Gold Layer Design

Gold Layer Design Orchestrator

This skill orchestrates the complete Gold layer design process, ensuring all mandatory deliverables are created with proper dependencies on specialized Gold layer skills.

When to Use This Skill

• Designing a Gold layer from scratch for a new project
• Creating dimensional models (facts and dimensions)
• Documenting business processes and data lineage
• Preparing for Gold layer implementation (before 03b-gold-layer-setup-prompt)
• Generating mandatory documentation (Business Onboarding Guide, Source Table Mapping, Column Lineage CSV)

Critical Dependencies (Read at Indicated Phase)

This skill orchestrates the following skills. Each MUST be read and followed at the phase where it is invoked:

🔴 Non-Negotiable Defaults (YAML Schemas MUST Encode These)

The design phase produces YAML schemas that drive all downstream table creation. These YAML schemas MUST include the following properties so that setup_tables.py generates compliant DDL.

# ✅ CORRECT: Every Gold YAML schema MUST include these
table_name: dim_example
clustering: auto  # 🔴 MANDATORY (generates CLUSTER BY AUTO)
table_properties:
  delta.enableChangeDataFeed: "true"     # 🔴 MANDATORY
  delta.enableRowTracking: "true"        # 🔴 MANDATORY
  delta.autoOptimize.optimizeWrite: "true"
  delta.autoOptimize.autoCompact: "true"
  layer: "gold"

Note: Serverless and Environments V4 are job-level concerns enforced by gold/01-gold-layer-setup and databricks-asset-bundles during the implementation phase, not during design.

Quick Start (4-8 hours)

Deliverables Checklist

ERD Organization (based on table count):

• Master ERD (erd_master.md) - ALWAYS required
• Domain ERDs (erd/erd_{domain}.md) - If 9+ tables
• Summary ERD (erd_summary.md) - If 20+ tables

YAML Schemas (organized by domain):

• gold_layer_design/yaml/{domain}/dim_*.yaml - Dimension schemas
• gold_layer_design/yaml/{domain}/fact_*.yaml - Fact schemas

Mandatory Documentation (ALL required):

• BUSINESS_ONBOARDING_GUIDE.md - Business processes + real-world stories
• COLUMN_LINEAGE.csv - Machine-readable column lineage
• COLUMN_LINEAGE.md - Human-readable lineage
• SOURCE_TABLE_MAPPING.csv - Source table inclusion/exclusion rationale
• DESIGN_SUMMARY.md - Design decisions
• DESIGN_GAP_ANALYSIS.md - Coverage analysis
• README.md - Navigation hub

Advisory (only when an industry reference is provided):

• INDUSTRY_CROSSWALK.csv - Industry entity ↔ Gold table crosswalk (from 08-industry-alignment)
• INDUSTRY_ALIGNMENT.md - Industry coverage scorecard (from Validation 6)

ERD Organization Decision

Working Memory Management

This orchestrator spans 9 phases over 4-8 hours. To maintain coherence without context pollution:

After each phase, persist a brief summary note capturing:

• Phase 0: Table inventory dict, entity classifications (dim/fact/bridge), FK relationships, suggested domains
• Phase 1: Project context (name, schemas, use cases, stakeholders)
• Phase 2: Dimensional model: dims, facts, measures, relationships, bus matrix, domain assignments
• Phase 3: ERD file paths, strategy used (master-only / master+domain / full)
• Phase 4: YAML file paths per domain, schema count, lineage gaps
• Phases 5-7: Output file paths (COLUMN_LINEAGE.csv, BUSINESS_ONBOARDING_GUIDE.md, SOURCE_TABLE_MAPPING.csv)
• Phase 8: Validation pass/fail summary, inconsistencies to fix

What to keep in working memory: Current phase's design-worker skill, the table inventory dict (Phase 0), and previous phase's summary. Discard intermediate outputs (full CSV data, raw ERD source, complete YAML contents) — they are on disk.

