|
||||
|
||||
### Step 2: Define Target State
|
||||
|
||||
1. Target technology/framework
|
||||
2. Target architecture
|
||||
3. Expected benefits
|
||||
4. Success criteria
|
||||
|
||||
### Step 3: Assess Team Readiness
|
||||
|
||||
- How many developers have target technology experience?
|
||||
- What training is needed?
|
||||
- What is the team's capacity during migration?
|
||||
|
||||
### Step 4: Run Migration Analysis
|
||||
|
||||
```bash
|
||||
python scripts/migration_analyzer.py \
|
||||
--from "angular-1.x" \
|
||||
--to "react" \
|
||||
--codebase-size 50000 \
|
||||
--components 200 \
|
||||
--team-size 6
|
||||
```
|
||||
|
||||
### Step 5: Review Risk Assessment
|
||||
|
||||
For each risk category:
|
||||
1. Identify specific risks
|
||||
2. Assess probability and impact
|
||||
3. Define mitigation strategies
|
||||
4. Assign risk owners
|
||||
|
||||
### Step 6: Plan Migration Phases
|
||||
|
||||
1. **Phase 1: Foundation**
|
||||
- Setup new infrastructure
|
||||
- Create migration utilities
|
||||
- Train team
|
||||
|
||||
2. **Phase 2: Incremental Migration**
|
||||
- Migrate by feature area
|
||||
- Maintain parallel systems
|
||||
- Continuous testing
|
||||
|
||||
3. **Phase 3: Completion**
|
||||
- Remove legacy code
|
||||
- Optimize performance
|
||||
- Complete documentation
|
||||
|
||||
4. **Phase 4: Stabilization**
|
||||
- Monitor production
|
||||
- Address issues
|
||||
- Gather metrics
|
||||
|
||||
### Step 7: Define Rollback Plan
|
||||
|
||||
Document:
|
||||
- Trigger conditions for rollback
|
||||
- Rollback procedure
|
||||
- Data recovery steps
|
||||
- Communication plan
|
||||
|
||||
---
|
||||
|
||||
## Security Evaluation Workflow
|
||||
|
||||
Use this workflow for security and compliance assessment.
|
||||
|
||||
### Step 1: Identify Requirements
|
||||
|
||||
1. List applicable compliance standards:
|
||||
- [ ] GDPR
|
||||
- [ ] SOC2
|
||||
- [ ] HIPAA
|
||||
- [ ] PCI-DSS
|
||||
- [ ] Other: _____
|
||||
|
||||
2. Define security priorities:
|
||||
- Data encryption requirements
|
||||
- Access control needs
|
||||
- Audit logging requirements
|
||||
- Incident response expectations
|
||||
|
||||
### Step 2: Gather Security Data
|
||||
|
||||
For each technology:
|
||||
- [ ] CVE count (last 12 months)
|
||||
- [ ] CVE count (last 3 years)
|
||||
- [ ] Severity distribution
|
||||
- [ ] Average patch time
|
||||
- [ ] Security features list
|
||||
|
||||
### Step 3: Run Security Assessment
|
||||
|
||||
```bash
|
||||
python scripts/security_assessor.py \
|
||||
--technology "express-js" \
|
||||
--compliance "soc2,gdpr" \
|
||||
--output security_report.json
|
||||
```
|
||||
|
||||
### Step 4: Analyze Results
|
||||
|
||||
Review:
|
||||
1. Overall security score
|
||||
2. Vulnerability trends
|
||||
3. Patch responsiveness
|
||||
4. Compliance readiness per standard
|
||||
|
||||
### Step 5: Identify Gaps
|
||||
|
||||
For each compliance standard:
|
||||
1. List missing requirements
|
||||
2. Estimate remediation effort
|
||||
3. Identify workarounds if available
|
||||
4. Calculate compliance cost
|
||||
|
||||
### Step 6: Make Risk-Based Decision
|
||||
|
||||
Consider:
|
||||
- Acceptable risk level
|
||||
- Cost of remediation
|
||||
- Alternative technologies
|
||||
- Business impact of compliance gaps
|
||||
|
||||
---
|
||||
|
||||
## Cloud Provider Selection Workflow
|
||||
|
||||
Use this workflow for AWS vs Azure vs GCP decisions.
|
||||
|
||||
### Step 1: Define Workload Requirements
|
||||
|
||||
1. Workload type:
|
||||
- [ ] Web application
|
||||
- [ ] API services
|
||||
- [ ] Data analytics
|
||||
- [ ] Machine learning
|
||||
- [ ] IoT
|
||||
- [ ] Other: _____
|
||||
|
||||
2. Resource requirements:
|
||||
- Compute: ____ instances, ____ cores, ____ GB RAM
|
||||
- Storage: ____ TB, type (block/object/file)
|
||||
- Database: ____ type, ____ size
|
||||
- Network: ____ GB/month transfer
|
||||
|
||||
3. Special requirements:
|
||||
- [ ] GPU/TPU for ML
|
||||
- [ ] Edge computing
|
||||
- [ ] Multi-region
|
||||
- [ ] Specific compliance certifications
|
||||
|
||||
### Step 2: Evaluate Feature Availability
|
||||
|
||||
For each provider, verify:
|
||||
- Required services exist
|
||||
- Service maturity level
|
||||
- Regional availability
|
||||
- SLA guarantees
|
||||
|
||||
### Step 3: Run Cost Comparison
|
||||
|
||||
```bash
|
||||
python scripts/tco_calculator.py \
|
||||
--providers "aws,azure,gcp" \
|
||||
--workload-config workload.json \
|
||||
--years 3
|
||||
```
|
||||
|
||||
### Step 4: Assess Ecosystem Fit
|
||||
|
||||
Consider:
|
||||
- Team's existing expertise
|
||||
- Development tooling preferences
|
||||
- CI/CD integration
|
||||
- Monitoring and observability tools
|
||||
|
||||
### Step 5: Evaluate Vendor Lock-in
|
||||
|
||||
For each provider:
|
||||
1. List proprietary services you'll use
|
||||
2. Estimate migration cost if switching
|
||||
3. Identify portable alternatives
|
||||
4. Calculate lock-in risk score
|
||||
|
||||
### Step 6: Make Final Selection
|
||||
|
||||
Weight factors:
|
||||
- Cost: ____%
|
||||
- Features: ____%
|
||||
- Team expertise: ____%
|
||||
- Lock-in risk: ____%
|
||||
- Support quality: ____%
|
||||
|
||||
Select provider with highest weighted score.
|
||||
|
||||
---
|
||||
|
||||
## Best Practices
|
||||
|
||||
### For All Evaluations
|
||||
|
||||
1. **Document assumptions** - Make all assumptions explicit
|
||||
2. **Validate data** - Verify metrics from multiple sources
|
||||
3. **Consider context** - Generic scores may not apply to your situation
|
||||
4. **Include stakeholders** - Get input from team members who will use the technology
|
||||
5. **Plan for change** - Technology landscapes evolve; plan for flexibility
|
||||
|
||||
### Common Pitfalls to Avoid
|
||||
|
||||
1. Over-weighting recent popularity vs. long-term stability
|
||||
2. Ignoring team learning curve in timeline estimates
|
||||
3. Underestimating migration complexity
|
||||
4. Assuming vendor claims are accurate
|
||||
5. Not accounting for hidden costs (training, hiring, technical debt)
|
||||
Reference in New Issue
Block a user