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- Duckdb Sql
duckdb-sql_skill
- Python
0
GitHub Stars
2
Bundled Files
2 months ago
Catalog Refreshed
4 months ago
First Indexed
Readme & install
Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.
Installation
Preview and clipboard use veilstrat where the catalogue uses aiagentskills.
npx veilstrat add skill robdmc/claude_skills --skill duckdb-sql- README.md4.6 KB
- SKILL.md39.4 KB
Overview
This skill helps you generate, review, and improve DuckDB SQL queries for .ddb, CSV, and Parquet sources. It prioritizes existing project documentation in duckdb_sql_assets/ and is display-only by default, producing ready-to-run queries and guidance for in-memory DuckDB sessions.
How this skill works
On every request the skill first checks duckdb_sql_assets/ for tables_inventory.json, schema_*.sql, and data_dictionary.md and uses those files as the single source of truth. It produces DuckDB-compliant SQL (including ATTACH preambles for .ddb files) and never runs queries unless you explicitly request execution. For new projects it guides you through a controlled initialization flow to generate assets and schema files.
When to use it
- You need a DuckDB SQL query written for .ddb, .csv, or .parquet data.
- You want to explore or document what tables and fields exist in a project.
- You need an existing SQL reviewed or optimized for DuckDB.
- You want safe, copy-paste-ready queries with proper ATTACH and path conventions.
- You want to initialize duckdb assets (use initialization trigger phrases).
Best practices
- Always let the skill read duckdb_sql_assets/ first — those files are the source of truth.
- Default behavior is to display SQL only; explicitly request 'run', 'execute', or 'show results' to run queries.
- Use relative paths by default; only use absolute paths when you explicitly provide them.
- Include ATTACH statements for .ddb files using the _db_<slug> alias convention.
- Cross-reference data_dictionary.md and schema_*.sql when writing joins, enums, and filters.
Example use cases
- Generate a query that joins a table inside sales.ddb with a transactions CSV and filters by recent dates.
- Review and optimize a provided SELECT with joins and GROUP BY for DuckDB performance.
- Explain which tables and key fields exist using tables_inventory.json and data_dictionary.md.
- Initialize duckdb_sql_assets/ for a new project by providing file paths or glob patterns.
- Detect likely enum columns during initial asset creation and add them to the data dictionary.
FAQ
I will read tables_inventory.json, schema_*.sql, and data_dictionary.md and generate queries using those assets. I will not access .ddb files directly or re-run schema extraction.
Will you execute queries?
No — I only generate and display SQL by default. I will execute only if you explicitly ask me to run a query or show results.