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.

Claude Skills aren't limited to natural language instructions; they can reference and execute Python scripts. This enables developers to enforce consistency for technical tasks like data cleaning or validation, preventing the variability that occurs when the LLM generates code on its own.

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 prompting a specialized AI tool directly, experts employ a meta-workflow. They first use a general LLM like ChatGPT or Claude to generate a detailed, context-rich 'master prompt' based on a PRD or user story, which they then paste into the specialized tool for superior results.

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.

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.

The tangible asset for a Claude Skill is surprisingly low-tech: a folder containing a 'skills.md' file and other optional resources. This folder is either referenced by Claude in a local directory or zipped and uploaded to the web UI, demystifying the creation process for non-engineers.