While Claude's built-in 'create skill' tool is clunky, its output reveals a highly structured template for effective prompts. It includes decision trees, clarifying questions for the user, and keywords for invocation, serving as an invaluable guide for building robust skills without starting from scratch.

LLMs often get stuck or pursue incorrect paths on complex tasks. "Plan mode" forces Claude Code to present its step-by-step checklist for your approval before it starts editing files. This allows you to correct its logic and assumptions upfront, ensuring the final output aligns with your intent and saving time.

Before delegating a complex task, use a simple prompt to have a context-aware system generate a more detailed and effective prompt. This "prompt-for-a-prompt" workflow adds necessary detail and structure, significantly improving the agent's success rate and saving rework.

Instead of spending time trying to craft the perfect prompt from scratch, provide a basic one and then ask the AI a simple follow-up: "What do you need from me to improve this prompt?" The AI will then list the specific context and details it requires, turning prompt engineering into a simple Q&A session.

Anthropic suggests that LLMs, trained on text about AI, respond to field-specific terms. Using phrases like 'Think step by step' or 'Critique your own response' acts as a cheat code, activating more sophisticated, accurate, and self-correcting operational modes in the model.

Instead of managing prompts in a separate library, save them as custom commands directly within your Claude Code project folder. This lets you trigger complex, multi-file prompts with a simple command (e.g., `/meeting_notes`), embedding powerful, recurring workflows directly into your development environment.

Use the Claude chat application for deep research on technical architecture and best practices *before* coding. It can research topics for over 10 minutes, providing a well-summarized plan that you can then feed into a dedicated coding tool like Cursor or Claude Code for implementation.

Many AI tools expose the model's reasoning before generating an answer. Reading this internal monologue is a powerful debugging technique. It reveals how the AI is interpreting your instructions, allowing you to quickly identify misunderstandings and improve the clarity of your prompts for better results.

Instead of only giving instructions, ask ChatGPT to first ask you questions about your goal. This leverages the AI's knowledge of what information it needs to produce the best possible, most tailored output for your specific request.