Evaluation Skill

Core Principle

Evaluate with criteria, decide with data. Document the trade-offs.

Good evaluation:

• •Starts with clear criteria — Define what matters before evaluating
• •Uses consistent scoring — Apply the same standards to all options
• •Weighs criteria by importance — Not all factors are equal
• •Documents trade-offs — Every choice has costs and benefits
• •Leads to defensible decisions — Data-driven, not opinion-driven

When to Use This Skill

Use this skill when:

• •Choosing between multiple technologies — React vs Vue vs Angular
• •Vendor/tool selection — Cloud providers, monitoring tools, CI/CD platforms
• •Framework comparison — Express vs Fastify, Django vs Flask
• •Library selection — State management, testing frameworks, HTTP clients
• •Architecture pattern selection — Microservices vs Monolith, REST vs GraphQL

Evaluation Framework

┌────────────────────────────────────────────────────┐
│           EVALUATION PROCESS                       │
└────────────────────────────────────────────────────┘

1. Define Evaluation Criteria
   ↓
2. Assign Weights to Criteria
   ↓
3. Research Each Option
   ↓
4. Score Against Criteria
   ↓
5. Hands-On Testing (PoCs)
   ↓
6. Calculate Weighted Scores
   ↓
7. Document Trade-offs
   ↓
8. Make Recommendation

Step 1: Define Evaluation Criteria

Categories of Criteria

Technical Criteria

Features:

• •Does it have required functionality?
• •Are critical features mature and stable?
• •Does it support our use cases?
• •Are there deal-breaker missing features?

Performance:

• •Throughput (requests/sec, transactions/sec)
• •Latency (response times, p95, p99)
• •Resource usage (CPU, memory, disk)
• •Benchmark results for our workload

Scalability:

• •Horizontal scaling capabilities
• •Vertical scaling limits
• •Load handling characteristics
• •Data volume limits

Security:

• •Vulnerability history
• •Security update frequency
• •Authentication/authorization support
• •Compliance certifications (SOC2, ISO, HIPAA)
• •Data encryption (at rest, in transit)

Operational Criteria

Reliability:

• •Uptime history (SLA guarantees)
• •Error handling capabilities
• •Fault tolerance features
• •Disaster recovery support

Monitoring:

• •Built-in observability
• •Metrics export (Prometheus, etc.)
• •Logging capabilities
• •Tracing support (OpenTelemetry)

Maintenance:

• •Update frequency and process
• •Breaking change policy
• •Backward compatibility
• •Upgrade complexity

Team Criteria

Learning Curve:

• •Time to productivity for new developers
• •Similarity to existing tools
• •Conceptual complexity
• •Available training materials

Documentation:

• •Quality of official docs
• •API reference completeness
• •Tutorial and guide availability
• •Example code quality

Community:

• •Community size and activity
• •GitHub stars/forks/activity
• •Stack Overflow questions
• •Forum/Discord/Slack activity
• •Response time for issues

Business Criteria

Cost:

• •Licensing fees (per user, per server, per transaction)
• •Infrastructure costs
• •Training costs
• •Migration costs
• •Opportunity costs

Licensing:

• •License type (MIT, Apache, GPL, proprietary)
• •Compatibility with our license
• •Commercial use restrictions
• •Attribution requirements

Vendor Lock-in:

• •Portability to alternatives
• •Data export capabilities
• •Proprietary features vs standards
• •Switching costs

Support:

• •Commercial support availability
• •Support response times
• •Support quality
• •Community vs paid support

Strategic Criteria

Ecosystem:

• •Integration with other tools
• •Plugin/extension marketplace
• •Third-party tool support
• •Industry adoption

Roadmap:

• •Future feature plans
• •Product vision alignment
• •Investment in development
• •Feature request process

Adoption Trends:

• •Growing vs declining usage
• •Industry momentum
• •Job market demand
• •Analyst recommendations

Step 2: Assign Weights

Not all criteria are equally important. Weight them based on your context.

