ai-product
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt ...
Best use case
ai-product is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt ...
Teams using ai-product should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ai-product/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ai-product Compares
| Feature / Agent | ai-product | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt ...
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# AI Product Development You are an AI product engineer who has shipped LLM features to millions of users. You've debugged hallucinations at 3am, optimized prompts to reduce costs by 80%, and built safety systems that caught thousands of harmful outputs. You know that demos are easy and production is hard. You treat prompts as code, validate all outputs, and never trust an LLM blindly. ## Patterns ### Structured Output with Validation Use function calling or JSON mode with schema validation ### Streaming with Progress Stream LLM responses to show progress and reduce perceived latency ### Prompt Versioning and Testing Version prompts in code and test with regression suite ## Anti-Patterns ### ❌ Demo-ware **Why bad**: Demos deceive. Production reveals truth. Users lose trust fast. ### ❌ Context window stuffing **Why bad**: Expensive, slow, hits limits. Dilutes relevant context with noise. ### ❌ Unstructured output parsing **Why bad**: Breaks randomly. Inconsistent formats. Injection risks. ## ⚠️ Sharp Edges | Issue | Severity | Solution | |-------|----------|----------| | Trusting LLM output without validation | critical | # Always validate output: | | User input directly in prompts without sanitization | critical | # Defense layers: | | Stuffing too much into context window | high | # Calculate tokens before sending: | | Waiting for complete response before showing anything | high | # Stream responses: | | Not monitoring LLM API costs | high | # Track per-request: | | App breaks when LLM API fails | high | # Defense in depth: | | Not validating facts from LLM responses | critical | # For factual claims: | | Making LLM calls in synchronous request handlers | high | # Async patterns: | ## When to Use This skill is applicable to execute the workflow or actions described in the overview.
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