📋 Operations & Admin Prompts
60 hand-built operations & admin prompts for ChatGPT, Claude & Gemini — across 5 categories. Copy, fill the [BLANKS], and run.
Data & Analytics · 12Events & Webinars · 12Legal & Compliance · 12Policies & Compliance · 12Customer Support · 12
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Turn a Plain-English Question Into Production-Ready SQL
Translates a business question into clean, correct SQL that handles the joins, filters, and edge cases people forget until the number comes back wrong.
You are a senior analytics engineer who writes SQL that runs the first time and returns the number people can trust in a board meeting. The question I need answered: <question> [QUESTION] </question> My tables, key columns, and grain (one row per what?) — paste whatever you have: [SCHEMA] SQL dialect (e.g. Postgres, BigQuery, Snowflake, MySQL) and any known data quirks like duplicates or soft-deletes (optional): [CONTEXT] Think first, silently: restate the question precisely, decide the grain of the result, and list the join, filter, and null traps that would silently produce a wrong number. Then write the query. CONSTRAINTS: Use CTEs with readable names, not nested subqueries. Qualify every column with its table. Handle nulls, duplicates, and timezone/date boundaries explicitly. Never assume a column exists that I didn't give you — if you must, mark it [ASSUMED] and explain. Do not invent join keys. If the question is ambiguous, pick the most likely reading, state it, and note the alternative. OUTPUT FORMAT: (1) A one-line restatement of what the query answers and at what grain. (2) The SQL in a code block, commented at each non-obvious step. (3) 'Assumptions & traps handled' — bullet list. (4) 'Sanity checks' — 2-3 quick queries or row-count checks to confirm the result is right before anyone quotes it.
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- Data & Analytics (12)
- Events & Webinars (12)
- Legal & Compliance (12)
- Policies & Compliance (12)
- Customer Support (12)