This covers the gap between LLM demos and production systems. You get patterns for structured output validation, prompt versioning, RAG with hybrid search, and circuit breakers for API failures. The principles are practical: treat prompts like code with version control and tests, design for LLM latency with streaming, cache aggressively because costs add up fast. The sharp edges section is worth the price alone. It walks through actual failure modes like prompt injection and malformed JSON responses with specific fixes using Zod schemas and message separation. If you're shipping anything beyond a prototype that calls an LLM API, you'll recognize these problems immediately.
npx -y skills add sickn33/antigravity-awesome-skills --skill ai-product --agent claude-codeInstalls into .claude/skills of the current project.
Select a file.
sickn33/antigravity-awesome-skills
moizibnyousaf/ai-agent-skills
github/awesome-copilot