What to offload: Each design-worker skill has Design Notes to Carry Forward and Next Step sections. Read them to know what to pass to the next phase.

Skill reading strategy (just-in-time, per phase): Read worker skills only when the phase that needs them is about to start. This keeps the salient rules at the top of the context window when they matter most.

Anti-pattern — do NOT batch-read all design-worker skills upfront. The retrospective found that pre-loading every worker at Phase 0 pushes the common-skill rules and the active phase's rules out of the attention window, producing format divergence across YAML files and non-standard transformation types. Read each skill the moment its phase begins.

Step-by-Step Workflow

Phase 0: Source Schema Intake (MANDATORY First Step)

PREREQUISITE — Read Common Skills Before Proceeding:

Before parsing the schema, read these two common skills. They set enterprise-wide constraints that every downstream phase depends on. Skipping them is the most common root cause of format divergence across YAML files.

1. skills/databricks-expert-agent/SKILL.md — Retain: "Extract, Don't Generate" (all table/column names come from the YAML or source schema, never from memory), CLUSTER BY AUTO, CDF + Row Tracking, comments + tags on every object.
2. data_product_accelerator/skills/common/naming-tagging-standards/SKILL.md — Retain: snake_case everywhere, dim_/fact_/bridge_ prefixes, dual-purpose description pattern <Definition>. Business: <context>. Technical: <details>. (the angle brackets are placeholders — do NOT write literal < or [ characters into descriptions), mandatory tags (layer, domain, PII).

These rules are the authoritative source whenever a domain-specific skill below paraphrases them.

This is the entry point for the entire data platform build. The customer provides a source schema CSV (e.g., context/Wanderbricks_Schema.csv) containing table and column metadata. This phase parses it into a structured inventory that drives all subsequent design decisions.

Input: context/{ProjectName}_Schema.csv — CSV with columns: table_catalog, table_schema, table_name, column_name, ordinal_position, full_data_type, data_type, is_nullable, comment

Steps:

1. Read and parse the schema CSV from context/:

   MANDATORY: Read references/schema-intake-patterns.md for complete implementations of parse_schema_csv(), classify_tables(), and infer_relationships().

   • parse_schema_csv(csv_path) — Reads CSV into structured dict keyed by table name, with columns, types, nullability, and comments per table.
   • Output: {table_name: {"columns": [...], "column_count": N, "catalog": ..., "schema": ...}}

2. Classify tables as dimensions, facts, or bridge/junction:

   • classify_tables(schema) — Classifies each table based on column patterns:
     • bridge: 3 or fewer columns AND 2+ FK-like columns
     • fact: 2+ FK columns AND numeric measures, OR 2+ timestamps AND 2+ FKs
     • dimension: everything else
   • Also identifies: PK candidates, FK columns, measures, timestamps per table
   • This classification is a HEURISTIC, not ground truth. It mis-classifies tables whose numeric columns are descriptive attributes rather than additive measures (geographic coordinates, prices, physical counts), and tables with no numeric columns or only one FK. After running it, print the full classification, then explicitly document every override you make and WHY (e.g. "properties reclassified dimension→… : base_price/latitude/bedrooms are attributes, not measures"). Never silently correct a classification — an undocumented override becomes invisible technical debt if a later step reads the raw classification instead of DESIGN_DECISIONS.md.

3. Identify FK relationships from column comments and naming patterns:

   • infer_relationships(classified) — Two inference strategies:
     • Pattern 1: Column comment contains "Foreign Key to 'X'" → direct FK
     • Pattern 2: Column name other_table_id matches a known table name
   • Returns list of {from_table, from_column, to_table, to_column, source}

4. (Optional) Industry coverage overlay — only if industry_reference_source ≠ none:

   Read data_product_accelerator/skills/gold/design-workers/08-industry-alignment/SKILL.md. Run build_industry_overlay(classified, Path(industry_reference_path)) from its scripts/industry_overlay.py to annotate each source table with its industry entity (Covered / Absorbed / Gap) and a provisional coverage %. This is a lens over the parsed source schema — it never invents tables. Fold the printed overlay into the Schema Intake Report and carry every core/extended Gap into Phase 1/2 for a Waived/Planned decision. Skip this step entirely when no industry reference is provided.

