# 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
```json
{
  "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