Add Data Warehouse Development Kit preset

[branky]

Introduces presets/data-warehouse/ — a spec-kit preset tailored for data warehousing projects. Covers the full SDD workflow with DW-native concepts baked into every template and command.

Templates:

• spec-template.md — data sources, grain statement, dimensional model,
  SCD strategy, business rules, DQ checks, SLA table
• plan-template.md — schema design (fact+dim DDL sketches), SCD table,
  ETL layer definitions, load strategy, idempotency
  design, DQ implementation, data lineage, quickstart
• constitution-template.md — 8 principles: modeling standards, naming
  conventions (fct_/dim_/stg_ + column rules),
  SCD mandates, DQ gates, idempotency, lineage,
  performance, security/compliance
• tasks-template.md — pipeline-phase task list (Setup → Raw → Staging
  → Dimensions → Facts per use case → Ops) with
  DQ-first ordering, parallelism markers, checkpoints,
  and rollback checklist

Commands:

• speckit.specify — DW-aware spec creation; extracts grain, sources,
  consumers, SCD choices, DQ rules, SLAs; validates
  against DW-specific quality checklist
• speckit.plan — designs schema, SCD impl, load strategies,
  idempotency, DQ tool mapping, data-lineage.md,
  source contracts, and quickstart sign-off queries
• speckit.tasks — generates phase-ordered task list from plan+spec
  with DQ-first rule, exact file paths, and ops tasks
• speckit.constitution — guides warehouse constitution creation covering
  platform, modeling philosophy, naming, SCD policy,
  DQ tooling, and governance

https://claude.ai/code/session_017fbvhCN7HykaBHD8KYma22

Description

Testing

• Tested locally with uv run specify --help
• Ran existing tests with uv sync && uv run pytest
• Tested with a sample project (if applicable)

AI Disclosure

• I did not use AI assistance for this contribution
• I did use AI assistance (describe below)