query
SolidUse when the user wants to query or analyze data through the Honeydew semantic layer — including natural language analysis questions, deep multi-step investigations, and structured queries. For model/field discovery use the model-exploration skill.
Install
Quality Score: 82/100
Skill Content
Details
- Author
- honeydew-ai
- Repository
- honeydew-ai/honeydew-ai-coding-agents-plugins
- Created
- 5 months ago
- Last Updated
- 2 days ago
- Language
- Shell
- License
- Apache-2.0
Bundled in these plugins
Similar Skills
Semantically similar based on skill content — not just same category
model-exploration
Use when exploring Honeydew semantic layer, discovering entities/fields, setting up workspace and branch context, or running structured queries to spot-check field values. Any question about the data itself — "why", "how", trends, root cause, anything needing multiple steps — belongs to the query skill's deep analysis, including when already mid-exploration. For creating metrics use metric-creation skill. For creating attributes use attribute-creation skill.
query-debugging
Use when the user wants to review or inspect past query executions in Honeydew — what ran, from which client (BI tools, SQL interface, MCP, deep analysis), the semantic definition or compiled SQL behind a run, who ran it and when — and to debug failures. Uses the list_query_history MCP tool. For running new queries or analysis use the query skill.
agami-query
Answers natural-language questions about the user's database. Loads the agami semantic model (subject areas, tables, columns, relationships with join cardinality, entities, metrics) and few-shot examples from <artifacts_dir>/<profile>/, generates SQL via the examples-first traversal (pick subject area → match examples → resolve entities/metrics → compound table context), executes it locally via the user's chosen tool (psql / mysql / snowsql / sqlite3 native CLI, DuckDB binary, or the Python driver `execute_sql.py` — which runs the scope gates and reports fan-trap/chasm-trap and aggregation findings on the receipt), returns results as a markdown table with optional CSV export, and renders Chart.js HTML charts on request. All execution is local — no data leaves the machine.