feat: Add 57 skills from vibeship-spawner-skills

Ported 3 categories from Spawner Skills (Apache 2.0): - AI Agents (21 skills): langfuse, langgraph, crewai, rag-engineer, etc. - Integrations (25 skills): stripe, firebase, vercel, supabase, etc. - Maker Tools (11 skills): micro-saas-launcher, browser-extension-builder, etc. All skills converted from 4-file YAML to SKILL.md format. Source: https://github.com/vibeforge1111/vibeship-spawner-skills
2026-01-19 12:18:43 +01:00
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--- a/skills/ai-wrapper-product/SKILL.md
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+---
+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
+
+```python
+## 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
+```javascript
+// 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
+```javascript
+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`