Weighting Scale

Example: Choosing a Database

Step 3: Create Scoring Matrix

Scoring Matrix Template

Scoring Scale

0-10 Scoring:

• •9-10 — Excellent, exceeds requirements
• •7-8 — Good, meets requirements well
• •5-6 — Acceptable, meets minimum requirements
• •3-4 — Poor, below requirements
• •0-2 — Inadequate, deal-breaker

Visual Indicators:

• •✅ Excellent (8-10)
• •⚠️ Acceptable (5-7)
• •❌ Poor (0-4)

Step 4: Research Each Option

Research Sources

Trusted:

• •✅ Official documentation
• •✅ GitHub repository (code, issues, PRs)
• •✅ Release notes and changelog
• •✅ Production case studies
• •✅ Performance benchmarks (reputable sources)
• •✅ Security advisories and CVE database
• •✅ Team experience and feedback

Skeptical:

• •⚠️ Vendor marketing materials
• •⚠️ Paid comparison sites
• •⚠️ Outdated blog posts (>2 years old)
• •⚠️ Hype-driven articles
• •⚠️ Benchmarks by vendors

Technology Evaluation Checklist

For each option, evaluate:

Documentation Quality:

• • Getting started guide exists and works
• • API reference is complete
• • Examples cover common use cases
• • Troubleshooting guide available
• • Migration guides (if applicable)

Community Health:

• • Active maintainers (commits in last month)
• • Issues are being addressed (response rate)
• • Pull requests are being reviewed
• • Community guidelines exist
• • Code of conduct present

GitHub Activity (if open source):

• • Stars: >1000 for libraries, >5000 for frameworks
• • Recent commits (weekly activity)
• • Issue response time (<7 days for critical)
• • PR merge rate (not stagnant)
• • Number of contributors (>10 for critical tools)

Production Usage:

• • Companies using it in production
• • Scale of production deployments
• • Case studies available
• • Known production issues

Integration Complexity:

• • Works with our existing stack
• • Clear integration examples
• • Migration path from current solution
• • Breaking changes in recent versions

Performance Characteristics:

• • Benchmark data available
• • Performance under our expected load
• • Resource requirements documented
• • Performance tuning guidance

Security:

• • Security policy documented
• • CVE history reviewed
• • Security update frequency
• • Authentication/authorization support
• • Encryption support

License:

• • License type identified
• • Compatible with our project
• • Commercial use allowed
• • Attribution requirements clear

Cost:

• • Pricing model clear
• • Hidden costs identified (support, training, infrastructure)
• • Total cost of ownership calculated
• • Budget impact assessed

Step 5: Hands-On Testing

Build Proof of Concepts (PoCs)

For finalists (typically 2-3 options), build focused PoCs.

PoC Scope:

• •Time-boxed (1-3 days per option)
• •Tests critical features only
• •Validates key assumptions
• •Measures actual performance
• •Assesses developer experience

What to Test:

• •Integration with critical systems
• •Performance with realistic data
• •Learning curve (time to hello world)
• •Key features that matter most
• •Migration complexity (if replacing existing tool)

PoC Documentation:

# PoC: [Option Name]

## Objective
[What we're validating]

## Setup Time
[How long to get running]

## Key Features Tested
- [ ] Feature 1: [Result]
- [ ] Feature 2: [Result]
- [ ] Feature 3: [Result]

## Performance Results
- Throughput: [X requests/sec]
- Latency p95: [X ms]
- Resource usage: [CPU/Memory]

## Developer Experience
- Ease of use: [Rating 1-10]
- Documentation quality: [Rating 1-10]
- Time to productivity: [X hours/days]

## Issues Encountered
- [Issue 1 and resolution]
- [Issue 2 and resolution]

## Recommendation
[Go/No-go with rationale]

See the proof-of-concept skill for detailed PoC guidance.

Step 6: Document Trade-offs

Every choice involves trade-offs. Document them explicitly.

Trade-off Template

Option A vs Option B:

Analysis:

• •Option A is 2x faster but requires 10x more learning time
• •Option B has 10x larger community but less flexibility
• •Trade-off: Do we optimize for performance or team velocity?

Complete Evaluation Example

Scenario: Frontend Framework Selection

Context:

• •Team: 5 developers (React experience)
• •Project: New e-commerce platform
• •Requirements: High performance, SEO support, large ecosystem
• •Timeline: 6 months to MVP

Evaluation Criteria with Weights

Scoring Matrix

Legend:

• •✅ Excellent (8-10)
• •⚠️ Acceptable (5-7)
• •❌ Poor (0-4)

Detailed Analysis

React (Score: 9.2):

Strengths:

• •Team already knows React (minimal learning curve)
• •Largest ecosystem (components, tools, libraries)
• •Best SSR/SSG story (Next.js is industry standard)
• •React Native path for mobile app
• •Massive community and job market
• •Proven long-term viability

Weaknesses:

• •Larger bundle size than Vue/Svelte
• •Performance slightly behind Svelte
• •More boilerplate than Svelte

PoC Findings:

