Skill: Design Thinking - Empathize

Description

Deeply understand users through research, interviews, and observation to identify real pain points and needs.

Input

context: Product/feature context (required)
target_users: User segments to research (required)
research_type: interview|observation|survey|analytics (optional, default: interview)
existing_data: Current user feedback/analytics (optional)

Research Techniques

1. User Interviews

Framework: TEDW

Tell me about the last time you...
Explain your workflow for...
Describe the biggest challenge with...
Walk me through how you currently...

Best Practices:

5-7 users per segment minimum
Open-ended questions only
Listen 80%, talk 20%
Ask "why" 5 times (5 Whys technique)
Record exact quotes for later synthesis

2. Pain Point Identification

Severity Matrix:

Critical: Blocks core workflow, happens daily
High: Major frustration, weekly occurrence
Medium: Annoying but workaround exists
Low: Minor inconvenience, rare

Indicators:

User workarounds/hacks
Manual data entry
Switching between tools
Waiting/delays
Errors/mistakes
Emotional language ("frustrating", "annoying")

3. Empathy Mapping Template

SAYS (verbatim quotes)
"I waste 2 hours every week on..."
"Its so frustrating when..."

THINKS (unspoken concerns)
- Worried about making mistakes
- Unsure if doing it right
- Feels inefficient

DOES (observed behaviors)
- Opens 5 tabs to complete one task
- Double-checks every entry
- Asks colleagues for help

FEELS (emotions)
- Frustrated with repetitive work
- Anxious about errors
- Overwhelmed by complexity

PAIN POINTS
- [Critical pain identified]
- [High priority pain]

GAINS (what success looks like)
- Completes task in <5 minutes
- Confident in accuracy
- No context switching

4. Observation Techniques

Contextual Inquiry:

Shadow users in their environment
Note workarounds and hacks
Identify unstated needs
Look for patterns across users

What to Observe:

Where do they slow down?
When do they switch tools?
What causes confusion/errors?
What do they complain about?

Output Format

{
  "status": "success",
  "research_summary": {
    "users_interviewed": 7,
    "segments": ["power_users", "occasional_users"],
    "methods": ["interview", "observation"]
  },
  "user_personas": [
    {
      "name": "Sarah - Marketing Manager",
      "context": "Creates 10+ campaigns/month",
      "goals": ["Speed", "Consistency", "Analytics"],
      "frustrations": ["Manual data entry", "Lost context"],
      "quote": "I spend more time copying data than creating campaigns"
    }
  ],
  "pain_points": [
    {
      "description": "Manual campaign setup takes 2+ hours",
      "severity": "critical",
      "frequency": "daily",
      "users_affected": 6,
      "evidence": ["Quote 1", "Quote 2"],
      "current_workaround": "Excel templates + copy-paste"
    }
  ],
  "empathy_insights": [
    "Users prioritize speed over features",
    "Fear of making mistakes drives behavior",
    "Existing tools lack integration"
  ],
  "key_quotes": [
    "I waste 2 hours every week on manual setup",
    "Im never confident I did it right"
  ],
  "next_step": "Define problem with /dt define"
}

Quality Gates

At least 5 users per segment
Mix of qualitative + quantitative data
Clear pain points with severity ratings
Verbatim user quotes captured
Patterns identified across users
Jobs-to-be-done understood

Token Budget

Max input: 800 tokens
Max output: 2000 tokens

Model

Recommended: sonnet (deep analysis)

Philosophy

"People dont want a quarter-inch drill. They want a quarter-inch hole." Focus on the underlying need, not the stated solution.

Keep it simple:

Real users, real problems
Evidence over assumptions
Quality over quantity of feedback
Deep understanding over surface-level

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