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.

When setting up an AI data agent, don't invent example queries from scratch. Instead, bootstrap the process by analyzing your database logs (e.g., from Snowflake) to find the most popular, real-world queries already being run against your key tables. This ensures the AI learns from actual usage patterns.

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.

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.

AI data agents can misinterpret results from large tables due to context window limits. The solution is twofold: instruct the AI to use query limits (e.g., `LIMIT 1000`), and crucially, remind it in subsequent prompts that the data it is analyzing is only a sample, not the complete dataset.

To enable AI tools like Cursor to write accurate SQL queries with minimal prompting, data teams must build a "semantic layer." This file, often a structured JSON, acts as a translation layer defining business logic, tables, and metrics, dramatically improving the AI's zero-shot query generation ability.

To safely empower non-technical users with self-service analytics, use AI 'Skills'. These are pre-defined, reusable instructions that act as guardrails. A skill can automatically enforce query limits, set timeouts, and manage token usage, preventing users from accidentally running costly or database-crashing queries.

Unlike Claude Projects where the LLM decides how to use tools, Skills execute predefined scripts. This gives users precise control over data analysis and repeatable tasks, ensuring consistent, accurate results and overcoming the common issue of non-deterministic AI outputs.

Instead of asking one-off questions, build a detailed, pre-written prompt (a "shortcut") within an AI browser. This standardizes your analysis framework, allowing you to instantly reverse-engineer any company's marketing strategy with a single command, making deep research scalable and repeatable.