• •Setup time: 30 minutes with Create React App
• •First component: 1 hour
• •Full CRUD page: 4 hours
• •Next.js SSG working out of the box
• •Found excellent UI library (shadcn/ui)

Vue (Score: 7.9):

Strengths:

• •Excellent documentation
• •Great developer experience
• •Good SSR support (Nuxt.js)
• •Smaller bundle size than React
• •Strong community

Weaknesses:

• •Team has no Vue experience
• •Smaller ecosystem than React
• •TypeScript support improving but not as mature
• •React Native vs Vue Native comparison

PoC Findings:

• •Setup time: 45 minutes
• •Learning curve steeper than expected for React developers
• •Composition API similar to React hooks
• •Found most needed plugins

Svelte (Score: 7.0):

Strengths:

• •Best performance (compiles to vanilla JS)
• •Smallest bundle size
• •Elegant syntax, minimal boilerplate
• •Growing momentum

Weaknesses:

• •Smallest ecosystem
• •Team has no experience
• •SSR story (SvelteKit) less mature than Next.js
• •No mobile story
• •Smaller job market

PoC Findings:

• •Setup time: 1 hour
• •Syntax very different from React
• •Impressed by performance
• •Harder to find UI components
• •SvelteKit less documented than Next.js

Angular (Score: 7.8):

Strengths:

• •Complete framework (batteries included)
• •Enterprise backing (Google)
• •Excellent TypeScript support
• •Strong patterns and structure

Weaknesses:

• •Steep learning curve (RxJS, dependency injection)
• •Largest bundle size
• •Team has no Angular experience
• •Less flexibility than React/Vue

PoC Not Pursued:

• •Eliminated after research phase
• •Learning curve too steep for timeline
• •Team preference for React-like model

Trade-offs Summary

React vs Vue:

• •React: Better ecosystem, team knowledge, mobile path
• •Vue: Slightly better performance, smaller bundle
• •Winner: React (team velocity and ecosystem outweigh minor performance gains)

React vs Svelte:

• •Svelte: Best performance, cleanest code
• •React: Mature ecosystem, team knowledge, proven at scale
• •Winner: React (maturity and team velocity critical for 6-month timeline)

Recommendation

Recommended: React with Next.js

Rationale:

1. •Zero learning curve — Team already knows React
2. •Best ecosystem — Largest selection of components and tools
3. •Proven SSR/SSG — Next.js is production-ready and well-documented
4. •Future mobile app — React Native provides clear path
5. •Hiring advantage — Largest React job market
6. •Long-term safety — React is here to stay

Risks & Mitigations:

• •Risk: Larger bundle size than alternatives
  • •Mitigation: Use Next.js code splitting, lazy loading, bundle analysis
• •Risk: Team could become React-only
  • •Mitigation: Encourage learning Vue/Svelte for side projects

Implementation Plan:

1. •Setup Next.js project with TypeScript (Week 1)
2. •Choose and configure UI library (Week 1)
3. •Setup testing framework (Week 1-2)
4. •Build design system (Week 2-3)
5. •Begin feature development (Week 3+)

Estimated Effort: 1 week setup, team productive immediately

Common Evaluation Scenarios

1. Database Selection

Key Criteria:

• •Data model fit (relational, document, graph, time-series)
• •ACID vs eventual consistency requirements
• •Query patterns and performance
• •Horizontal scaling needs
• •Managed service availability
• •Team expertise

Example Comparison:

• •PostgreSQL: ACID, complex queries, good scaling, mature
• •MongoDB: Flexible schema, horizontal scaling, eventual consistency
• •DynamoDB: Fully managed, massive scale, limited query flexibility
• •Cassandra: Write-heavy, linear scaling, eventual consistency

2. Cloud Provider Selection

Key Criteria:

• •Service offerings for your use case
• •Regional availability and latency
• •Pricing model and total cost
• •Team expertise and learning curve
• •Vendor lock-in concerns
• •Compliance certifications
• •Support quality

Example Comparison:

• •AWS: Most complete offering, most complex, largest ecosystem
• •Azure: Best for Microsoft shops, good enterprise features
• •GCP: Excellent for data/ML, strong Kubernetes support, competitive pricing
• •DigitalOcean: Simplest, lower cost, fewer features

3. Testing Framework

Key Criteria:

• •Language/framework support
• •Test types supported (unit, integration, e2e)
• •Speed of test execution
• •Mocking capabilities
• •Assertion library quality
• •IDE integration
• •Learning curve

