claude-skills-reference/engineering-team/tech-stack-evaluator/references/workflows.md

# 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)