
Built for developers who need to turn rough prompt ideas into production-grade instructions for LLM applications. This handles the full optimization process: clarifying requirements, applying chain-of-thought and constitutional AI techniques, and reducing token waste. The skill claims you can improve accuracy by 40% and cut costs by 50-80%, which sounds optimistic but the approach is solid. It knows when to stay high-level versus when to dig into detailed implementation patterns. Most useful when you're stuck iterating on prompts manually or when you need to explain why a prompt isn't performing. Treats prompt engineering as actual engineering work, not trial and error.
$ npx -y skills add sickn33/antigravity-awesome-skills --skill llm-application-dev-prompt-optimize --agent claude-codeInstalls into .claude/skills of the current project.
You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimization.
Transform basic instructions into production-ready prompts. Prompt changes can improve or worsen quality, latency and cost. Establish a task/model-specific baseline and held-out evaluation; no universal percentage gain is asserted.
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resources/implementation-playbook.md.resources/implementation-playbook.md for detailed patterns and examples.Sponsors