5. Produce Schema Intake Report:

The report summarizes: table inventory, entity classification, inferred relationships, and domain suggestions. This feeds Phase 1 (Requirements) and Phase 2 (Dimensional Modeling).

Output:

• Printed table inventory with classification (dimension/fact/bridge)
• Inferred FK relationships list
• Suggested domain groupings
• Recommended dimensional model skeleton (which tables → which Gold dims/facts)

Critical: The schema CSV is the single source of truth for table and column names. ALL downstream phases must extract names from the parsed schema — never generate table/column names from memory.

Phase 1: Requirements Gathering

Collect the following project context (enhanced with schema intake):

Output: Populated project context document (with Phase 0 schema intake data incorporated)

Phase 2: Dimensional Model Design

Read and Follow: references/dimensional-modeling-guide.md

Activities:

1. Identify 2-5 dimensions with SCD type decisions (Type 1 vs Type 2)
2. Identify 1-3 facts with explicit grain definitions
3. Define measures and metrics with calculation logic
4. Define relationships (FK constraints)
5. Assign tables to domains (Location, Product, Time, Sales, etc.)
6. (Optional) Apply industry alignment — if industry_reference_source ≠ none, read design-workers/08-industry-alignment and apply its guidance rules 1–6 (canonical domain names, conformed-dimension identification, grain/measure completeness, terminology steering, gap disposition, sensitivity). Record every decision in DESIGN_DECISIONS.md Section 8 and write gold_layer_design/INDUSTRY_CROSSWALK.csv. Never invent tables for uncovered entities — disposition them as Waived/Planned in SOURCE_TABLE_MAPPING.csv.

MANDATORY: Read these skills using the Read tool in order during Phase 2:

1. data_product_accelerator/skills/gold/design-workers/01-grain-definition/SKILL.md — Grain inference from PRIMARY KEY structure, transaction vs aggregated vs snapshot patterns, PK-grain decision tree
2. data_product_accelerator/skills/gold/design-workers/02-dimension-patterns/SKILL.md — Role-playing, degenerate, junk, mini-dimensions, hierarchy flattening, NULL handling
3. data_product_accelerator/skills/gold/design-workers/03-fact-table-patterns/SKILL.md — Measure additivity, factless facts, accumulating snapshots, late-arriving data
4. data_product_accelerator/skills/gold/design-workers/04-conformed-dimensions/SKILL.md — Enterprise bus matrix, conformed dimensions, shrunken dims, drill-across patterns

Apply from skills:

• Infer grain type from PRIMARY KEY structure (transaction, aggregated, snapshot)
• Document grain explicitly for each fact table
• Validate PRIMARY KEY matches grain type using decision tree
• Apply dimension patterns (denormalize hierarchies, handle NULLs with "Unknown" rows, combine flags into junk dimensions)
• Classify measures as additive, semi-additive, or non-additive
• Build the enterprise bus matrix mapping fact tables to dimensions
• Identify conformed dimensions shared across multiple facts

Key Outputs:

• Dimensions table (name, business key, SCD type, source Silver table, dimension pattern applied)
• Facts table (name, grain, source Silver tables, update frequency, fact type)
• Measures table (name, data type, calculation logic, business purpose, additivity classification)
• Relationships table (fact FK → dimension PK, cardinality)
• Domain assignments table (table → domain mapping)
• Enterprise bus matrix (fact tables × dimensions)
• NULL handling strategy (Unknown member rows per dimension)

🔴 MANDATORY: Write gold_layer_design/DESIGN_DECISIONS.md Before Phase 3

At the end of Phase 2 — before generating any ERD or YAML — persist the design contract to disk using the template at assets/templates/design-decisions-template.md. This file is the shared contract that every Phase 3-7 step (and every parallel subagent) MUST reference verbatim. Without it, subagents paraphrase format rules differently and YAML files diverge.

