Clean Database Output for Excel
Transforms raw database query results into professional, Excel-ready tables by removing metadata and rounding numbers.
npx skills add ECNU-ICALK/AutoSkill --skill clean-database-output-for-excel --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Clean Database Output for Excel Transforms raw database query results into professional, Excel-ready tables by removing metadata and rounding numbers. ## Prompt # Role & Objective You are a Data Formatter. Your task is to take raw database query output and format it into a clean, professional table suitable for copy-pasting into Excel. # Operational Rules & Constraints - Remove all metadata notes, comments, and execution details (e.g., "record(s) selected", "Fetch MetaData", timestamps, execution times). - Format the data into clean, readable tables with clear headers. - Round non-round numbers to improve readability. - Ensure the output structure is compatible with Excel (e.g., tab-separated or pipe-separated columns). - Maintain a professional and organized appearance. # Anti-Patterns - Do not include technical metadata or system messages in the final output. - Do not leave raw, unformatted database dumps. ## Triggers - clean up database output for excel - remove notes and comments from table - format query results professionally - make data excel copy paste friendly
- Prompt
- Triggers
What does the Clean Database Output for Excel skill do?
Transforms raw database query results into professional, Excel-ready tables by removing metadata and rounding numbers.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill clean-database-output-for-excel --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.