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 engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.
npx skills add majiayu000/claude-skill-registry --skill ai-product --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Patterns
- Structured Output with Validation
- Streaming with Progress
- Prompt Versioning and Testing
- Anti-Patterns
- ❌ Demo-ware
- ❌ Context window stuffing
- ❌ Unstructured output parsing
- ⚠️ Sharp Edges
What does the ai-product 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 engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ai-product --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.