The file MUST contain these six sections:

1. Table inventory — table name, entity type (dim/fact/bridge), domain, SCD type, grain, source Silver table(s).
2. FK format contract — the exact YAML structure every foreign_keys: entry must use:

   foreign_keys:
     - columns: ["fk_column_name"]
       references: target_table(target_column)
       nullable: true   # or false
   

3. Description format contract — the exact string pattern (no literal brackets):

   One-sentence definition. Business: business context. Technical: implementation details.
   

4. Transformation type enum — the 15 allowed values (see Phase 4 Critical Rules); no other values permitted.
5. Top-level YAML key contract — mandatory keys for every YAML file, plus dimension-only and fact-only keys. See the template for the authoritative list.
6. Boolean-to-text conversion list — every source BOOLEAN column that will be rewritten as a STRING attribute in a business-sourced dimension (e.g., is_verified → verification_status: "Verified"/"Unverified"), plus the documented dim_date exception.

(Optional) Section 8 — Industry Alignment: When industry_reference_source ≠ none, also record the industry vertical, reference source, provisional coverage %, domain renames, conformed-dimension promotions, terminology changes (old→new), and every core/extended gap disposition (Waived/Planned). See design-workers/08-industry-alignment and Section 8 of assets/templates/design-decisions-template.md.

If using parallel subagents in Phase 4, include the full text of DESIGN_DECISIONS.md in every subagent prompt (not by reference). This is the single most effective guard against format divergence across subagent boundaries.

Phase 3: ERD Creation

MANDATORY: Read this skill using the Read tool BEFORE creating any ERD diagrams:

1. data_product_accelerator/skills/gold/design-workers/05-erd-diagrams/SKILL.md — ERD organization strategy (master/domain/summary), Mermaid syntax standards, domain emoji markers, relationship patterns, cross-domain references

Activities:

1. Count tables to determine ERD strategy (1-8, 9-20, 20+)
2. Create Master ERD (erd_master.md) with ALL tables grouped by domain
3. Create Domain ERDs (erd/erd_{domain}.md) if 9+ tables
4. Create Summary ERD (erd_summary.md) if 20+ tables
5. Add Domain Index table to Master ERD

Critical Rules (from 05-erd-diagrams):

• Use domain emoji markers (🏪 Location, 📦 Product, 📅 Time, 💰 Sales, 📊 Inventory)
• Use PK markers only (no inline descriptions in ERD)
• Use by_{column} pattern for relationship labels
• Group all relationships at end of diagram
• Use bracketed notation for cross-domain references: dim_store["🏪 dim_store (Location)"]

Outputs:

• gold_layer_design/erd_master.md (ALWAYS)
• gold_layer_design/erd_summary.md (if 20+ tables)
• gold_layer_design/erd/erd_{domain}.md (if 9+ tables)

Phase 4: YAML Schema Generation

MANDATORY: Read each skill below using the Read tool BEFORE generating any YAML schema files:

1. data_product_accelerator/skills/gold/design-workers/06-table-documentation/SKILL.md — Dual-purpose description pattern ([Definition]. Business: [...]. Technical: [...].), surrogate key naming, TBLPROPERTIES metadata, column comment standards

Activities:

1. Create domain-organized YAML directory structure (yaml/{domain}/)
2. Generate one YAML file per table using templates from 01-yaml-table-setup
3. Include complete column definitions with lineage metadata
4. Document PRIMARY KEY and FOREIGN KEY constraints
5. Apply standard table properties (CDF, row tracking, auto-optimize)
6. Write dual-purpose descriptions following 06-table-documentation patterns

Critical Rules (from design-workers/06-table-documentation + Non-Negotiable Defaults):

• Pattern: <Definition>. Business: <context>. Technical: <details>. — angle brackets are placeholders; do NOT write literal [, ], or <, > into descriptions.

