The power of tools like Claude Code comes from giving the AI access to fundamental command-line tools (e.g., `bash`, `grep`). This allows the AI to compose novel solutions and lets product teams define new features using simple English prompts rather than hard-coded logic.

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.

Browser-based ChatGPT cannot execute code or connect to external APIs, limiting its power. The Codex CLI unlocks these agentic capabilities, allowing it to interact with local files, run scripts, and connect to databases, making it a far more powerful tool for real-world tasks.

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.

Claude Code's terminal-based interaction within a specific folder allows it to automatically read and reference local files. This makes "context engineering" drastically faster and more powerful than manually pasting information into a traditional chat interface, as the context is implicitly understood.

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 Claude's "Artifacts" feature to generate interactive, LLM-powered application prototypes directly from a prompt. This allows product managers to test the feel and flow of a conversational AI, including latency and response length, without needing API keys or engineering support, bridging the gap between a static mock and a coded MVP.

The recent leap in AI coding isn't solely from a more powerful base model. The true innovation is a product layer that enables agent-like behavior: the system constantly evaluates and refines its own output, leading to far more complex and complete results than the LLM could achieve alone.

Instead of using Claude's slow and error-prone web UI to generate skills, a more effective workflow is to use an AI-native code editor like Cursor. By providing Cursor with the official documentation link, it can rapidly and reliably generate the entire skill folder structure, including markdown and validation scripts.