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name: technical-research description: Systematic technical research methodology for evaluating technologies, libraries, and approaches. Use when researching new technologies, comparing alternatives, or making technical decisions.

Technical Research Skill

Core Principle

Research systematically to make evidence-based decisions.

Good technical research:

• Starts with clear questions — Know what you're trying to decide
• Evaluates systematically — Consistent criteria across options
• Documents findings — Share knowledge with team
• Leads to action — Research informs decisions

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Research Process

Copy┌────────────────────────────────────────────────────┐
│           RESEARCH PROCESS                         │
└────────────────────────────────────────────────────┘

1. Define Question
   ↓
2. Establish Criteria
   ↓
3. Find Candidates
   ↓
4. Evaluate Systematically
   ↓
5. Hands-On Verification
   ↓
6. Document & Decide

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Step 1: Define the Research Question

Start with "Why"

❌ Vague: "Research state management libraries" ✅ Clear: "Which state management library best fits our React app with complex async workflows?"

Good Research Questions

✅ "Should we migrate from REST to GraphQL for our mobile API?" ✅ "Which message queue (RabbitMQ, Kafka, AWS SQS) handles our 10M events/day use case best?" ✅ "Can we replace our custom auth with Auth0 without breaking existing integrations?"

Bad Research Questions

❌ "What's the best framework?" (Depends on context) ❌ "Should we use microservices?" (Missing constraints) ❌ "Is technology X good?" (No decision criteria)

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Step 2: Establish Evaluation Criteria

Categories of Criteria

Functional:

• Does it solve our problem?
• Does it have required features?
• Does it integrate with our existing stack?

Non-Functional:

• Performance (speed, resource usage)
• Scalability (handles our load?)
• Reliability (uptime, error rates)
• Security (vulnerabilities, updates)

Team & Org:

• Team expertise (learning curve)
• Documentation quality
• Community support
• License compatibility

Operational:

• Maintenance burden
• Hosting/infrastructure costs
• Monitoring/observability
• Deployment complexity

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Prioritize Criteria

Not all criteria are equally important.

Example: Choosing a Database

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Step 3: Find Candidates

Sources

Trusted sources:

• Official documentation
• GitHub repos (stars, activity, issues)
• Technology radars (ThoughtWorks, etc.)
• Stack Overflow trends
• Team experience

Be skeptical of:

• Marketing websites
• Outdated blog posts
• Hype-driven articles
• Vendor comparisons

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Narrowing Down

Start broad, narrow systematically:

1. Initial research: 10-20 candidates
2. Quick filter: 3-5 top candidates (based on obvious deal-breakers)
3. Deep dive: 2-3 finalists
4. Hands-on: 1-2 prototypes

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Step 4: Evaluate Systematically

Use a Scoring Matrix

Example: API Client Libraries

Legend:

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

Conclusion: Option A best fits our needs

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Step 5: Hands-On Verification

Build a Proof of Concept (POC)

Purpose: Validate assumptions with real code

Scope:

• Small (1-3 days max)
• Focused (test key concerns)
• Disposable (will be thrown away)

What to test:

• Integration points
• Performance with realistic data
• Developer experience
• Edge cases that matter

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POC Structure

Copypoc-experiment/
├── README.md           # What you're testing and why
├── setup.sh            # How to run it
├── src/
│   ├── integration.js  # Test key integration
│   └── performance.js  # Test performance concern
├── results.md          # Findings and measurements
└── decision.md         # Recommendation based on POC

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Example POC Goals

Evaluating a new database:

• ✅ Can we migrate existing data?
• ✅ Does query performance meet our <100ms requirement?
• ✅ Can we implement our complex search feature?

Evaluating a framework:

• ✅ Can we integrate with our auth system?
• ✅ Does it handle our file upload use case?
• ✅ Is the learning curve acceptable for the team?

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Step 6: Document & Decide

Research Document Template

Copy# Research: [Topic]

## Context

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

**Decision needed by:** [Date]

**Stakeholders:** [Who cares about this decision?]

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## Research Question

[Clear, specific question]

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## Evaluation Criteria

| Criterion | Priority | Rationale |
|-----------|----------|-----------|
| [Criterion 1] | HIGH | [Why this matters] |
| [Criterion 2] | MEDIUM | [Why this matters] |

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## Candidates Evaluated

### Option A: [Name]

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

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

**Score:** X/10

**POC findings:** [Link to POC or summary]

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### Option B: [Name]

[Same structure]

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## Recommendation

**Recommended:** [Option X]

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

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

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## Implementation Plan

1. [Step 1]
2. [Step 2]
3. [Step 3]

**Estimated effort:** [Time estimate]

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## References

- [Link to documentation]
- [Link to POC]
- [Link to similar case studies]

---

**Date:** 2026-01-09
**Author:** [Your name]
**Reviewers:** [Who reviewed this]

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Research Anti-Patterns

1. Analysis Paralysis

❌ Anti-pattern: Researching forever, never deciding

Signs:

• Evaluating 15+ options
• Adding more criteria endlessly
• "Let's do more research" when decision is clear

Fix: Set time limits and decision deadlines

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2. Resume-Driven Development

❌ Anti-pattern: Choosing tech because it looks good on resume

Signs:

• "Let's use [hot new tech] because everyone is talking about it"
• Ignoring that current solution works fine
• Choosing complex over simple without justification

Fix: Evaluate based on actual needs, not hype

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3. Not Invented Here (NIH)

❌ Anti-pattern: Building custom solution instead of using existing tools

Signs:

• "It's simple, we can build it ourselves"
• Underestimating maintenance burden
• Ignoring mature, well-tested alternatives

Fix: Calculate total cost of ownership (development + maintenance)

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4. Sunk Cost Fallacy

❌ Anti-pattern: Continuing with poor choice because of past investment

Signs:

• "We've already invested 6 months in this"
• Ignoring better alternatives
• Escalating commitment to failing approach

Fix: Evaluate current state objectively, past is sunk cost

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Research Checklists

Before Starting Research

• Clear research question defined
• Evaluation criteria established
• Priorities assigned to criteria
• Decision deadline set
• Stakeholders identified

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During Research

• Evaluating at least 2-3 alternatives
• Using consistent criteria across all options
• Documenting findings as you go
• Building POCs for top candidates
• Measuring against real requirements

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Before Making Decision

• All high-priority criteria evaluated
• Hands-on validation completed (POC)
• Team has reviewed findings
• Risks identified with mitigations
• Implementation plan outlined
• Decision documented

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Integration with Other Skills

With Documentation Writing

• Document research in Architecture Decision Records (ADRs)
• Share findings with team
• Create decision trail for future reference

With Code Review

• Use research findings to inform coding standards
• Reference research when reviewing architectural decisions

With Testing Strategy

• POCs should include tests
• Validate performance claims with benchmarks

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Quick Reference

Research Question Template

"Which [technology/approach] best [solves our problem] given [our constraints]?"

Evaluation Criteria Categories

1. Functional — Does it work for our use case?
2. Non-Functional — Performance, security, reliability
3. Team — Learning curve, documentation, support
4. Operational — Maintenance, cost, complexity

POC Scope

• Time: 1-3 days
• Goal: Validate key assumptions
• Output: Decision recommendation

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Remember: Good research leads to good decisions. Define clear questions, evaluate systematically, validate with hands-on testing. Document your findings so others can learn from your work. Don't let perfect be the enemy of good—research enough to decide, then act.