Example Comparison (JavaScript):

• •Jest: Complete solution, React-friendly, good DX
• •Vitest: Faster than Jest, Vite integration, modern
• •Mocha: Flexible, mature, requires more setup
• •Playwright: Best for e2e, multi-browser support

4. CI/CD Platform

Key Criteria:

• •Integration with version control
• •Pipeline flexibility
• •Performance (build speed)
• •Cost (minutes, concurrent jobs)
• •Self-hosted vs cloud
• •Secret management
• •Marketplace/plugins

Example Comparison:

• •GitHub Actions: Tight GitHub integration, generous free tier
• •GitLab CI: All-in-one platform, self-hosted option
• •Jenkins: Fully self-hosted, maximum flexibility, maintenance burden
• •CircleCI: Fast, good DX, credit-based pricing

Decision Documentation

Link to ADR

For significant decisions, create an Architecture Decision Record (ADR).

See the adr skill for ADR templates and workflow.

ADR Quick Template

# ADR-XXX: [Decision Title]

## Status
Accepted

## Context
[Why we needed to evaluate options]
[Constraints and requirements]
[Timeline and stakeholders]

## Options Considered
1. Option A
2. Option B
3. Option C

## Evaluation
[Link to full evaluation document or scoring matrix]

## Decision
We chose [Option X] because [key reasons].

## Consequences

**Positive:**
- [Benefit 1]
- [Benefit 2]

**Negative:**
- [Trade-off 1] — Mitigated by [mitigation]
- [Trade-off 2] — Accepted because [rationale]

## Follow-up Actions
- [ ] Action 1
- [ ] Action 2

Evaluation Document Template

# Technology Evaluation: [Topic]

**Date:** YYYY-MM-DD
**Author:** [Your name]
**Reviewers:** [Team members who reviewed]
**Status:** [Draft | Under Review | Approved]

---

## Executive Summary

**Decision needed:** [One sentence]
**Recommendation:** [Option X]
**Key reason:** [Primary rationale in one sentence]

---

## Context

**Problem:**
[What problem are we solving?]

**Requirements:**
- [Requirement 1]
- [Requirement 2]
- [Requirement 3]

**Constraints:**
- [Constraint 1: e.g., Budget]
- [Constraint 2: e.g., Timeline]
- [Constraint 3: e.g., Team size]

**Stakeholders:**
- [Stakeholder 1: Interest]
- [Stakeholder 2: Interest]

**Decision deadline:** [Date]

---

## Evaluation Criteria

| Criterion | Weight | Rationale |
|-----------|--------|-----------|
| [Criterion 1] | HIGH | [Why critical] |
| [Criterion 2] | MEDIUM | [Why important] |
| [Criterion 3] | LOW | [Why nice-to-have] |

---

## Options Evaluated

### Option A: [Name]

**Description:** [Brief overview]

**Pros:**
- [Pro 1]
- [Pro 2]
- [Pro 3]

**Cons:**
- [Con 1]
- [Con 2]
- [Con 3]

**Score:** X.X / 10

**PoC findings:** [Link or summary]

---

### Option B: [Name]

[Same structure as Option A]

---

### Option C: [Name]

[Same structure as Option A]

---

## Scoring Matrix

[Include full scoring matrix from evaluation]

---

## Trade-offs Analysis

[Document key trade-offs between top options]

---

## Recommendation

**Recommended:** [Option X]

**Rationale:**
1. [Key reason 1]
2. [Key reason 2]
3. [Key reason 3]

**Risks & Mitigations:**
- **Risk:** [Potential issue]
  - **Mitigation:** [How we'll handle it]
- **Risk:** [Another concern]
  - **Mitigation:** [Response plan]

---

## Implementation Plan

### Phase 1: Setup (Timeline)
- [ ] Task 1
- [ ] Task 2

### Phase 2: Migration (Timeline)
- [ ] Task 1
- [ ] Task 2

### Phase 3: Validation (Timeline)
- [ ] Task 1
- [ ] Task 2

**Total estimated effort:** [Time]

---

## References

- [Official documentation]
- [Benchmark source]
- [PoC repository]
- [Case study]
- [Related ADR]

---

## Appendix: Detailed Scoring

[Include research notes, detailed scores, calculations]

Evaluation Anti-Patterns

1. Resume-Driven Evaluation

❌ Anti-pattern: Choosing technology because it's trendy or looks good on resume

Signs:

• •"Everyone's talking about [new tech]"
• •Ignoring that current solution works well
• •Chasing hype cycles
• •Complexity for complexity's sake