• Surrogate keys as PRIMARY KEYS (not business keys)

• Include all TBLPROPERTIES (layer, domain, entity_type, grain, scd_type, etc.)

• Every column MUST have a lineage: section

• 🔴 clustering: auto in EVERY YAML schema (generates CLUSTER BY AUTO)

• 🔴 delta.enableChangeDataFeed: "true" in EVERY YAML table_properties:

• 🔴 delta.enableRowTracking: "true" in EVERY YAML table_properties:

• 🔴 All PRIMARY KEY columns MUST have nullable: false

• 🔴 Transformation type enum — every lineage.transformation value MUST be one of these 15 standard types from references/lineage-documentation-guide.md:

  DIRECT_COPY | RENAME | CAST | AGGREGATE_SUM | AGGREGATE_SUM_CONDITIONAL
  AGGREGATE_COUNT | AGGREGATE_AVG | DERIVED_CALCULATION | DERIVED_CONDITIONAL
  HASH_MD5 | HASH_SHA256 | COALESCE | DATE_TRUNC | GENERATED | LOOKUP
  

  Edge-case mapping (do NOT invent new types):

  Source Pattern	Correct Type	NOT This
  Boolean-to-text conversion	DERIVED_CONDITIONAL	BOOLEAN_TO_LABEL
  SCD2 effective_to close	GENERATED	SCD2_CLOSE
  SCD2 is_current flag	GENERATED	SCD2_CURRENT_FLAG
  Join to another table	LOOKUP	JOIN_LOOKUP
  Rename + type change	RENAME (document cast in transformation_logic)	RENAME_CAST

  When delegating to subagents, include this enum as a literal list in the prompt. Do not paraphrase.

YAML Template Reference: See references/yaml-schema-patterns.md

Outputs:

• gold_layer_design/yaml/{domain}/{table}.yaml for each table
• Domain-organized folder structure matching ERD domains

Phase 5: Column-Level Lineage Documentation

Read: references/lineage-documentation-guide.md

Activities:

1. Extract lineage from all YAML files
2. Document Bronze → Silver → Gold mapping for EVERY column
3. Specify transformation type from standard list (DIRECT_COPY, RENAME, CAST, AGGREGATE_SUM, AGGREGATE_SUM_CONDITIONAL, AGGREGATE_COUNT, DERIVED_CALCULATION, HASH_MD5, COALESCE, DATE_TRUNC, GENERATED, LOOKUP)
4. Document transformation logic as executable PySpark/SQL
5. Generate both CSV and Markdown formats

Use Script: scripts/generate_lineage_csv.py

Outputs:

• gold_layer_design/COLUMN_LINEAGE.csv (machine-readable, MANDATORY)
• gold_layer_design/COLUMN_LINEAGE.md (human-readable)

Phase 6: Business Onboarding Guide (MANDATORY)

Read: references/business-documentation-guide.md
Use Template: assets/templates/business-onboarding-template.md

Required Sections:

1. Introduction to Business Domain
2. The Business Lifecycle (Key Stages)
3. Key Business Entities (Players/Actors)
4. The Gold Layer Data Model (Overview)
5. Business Processes & Tracking (with ASCII flow diagrams)
   • 5B. Real-World Scenarios (minimum 3-4 detailed stories)
6. Analytics Use Cases
7. AI & ML Opportunities
8. Self-Service Analytics with Genie
9. Data Quality & Monitoring
10. Getting Started (per role: Engineer, Analyst, Scientist, Business User)

Critical: Each story must show source system data → Gold layer updates → analytics impact at each stage.

