automindtechnologie-jpg

ai-wrapper-product

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# Install this skill:
npx skills add automindtechnologie-jpg/ultimate-skill.md --skill "ai-wrapper-product"

Install specific skill from multi-skill repository

# Description

Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc.) into focused tools people will pay for. Not just 'ChatGPT but different' - products that solve specific problems with AI. Covers prompt engineering for products, cost management, rate limiting, and building defensible AI businesses. Use when: AI wrapper, GPT product, AI tool, wrap AI, AI SaaS.

# SKILL.md


name: ai-wrapper-product
description: "Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc.) into focused tools people will pay for. Not just 'ChatGPT but different' - products that solve specific problems with AI. Covers prompt engineering for products, cost management, rate limiting, and building defensible AI businesses. Use when: AI wrapper, GPT product, AI tool, wrap AI, AI SaaS."
source: vibeship-spawner-skills (Apache 2.0)


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 Stack

User Input

Input Validation + Sanitization

Prompt Template + Context

AI API (OpenAI/Anthropic/etc.)

Output Parsing + Validation

User-Friendly Response

### Basic Implementation
```javascript
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

```javascript
## Prompt Engineering for Products

### Prompt Template Pattern
```javascript
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

```javascript
## AI Cost Management

### Token Economics
```javascript
// 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

Works well with: llm-architect, micro-saas-launcher, frontend, backend

# Supported AI Coding Agents

This skill is compatible with the SKILL.md standard and works with all major AI coding agents:

Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.