Office DuckDB Query
SkillDatabases & dataRun SQL-style analysis across local files with DuckDB, using bounded results and file-aware query planning.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Office DuckDB Query skill
What this skill tells your AI
The instructions your AI receives, as published by contextgo/contextgo in src/process/resources/skills/office-analyst-pack/skills/office-duckdb-query/SKILL.md and read by ahel’s review.
This skill is absorbed from the real DuckDB official skill design, then adapted for ContextGo office workflows.
What it is for
Use DuckDB as the query engine when office work turns into:
- multiple data files
- large tabular exports
- SQL-style slicing and aggregation
- direct file querying without importing into a separate database first
Use when
- The user wants to ask questions across CSV, Parquet, JSON, Excel, or similar files.
- A normal spreadsheet summary is too weak for the scale or shape of the data.
- SQL is the clearest way to compare, aggregate, filter, or join the inputs.
Do not use when
- The task is a simple single-sheet inspection that can be answered faster with workbook reading.
- The user mainly needs document extraction rather than data querying.
Core operating model
1. Choose the query mode
Use one of these modes:
- direct file query
- query against an attached DuckDB database
- mixed mode where a database and files both matter
For office analysis, direct file query is the default unless the user already has a DuckDB database.
2. Verify DuckDB is available
Check for the CLI first. If missing, use office-duckdb-install.
3. Prefer Friendly SQL
Adopt DuckDB-friendly patterns:
FROM 'file.csv'GROUP BY ALLORDER BY ALLSELECT * EXCLUDE (...)DESCRIBESUMMARIZE
This keeps queries short, readable, and easy to iterate.
4. Bound the result set
Before running a query that could explode in size:
- inspect row count
- add
LIMIT - aggregate first if possible
Do not dump a million-row result into the conversation.
5. Interpret, do not just print
After executing a query, explain:
- what the result means
- what caveat matters
- what follow-up query is most useful next
Error handling expectations
- missing DuckDB -> install path
- missing extension -> install and load the needed extension, then retry
- file not found -> resolve the path before guessing
- syntax error -> fix the query instead of giving up
Friendly SQL reminders
Prefer:
- direct file reads like
FROM 'sales.parquet' count()instead ofcount(*)DESCRIBEfor schemaSUMMARIZEfor quick profiling- explicit bounded outputs
Output format
Return:
1. Query plan
- files or databases involved
- query mode
- assumptions
2. SQL used
- final SQL or query shape
3. Results
- bounded result
- interpretation
4. Next useful query
- one or two high-value follow-ups
Use together with
office-duckdb-read-fileto inspect a file before queryingoffice-duckdb-installif DuckDB or extensions are missingoffice-source-reconciliationwhen query results must be matched against office documents
Signals
- GitHub stars
- 54
- Forks
- 5
- Last commit
- May 2026
Advanced
- Catalog kind
- skill
- Gateway key
office-duckdb-query- Source
- github.com/contextgo/contextgo