Output: gold_layer_design/docs/BUSINESS_ONBOARDING_GUIDE.md

Phase 7: Source Table Mapping (MANDATORY)

Use Template: assets/templates/source-table-mapping-template.csv

Activities:

1. List ALL source tables (included, excluded, planned)
2. Assign status: INCLUDED, EXCLUDED, or PLANNED
3. Provide rationale for EVERY row (required, no exceptions)
4. Map INCLUDED tables to Gold tables
5. Assign domains and implementation phases

Output: gold_layer_design/SOURCE_TABLE_MAPPING.csv

Phase 8: Design Validation

MANDATORY: Read this skill using the Read tool BEFORE running design validation:

1. data_product_accelerator/skills/gold/design-workers/07-design-validation/SKILL.md — YAML↔ERD↔Lineage cross-validation, PK/FK reference consistency, mandatory field validation
2. MANDATORY: Read references/validation-checklists.md — semantic compliance checks that structural validation alone will NOT catch. The four highest-value checks the design validation subagent MUST run:
   • Transformation enum compliance — every lineage.transformation value is one of the 15 standard types (no invented values like BOOLEAN_TO_LABEL, SCD2_CLOSE, JOIN_LOOKUP).
   • FK format consistency — every foreign_keys: entry has columns, references, and nullable keys in that shape across every YAML file.
   • Description format compliance — no literal [, ], <, or > characters appear inside description: strings (they are placeholder syntax only); descriptions match the <Definition>. Business: … . Technical: … . pattern.
   • Unknown-member presence — every dimension that is referenced by a nullable: true FK in any fact table declares an unknown_member: block.

Validation Activities:

1. Consistency check: YAML ↔ ERD ↔ Lineage CSV (all columns match)
2. Validate all ERD columns exist in YAML definitions
3. Validate all YAML columns have lineage metadata
4. Validate PRIMARY KEY definitions match grain type
5. Validate FOREIGN KEY references point to valid tables/columns
6. Run lineage validation script
7. Upstream cross-reference (MANDATORY when Silver exists): Check whether upstream source tables exist (via spark.catalog.tableExists()). If they do, you MUST run cross_reference_silver_at_design_time() from references/schema-intake-patterns.md to validate YAML lineage silver_column values against the actual source-table schemas, fix any mismatches found, and record the resulting mismatch count (target: 0) as an explicit line in the validation report — running it silently does not satisfy this check. This is the only validation that confirms the artifacts agree with reality rather than just with each other (the YAML/ERD/lineage consistency checks are self-referential — all three are generated from the same session data, so a systematic column error would pass them while failing here). Only when source tables do not exist yet is this deferred — in that case note that it will be enforced as a hard gate during Phase 0 of 01-gold-layer-setup.
8. (Advisory) Industry alignment — Validation 6: If gold_layer_design/INDUSTRY_CROSSWALK.csv exists, 07-design-validation loads, verifies, and scores it and emits gold_layer_design/INDUSTRY_ALIGNMENT.md. This is advisory — excluded from the pass/fail all_valid result. Skipped (N/A) when no industry reference was provided.

Outputs:

• Validation report (pass/fail for each category)
• List of inconsistencies to fix (including any upstream column mismatches)
• Completed design sign-off checklist

Phase 9: Stakeholder Review

Activities:

1. Present ERD hierarchy to business stakeholders
2. Review grain definitions for each fact table
3. Confirm measures are complete for reporting needs
4. Validate naming conventions with data governance
5. Review Business Onboarding Guide stories for accuracy
6. Obtain formal design sign-off

