Examples Audit

Examples Audit

Analyze mock data and examples for cultural assumptions, with emphasis on understanding what these choices communicate.

Philosophy

This is not about finding "Christmas" and replacing it with "holiday". It's about asking:

"What do our examples say about who we think our users are?"

Every example is a choice. When your demo shows a user ordering a hamburger for their Christmas party, paying with a credit card, and shipping to their house in California, you're painting a picture of your "default user." Everyone else is an edge case.

Scope

The user may specify a file path, glob pattern, or directory. If not specified, ask what they'd like to check.

Config Integration

Before starting, check for .inclusion-config.md in the project root.

If it exists:

1. Read scope decisions and acknowledged findings
2. Skip acknowledged findings (note them in output)
3. Respect scope decisions (e.g., if US-only, don't flag US address examples)
4. Note at the top of output: "Config loaded: .inclusion-config.md"

Process

1. Read and Understand

First, read the content to understand:

• What is this? (test data, documentation, marketing, UI copy)
• Who sees this? (developers only, or end users?)
• What story are the examples telling?

2. Look for Patterns

Don't just flag individual terms. Look for patterns:

• Do ALL the examples assume US location?
• Do ALL the food references assume meat-eating?
• Do ALL the family references assume nuclear families?
• Do ALL the payment examples assume credit cards?

A single Christmas reference in otherwise diverse examples is different from a codebase where every example assumes Christian, Western, affluent users.

3. Understand the Assumptions

For each pattern, ask what it assumes:

Holiday/Event examples

• Christmas, Easter, Thanksgiving → Assumes Christian/Western holidays
• "Holiday season" in December → Assumes Northern hemisphere
• Birthday celebrations → Not all cultures celebrate birthdays
• Mother's Day, Father's Day → Can be painful; not universal

Food/Dietary examples

• Hamburgers, bacon, steak → Assumes meat-eating
• Pork → Excludes halal, kosher observers
• Beef → Excludes Hindu observers
• Alcohol → Excludes many religious/personal practices

Location/Address examples

• State, ZIP code → US-specific
• "Your home" → Assumes stable housing
• "Your car" → Assumes car ownership

Payment examples

• Credit card required → Excludes unbanked users
• USD prices → US-centric
• "Premium" tiers → Assumes disposable income

Family examples

• Mother/Father → Excludes diverse family structures
• Spouse → Assumes marriage
• "Your kids" → Assumes children; can be painful

4. Assess Impact

High impact (shapes perception):

• Onboarding flows and first-run experiences
• Marketing materials and landing pages
• Documentation that users read
• Demo data in screenshots/videos

Medium impact (still visible):

• Error messages and help text
• Email templates
• In-app examples and placeholders

Lower impact (internal):

• Unit test fixtures
• Development seed data
• Internal documentation

Reference

For comprehensive checklists, see references/examples-checklist.md.

Output Format

## Examples Analysis: [path]

### Overview

[What kind of content is this? What story do the examples currently tell? Who is the "default user" these examples assume?]

### Patterns Found

#### [Assumption Category]

**The pattern:** [What you observed across multiple examples]

**What this assumes:** [The implicit assumption about users]

**Who this might exclude:** [Specific groups]

**Examples found:**

1. `[file]:[line]` - `[content]`
2. `[file]:[line]` - `[content]`

**Suggestions:**
- [Neutral alternative]
- [Or how to add diversity without removing everything]

#### [Next category...]

### What's Working Well

[Note any existing diversity or thoughtful choices]

### Recommendations

**Quick wins:**
- [Easy changes with high impact]

**Larger effort:**
- [Systemic changes that would help]

**Consider:**
- [Questions for the team to discuss]

### Summary

- **Assumption patterns found:** [count]
- **Visibility level:** [High / Medium / Low]
- **Recommendation:** [Overall guidance]

What Makes This Different From a Linter

A linter would flag "Christmas". You should:

1. See the pattern - One Christmas reference vs. every example assuming Western holidays
2. Understand the context - Is this a greeting card app where holidays are the point?
3. Assess visibility - User-facing marketing vs. internal test data
4. Suggest proportionally - Sometimes add diversity; sometimes use neutral terms

Your value is understanding what the examples communicate as a whole.

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