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.
npx skills add majiayu000/claude-skill-registry --skill ai-product-sickn33-agentic-awesome-skil --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 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. ## Principles - LLMs are probabilistic, not deterministic | Description: The same input can give different outputs. Design for variance. Add validation layers. Never trust output blindly. Build for the edge cases that will definitely happen. | Examples: Good: Validate LLM output against schema, fallback to human review | Bad: Parse LLM response and use directly in database - Prompt engineering is product engineering | Description: Prompts are code. Version them. Test them. A/B test them. Document them. One word change can flip behavior. Treat them with the same rigor as code. | Examples: Good: Prompts in version control, regression tests, A/B testing | Bad: Prompts inline in code, changed ad-hoc, no testing - RAG over fine-tuning for most use cases | Description: Fine-tuning is expensive, slow, and hard to update. RAG lets you add knowledge without retraining. Start wi
- Principles
- Patterns
- Structured Output with Validation
- Streaming with Progress
- Prompt Versioning and Testing
- Caching Expensive Operations
- Circuit Breaker for LLM Failures
- RAG with Hybrid Search
- Sharp Edges
- Trusting LLM output without validation
- User input directly in prompts without sanitization
- 1. Separate user input:
- 2. Input sanitization:
- 3. Output filtering:
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ai-product-sickn33-agentic-awesome-skil --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.