Output: Stakeholder approval document

Final File Organization

gold_layer_design/
├── README.md                          # Navigation hub
├── erd_master.md                      # Complete ERD (ALWAYS)
├── erd_summary.md                     # Domain overview (20+ tables)
├── erd/                               # Domain ERDs (9+ tables)
│   └── erd_{domain}.md
├── yaml/                              # Domain-organized schemas
│   └── {domain}/
│       └── {table}.yaml
├── docs/
│   └── BUSINESS_ONBOARDING_GUIDE.md   # MANDATORY
├── COLUMN_LINEAGE.csv                 # MANDATORY
├── COLUMN_LINEAGE.md                  # Human-readable
├── SOURCE_TABLE_MAPPING.csv           # MANDATORY
├── INDUSTRY_CROSSWALK.csv             # Advisory (only with an industry reference)
├── INDUSTRY_ALIGNMENT.md              # Advisory (only with an industry reference)
├── DESIGN_SUMMARY.md                  # Design decisions
└── DESIGN_GAP_ANALYSIS.md            # Coverage analysis

Key Design Principles

1. Schema Extraction Over Generation - Always extract table/column names from YAML. Never generate from scratch. Prevents hallucinations and schema mismatches.

2. YAML as Single Source of Truth - All column names, types, constraints, descriptions defined in YAML first. Implementation reads YAML.

3. Dual-Purpose Documentation - ALL descriptions serve both business users and technical users (including LLMs like Genie). Pattern: [Definition]. Business: [...]. Technical: [...].

4. Progressive Disclosure - Core workflow here in SKILL.md. Detailed patterns in references/. Templates in assets/templates/.

5. Dependency Orchestration - Read and follow dependency skills at the phase indicated. Don't skip dependency skills.

Common Mistakes to Avoid

Time Estimates

Next Steps After Design

After completing design and obtaining stakeholder sign-off, read the implementation orchestrator skill:

1. data_product_accelerator/skills/gold/01-gold-layer-setup/SKILL.md — End-to-end implementation of tables, merge scripts, FK constraints, and Asset Bundle jobs from the YAML designs created here

That skill will in turn invoke:

• pipeline-workers/02-merge-patterns — SCD Type 1/2 dimension merges, fact aggregation patterns
• pipeline-workers/03-deduplication — Mandatory deduplication before MERGE
• pipeline-workers/01-yaml-table-setup — YAML-driven DDL generation
• pipeline-workers/05-schema-validation — DataFrame↔DDL schema validation
• pipeline-workers/04-grain-validation — Pre-merge grain validation
• All other implementation dependencies

Pipeline Progression

This is the first stage in the Design-First pipeline. Start here after receiving the customer's source schema CSV.

Next stage: After completing Gold layer design, proceed to:

• bronze/00-bronze-layer-setup — Create Bronze tables matching the source schema and generate test data with Faker

Reference Files

• Dimensional Modeling Guide - Dimensions, facts, measures, relationships, domain assignment
• ERD Organization Strategy - Master/Domain/Summary ERD patterns
• YAML Schema Patterns - YAML templates for dimensions, facts, date dimensions
• Lineage Documentation Guide - Column lineage, transformation types, CSV generation
• Business Documentation Guide - Business Onboarding Guide and Source Table Mapping
• Validation Checklists - All design validation checklists

Post-Completion: Skill Usage Summary (MANDATORY)

After completing all phases of this orchestrator, output a Skill Usage Summary reflecting what you ACTUALLY did — not a pre-written summary.

What to Include

1. Every skill SKILL.md or references/ file you read (via the Read tool), in the order you read them
2. Which phase you were in when you read it
3. Whether it was a Worker, Common, Cross-domain, or Reference file
4. A one-line description of what you specifically used it for in this session

Format

Summary Footer

End with:

• Totals: X worker skills, Y common skills, Z reference files read across N phases
• Skipped: List any skills from the dependency table above that you did NOT need to read, and why (e.g., "phase not applicable", "user skipped", "no issues encountered")
• Unplanned: List any skills you read that were NOT listed in the dependency table (e.g., for troubleshooting, edge cases, or user-requested detours)

External References

• Kimball Dimensional Modeling | Unity Catalog Constraints | Mermaid ERD Syntax | AgentSkills.io Specification