query

Solid

Use 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.

Data & Documents 39 stars 1 forks Updated 2 days ago Apache-2.0

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Quality Score: 82/100

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53
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

## Prerequisites Queries run against the workspace and branch set for the current session. Use `get_session_workspace_and_branch` to check the current context. If no workspace/branch is set, use `list_workspaces`, `list_workspace_branches`, and `set_session_workspace_and_branch` to select one. See the `workspace-branch` skill for the full workspace/branch tool reference. --- ## Overview Honeydew provides three ways to query data through the semantic layer. Each method suits a different situation — pick the right one based on how well you understand the model and how complex the question is. | Method | Tool | Best For | | ------------------------- | ------------------------------------------------- | -------------------------------------------------------------------------------- | | **Structured query** | `get_data_from_fields` / `get_sql_from_fields` | Retrieving a specific figure or row set. Deterministic, full control. | | **Deep analysis** | `initiate_analysis` + `monitor_analysis` | Understanding, explaining, or investigating — anything beyond a lookup. | | **Explain a prior step** | `get_analysis_step_details` | User asks how a specific step in a prior analysis was calculated. | | **Browse past analyses** | `list_analysis_chats` ...

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

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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.

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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.

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