AI Health Check

Before you ship an AI feature, it needs to pass 6 checks.

Most AI products fail because PMs skip the basics: no cost model, broken failure UX, terrible data quality. This skill stops you from launching garbage.

Entry Point

When this skill is invoked, start with:

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 AI HEALTH CHECK
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Before shipping an AI feature, it needs to pass 6 checks.

What AI feature are you preparing to launch?

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Usage

/ai-health-check [feature-name]

Examples:

• /ai-health-check "AI product recommendations" - Audit specific feature
• /ai-health-check "email composer AI" - Manual description
• /ai-health-check --pre-launch - Full checklist for current sprint

What Happens

1. Invokes the ai-implementation-auditor agent
2. Asks hard questions about your AI feature
3. Grades each of 6 dimensions: Ready / Risk / Blocker
4. Tells you if you can ship

The 6 Dimensions

Dimension	What It Checks
Model Selection	Did you try simple approaches first?
Data Quality	The thing you're probably ignoring
Cost Modeling	Can you afford this at scale?
Production Monitoring	How will you know if it breaks?
Failure UX	What happens when AI screws up?
System Optimization	Are you measuring the right things?

Verdict Logic

Condition	Verdict
Any Blocker	DON'T SHIP
2+ Risks (no blockers)	NEEDS WORK
0-1 Risks	READY

Sample Output

AI Health Check: Email Composer

Overall Readiness: NEEDS WORK (4/6 dimensions ready)

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Ready: Model Selection, Production Monitoring, System Optimization
Risk: Data Quality, Failure UX
Blocker: Cost Modeling

VERDICT: DON'T SHIP YET

You have 1 blocker:
- No cost model -> Run /ai-cost-check RIGHT NOW

You have 2 risks:
- Data quality strategy undefined
- Failure UX is broken ("Something went wrong" isn't helpful)

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What To Do Now:

Option A: Fix everything (RECOMMENDED)
1. Run /ai-cost-check (10 min)
2. Define data quality strategy (2 hours)
3. Build better failure UX (3 hours)
4. Rerun /ai-health-check

Option B: Ship with known risks
1. Fix the blocker only
2. Ship knowing data quality and failure UX are weak
3. Plan to fix in week 1

Common Blockers

Cost Modeling missing:

"You're about to launch with zero idea if this bankrupts you at scale." Run /ai-cost-check first.

Failure UX broken:

"Something went wrong" tells users nothing. No confidence indicators = users don't know when to trust the AI.

No monitoring plan:

"Launching without monitoring = flying blind."

Philosophy (Chip Huyen)

• "Most AI failures are UX problems, not technical ones."
• "Data quality beats tool selection."
• "Fine-tuning should be your last resort."
• "The gap between a demo and a product is production engineering."

Related Commands

• /ai-cost-check - Detailed cost modeling (run if cost dimension is blocked)
• /start-evals - Set up quality testing
• /four-risks - Overall feature risk assessment

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Best for: Pre-launch validation of AI features Key insight: "Fine-tuning is the last resort. Data quality beats tool selection. Most AI failures are UX problems."