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.

Instead of manually providing context in each prompt, use Claude Code's 'append system prompt' command. This preloads crucial information, like architectural diagrams, at the start of a session, leading to faster and more accurate AI responses without repeated file reads.

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.

The easiest way to teach Claude Code is to instruct it: "Don't make this mistake again; add this to `claude.md`." Since this file is always included in the prompt context, it acts as a permanent, evolving set of instructions and guardrails for the AI.

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.

For recurring AI tasks, such as loading project-specific diagrams or switching models in Claude Code, create short shell aliases (e.g., 'cdi' for 'Claude diagram load'). This avoids retyping long commands and allows you to quickly switch contexts or modes.

The terminal-first interface of Claude Code wasn't a deliberate design choice. It emerged organically from prototyping an API client in the terminal, which unexpectedly revealed the power of giving an AI model direct access to the same tools (like bash) that a developer uses.

Anthropic's Claude models are specifically trained on XML. By structuring system instructions using XML tags (e.g., <role>, <instructions>), you align with the model's training data. This provides better organization and can unlock additional functionality and more reliable outputs compared to using plain text prompts.