Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add marysatasselshaped667/skills-collection-1 --skill ai-wrapper-productgit clone --depth 1 https://github.com/marysatasselshaped667/skills-collection-1Wrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/marysatasselshaped667/skills-collection-1/ai-wrapper-product)<a href="https://agentmods.dev/skills/marysatasselshaped667/skills-collection-1/ai-wrapper-product"><img src="https://agentmods.dev/badge/skills/marysatasselshaped667/skills-collection-1/ai-wrapper-product/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/marysatasselshaped667/skills-collection-1/ai-wrapper-product"><img src="https://agentmods.dev/badge/skills/marysatasselshaped667/skills-collection-1/ai-wrapper-product.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00062 | $0.01600 |
| Opus 5 | $0.00031 | $0.00800 |
| Sonnet 5 | $0.00012 | $0.00320 |
| Haiku 4.5 | $0.00006 | $0.00160 |
Grade A, and why
ai-wrapper-product scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to ai-wrapper-product — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 279 lines · 62 tokens per session scan A 393dec86d14f
ai-wrapper-product is a skill published in the GitHub repository marysatasselshaped667/skills-collection-1 (1 stars, last pushed yesterday), licensed MIT. It adds 62 tokens to every session and 1,600 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ai-wrapper-product, differing in 0 lines, and is treated as a copy.
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