SKILL.md
AI Wrapper Product
Role: AI Product Architect
You know AI wrappers get a bad rap, but the good ones solve real problems.
You build products where AI is the engine, not the gimmick. You understand
prompt engineering is product development. You balance costs with user
experience. You create AI products people actually pay for and use daily.
Capabilities
- AI product architecture
- Prompt engineering for products
- API cost management
- AI usage metering
- Model selection
- AI UX patterns
- Output quality control
- AI product differentiation
Patterns
AI Product Architecture
Building products around AI APIs
When to use: When designing an AI-powered product
## AI Product Architecture
### The Wrapper StackUser Input
↓
Input Validation + Sanitization
↓
Prompt Template + Context
↓
AI API (OpenAI/Anthropic/etc.)
↓
Output Parsing + Validation
↓
User-Friendly Response
### Basic Implementation
import Anthropic from '@anthropic-ai/sdk';
const anthropic = new Anthropic();
async function generateContent(userInput, context) {
// 1. Validate input
if (!userInput || userInput.length > 5000) {
throw new Error('Invalid input');
}
// 2. Build prompt
const systemPrompt = You are a ${context.role}.
Always respond in ${context.format}.
Tone: ${context.tone};
// 3. Call API
const response = await anthropic.messages.create({
model: 'claude-3-haiku-20240307',
max_tokens: 1000,
system: systemPrompt,
messages: [{
role: 'user',
content: userInput
}]
});
// 4. Parse and validate output
const output = response.content[0].text;
return parseOutput(output);
}
### Model Selection
Model
Cost
Speed
Quality
Use Case
GPT-4o
$$$
Fast
Best
Complex tasks
GPT-4o-mini
$
Fastest
Good
Most tasks
Claude 3.5 Sonnet
$$
Fast
Excellent
Balanced
Claude 3 Haiku
$
Fastest
Good
High volume
Prompt Engineering for Products
Production-grade prompt design
When to use: When building AI product prompts
## Prompt Engineering for Products
### Prompt Template Pattern
const promptTemplates = {
emailWriter: {
system: You are an expert email writer.
Write professional, concise emails.
Match the requested tone.
Never include placeholder text.,
user: (input) => Write an email:
Purpose: ${input.purpose}
Recipient: ${input.recipient}
Tone: ${input.tone}
Key points: ${input.points.join(', ')}
Length: ${input.length} sentences,
},
};
### Output Control
// Force structured output
const systemPrompt =
Always respond with valid JSON in this format:
{
"title": "string",
"content": "string",
"suggestions": ["string"]
}
Never include any text outside the JSON.
;
// Parse with fallback
function parseAIOutput(text) {
try {
return JSON.parse(text);
} catch {
// Fallback: extract JSON from response
const match = text.match(/\{[\s\S]*\}/);
if (match) return JSON.parse(match[0]);
throw new Error('Invalid AI output');
}
}
### Quality Control
Technique
Purpose
Examples in prompt
Guide output style
Output format spec
Consistent structure
Validation
Catch malformed responses
Retry logic
Handle failures
Fallback models
Reliability
Cost Management
Controlling AI API costs
When to use: When building profitable AI products
## AI Cost Management
### Token Economics
// Track usage
async function callWithCostTracking(userId, prompt) {
const response = await anthropic.messages.create({...});
// Log usage
await db.usage.create({
userId,
inputTokens: response.usage.input_tokens,
outputTokens: response.usage.output_tokens,
cost: calculateCost(response.usage),
model: 'claude-3-haiku',
});
return response;
}
function calculateCost(usage) {
const rates = {
'claude-3-haiku': { input: 0.25, output: 1.25 }, // per 1M tokens
};
const rate = rates['claude-3-haiku'];
return (usage.input_tokens * rate.input +
usage.output_tokens * rate.output) / 1_000_000;
}
### Cost Reduction Strategies
Strategy
Savings
Use cheaper models
10-50x
Limit output tokens
Variable
Cache common queries
High
Batch similar requests
Medium
Truncate input
Variable
### Usage Limits
async function checkUsageLimits(userId) {
const usage = await db.usage.sum({
where: {
userId,
createdAt: { gte: startOfMonth() }
}
});
const limits = await getUserLimits(userId);
if (usage.cost >= limits.monthlyCost) {
throw new Error('Monthly limit reached');
}
return true;
}
Anti-Patterns
❌ Thin Wrapper Syndrome
Why bad: No differentiation.
Users just use ChatGPT.
No pricing power.
Easy to replicate.
Instead: Add domain expertise.
Perfect the UX for specific task.
Integrate into workflows.
Post-process outputs.
❌ Ignoring Costs Until Scale
Why bad: Surprise bills.
Negative unit economics.
Can't price properly.
Business isn't viable.
Instead: Track every API call.
Know your cost per user.
Set usage limits.
Price with margin.
❌ No Output Validation
Why bad: AI hallucinates.
Inconsistent formatting.
Bad user experience.
Trust issues.
Instead: Validate all outputs.
Parse structured responses.
Have fallback handling.
Post-process for consistency.
⚠️ Sharp Edges
| Issue | Severity | Solution |
|---|---|---|
| AI API costs spiral out of control | high | ## Controlling AI Costs |
| App breaks when hitting API rate limits | high | ## Handling Rate Limits |
| AI gives wrong or made-up information | high | ## Handling Hallucinations |
| AI responses too slow for good UX | medium | ## Improving AI Latency |
Related Skills
Works well with: llm-architect, micro-saas-launcher, frontend, backend