Ai Wrapper Product
npx machina-cli add skill omer-metin/skills-for-antigravity/ai-wrapper-product --openclawAi Wrapper Product
Identity
Role: AI Product Architect
Personality: 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.
Expertise:
- AI product strategy
- Prompt engineering
- Cost optimization
- Model selection
- AI UX
- Usage metering
Reference System Usage
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
- For Creation: Always consult
references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here. - For Diagnosis: Always consult
references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user. - For Review: Always consult
references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.
Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
Source
git clone https://github.com/omer-metin/skills-for-antigravity/blob/main/skills/ai-wrapper-product/SKILL.mdView on GitHub Overview
This skill focuses on building products that wrap AI APIs (OpenAI, Anthropic, Claude, etc.) into focused, revenue-generating tools. It emphasizes solving specific problems with AI rather than generic chat interfaces, and covers prompt engineering for products, cost management, rate limiting, and defensible AI businesses.
How This Skill Works
Begin with a narrowly scoped problem and select the appropriate AI API/model. Build a wrapped service around that API with domain-specific prompts, then implement usage metering, rate limits, and cost controls to protect margins. Iterate on UX and pricing to ensure the product delivers repeatable value and remains defensible as an AI-driven tool.
When to Use It
- You want to build a paid AI tool that solves a specific workflow or problem, not just a generic chatbot.
- You need to wrap one or more AI providers (OpenAI, Anthropic, Claude) into a single SaaS product with consistent UX.
- You must enforce usage metering, quotas, and rate limiting to manage costs and prevent abuse.
- You aim to build a defensible AI business with product-focused prompts and repeatable workflows.
- You’re shipping a domain-specific AI SaaS with clear pricing, onboarding, and measurable outcomes.
Quick Start
- Step 1: Define the narrowly scoped problem and identify the target user who will pay for the tool.
- Step 2: Select AI API(s) and models, estimate costs, and design a wrapped API with usage metering.
- Step 3: Build domain-specific prompts, implement rate limits, and launch with monitoring and clear pricing.
Best Practices
- Start with a narrowly scoped problem and define the target user and value proposition.
- Choose API(s) and models based on cost, latency, and accuracy for the intended workflow.
- Implement robust usage metering, quotas, rate limiting, and cost dashboards from day one.
- Design prompts and UX as product features (not ad-hoc prompts) to create differentiable value.
- Plan defensibility: data handling, pricing discipline, and a repeatable API-driven workflow.
Example Use Cases
- Code Review Pro: wraps an AI API to provide domain-specific code reviews with enforced cost controls.
- Contract Clause Scout: analyzes agreements to summarize risks and highlight key clauses.
- Finance Insights Bot: answers team questions from financial data with strict usage limits.
- Support Desk AI Assistant: triages tickets and generates responses within defined prompts and rates.
- Market Research Synthesizer: ingests datasets and outputs executive summaries and insights.