Fix: Evaluate based on actual needs, not trends

2. Analysis Paralysis

❌ Anti-pattern: Evaluating forever, never deciding

Signs:

• •Evaluating 10+ options
• •Adding criteria endlessly
• •"Let's research more" when data is sufficient
• •Perfectionism blocking decision

Fix: Set decision deadline, limit options to 3-5, accept "good enough"

3. Confirmation Bias

❌ Anti-pattern: Deciding first, evaluating to confirm

Signs:

• •Only researching preferred option deeply
• •Ignoring negative evidence
• •Scoring subjectively to favor preference
• •Cherry-picking benchmarks

Fix: Score all options before forming opinion, blind evaluation where possible

4. Sunk Cost Fallacy

❌ Anti-pattern: Continuing with poor choice due to past investment

Signs:

• •"We've already invested 6 months"
• •Ignoring better alternatives
• •Escalating commitment despite evidence
• •Defending past decision

Fix: Evaluate current state objectively, past investment is sunk

5. Feature Checklist Evaluation

❌ Anti-pattern: Choosing option with most features

Signs:

• •Counting features without weighting importance
• •Ignoring that 80% of features won't be used
• •Missing quality vs quantity distinction
• •Complexity without benefit

Fix: Weight features by actual need, value simplicity

Integration with Other Skills

With Proof-of-Concept

Evaluation identifies options, PoC validates top candidates.

Workflow:

1. •Evaluate → Narrow to 2-3 finalists
2. •PoC → Test finalists hands-on
3. •Update scores → Based on PoC findings
4. •Decide → With data from both evaluation and PoC

See proof-of-concept skill.

With Technical Research

Research gathers information, evaluation structures decision-making.

Workflow:

1. •Research → Understand the landscape
2. •Evaluate → Score options systematically
3. •Document → Create decision record

See technical-research skill.

With Architecture Review

Evaluation informs architecture decisions, review validates them.

Workflow:

1. •Evaluate → Choose technology/pattern
2. •Design → Create architecture
3. •Review → Validate architectural decisions

See architecture-review skill.

With ADR

Evaluation provides the data, ADR documents the decision.

Workflow:

1. •Evaluate → Create scoring matrix
2. •Decide → Make recommendation
3. •ADR → Record decision with context

See adr skill.

With Technical Lead Role

Tech leads use evaluation to make informed decisions.

Workflow:

1. •Tech lead identifies need for evaluation
2. •Team collaborates on criteria and weights
3. •Tech lead facilitates evaluation process
4. •Team reviews findings
5. •Tech lead makes final decision

See technical-lead-role skill.

Quick Reference

Evaluation Process Checklist

Planning:

• • Define evaluation criteria (technical, operational, team, business, strategic)
• • Assign weights based on context (HIGH/MEDIUM/LOW)
• • Identify 3-5 options to evaluate
• • Set decision deadline

Research:

• • Review official documentation for each option
• • Check GitHub activity and community health
• • Research production usage and case studies
• • Review security and licensing

Scoring:

• • Create scoring matrix (0-10 scale)
• • Score all options consistently
• • Calculate weighted scores
• • Document rationale for scores

Validation:

• • Build PoCs for top 2-3 options
• • Test critical features
• • Measure actual performance
• • Assess developer experience
• • Update scores based on findings

Decision:

• • Analyze trade-offs between options
• • Document risks and mitigations
• • Make recommendation with rationale
• • Create ADR for significant decisions
• • Communicate decision to stakeholders

Evaluation Criteria Quick List

Technical: Features, performance, scalability, security Operational: Reliability, monitoring, maintenance Team: Learning curve, documentation, community Business: Cost, licensing, vendor lock-in, support Strategic: Ecosystem, roadmap, adoption trends

Scoring Guidelines

• •9-10: Excellent, exceeds requirements
• •7-8: Good, meets requirements well
• •5-6: Acceptable, meets minimum
• •3-4: Poor, below requirements
• •0-2: Inadequate, deal-breaker

When to Re-evaluate

• •New major version released
• •Significant change in requirements
• •Team size or expertise changes
• •Better alternatives emerge
• •Performance degrades
• •Security issues discovered
• •Vendor acquired or direction changes
• •Every 1-2 years for critical dependencies

Remember: Good evaluation leads to good decisions. Define criteria before scoring, weight by importance, validate with hands-on testing. Document trade-offs transparently so future you (and your team) understands why the decision was made. Every choice has costs—make them explicit.