Individual skill
business-intelligence
Use when a metric (revenue, MRR, margin) needs defining once in a governed semantic layer so every dashboard, report and agent returns the same number, or when an LLM must answer data questions in plain language without hallucinating SQL. NOT chart layout (that is `dashboard`), NOT which KPIs to track (that is `kpi-framework`), NOT a hand-written query (that is `sql`).
skills/business-intelligence/SKILL.md
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- agent-skills Repository topic
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- business-intelligence Declared skill tag
- semantic-layer Declared skill tag
- metrics-layer Declared skill tag
- text-to-sql Declared skill tag
- natural-language-query Declared skill tag
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SKILL.mdcore · 10,042 bytesSKILL.mdcore · 10,042 bytesevals/README.mddoc · 762 bytesevals/cases.yamlasset · 3,176 bytesreferences/authoring-semantic-models.mddoc · 5,535 bytesreferences/wiring-agents-and-apis.mddoc · 3,883 bytesscripts/verify.shruns · 4,908 bytes
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# Business intelligence
Answer business questions over the org's data **through a governed semantic layer** — define each metric once in versioned YAML, then route every "what was revenue last quarter by region" through that layer. Numbers come out consistent, auditable, and the same for everyone. This skill builds the layer and queries it in plain language.
## The one rule
**Never free-hand SQL against raw tables to answer a governed business question. Go through the layer.**
Why, with numbers: in dbt's April 2026 benchmark (ACME Insurance, 11 questions × 20 runs, ~15-table schema), an LLM grounded in a semantic layer scored **98.2% (Claude Sonnet 4.6) / 100% (GPT-5.3 Codex)** vs **90.0% / 84.1%** for raw text-to-SQL on the same schema; on the *unmodeled* schema it was 72.7% vs 64.5%, and a 2023 GPT-4 baseline managed 32.7%. The layer is not bureaucracy — it is the accuracy. The model writing SQL against undecorated tables is the failure mode you are eliminating.
Your job is two motions: (1) **build** the metrics layer (entities, dimensions, measures, metrics) and (2) **query** it — translate a plain-language question into metric + dimensions + grain + filter, never into a hand-written query.
## The four primitives
