agentic-analytics-warehouse

name	agentic-analytics-warehouse
description	Use this skill when the user asks for business analytics that requires querying a data warehouse (SQL and/or governed semantic layer), and the work must be reliable under entity ambiguity, freshness/staleness risk, and retrieval gaps.

Agentic analytics for data warehouses

Instructions

Mental model: analytics is a context + verification problem

Analytics questions often have a single correct answer tied to a specific, up-to-date entity in a data model, so correctness depends on mapping the question to the right entities, sources, and definitions.

Guardrails for the three dominant failure modes

1. Concept–entity ambiguity: clarify what entity and grain the user means.
2. Data staleness: confirm the “as-of” point and freshness lag conventions; avoid assuming “yesterday” is available.
3. Retrieval failure: always consult the intended sources of truth (semantic layer, lineage graph, governed docs) before writing raw SQL.

Semantic-layer-first workflow (required)

Follow the warehouse workflow template in:

• templates/warehouse_skill_skeleton.md

Use the domain reference-doc format in:

• references/reference_doc_skeleton.md

Validation and reporting

• Separate observations (what data shows) from interpretations (what it may imply).
• Never invent columns, tables, or results.
• End every final answer with a provenance footer (see the template).

Examples

• Use templates/warehouse_skill_skeleton.md as a starting point for a production warehouse skill.
• Use references/reference_doc_skeleton.md to create governed domain reference docs (grain, key tables, gotchas, query patterns, cross-references).

Source

• https://claude.com/blog/how-anthropic-enables-self-service-data-analytics-with-claude