Data Context Extractor

Bootstrap Mode

Objective: Initialize a comprehensive knowledge base for a set of high-value semantic models.

Phase 1: Semantic Model Discovery & Technical Profiling

Leverage the MCP tools to map the technical landscape before engaging stakeholders.

1. Catalog Enumeration: Call kyvos_list_semantic_models to identify available semantic domains.
2. Target Selection: Consult with the user to identify the "Core Business Cubes" (e.g., the 20% of cubes that answer 80% of questions).
3. Schema Analysis: Call kyvos_list_semantic_model_columns to retrieve the flat structure (measures and dimensions).
4. Prompt Intelligence: Call kyvos_sql_generation_prompt for the target cubes.
   • Expert Note: This prompt contains the "ground truth" for SQL generation. Analyze it to understand the cube's specific constraints (e.g., "This cube is pre-aggregated at the Monthly level").

Phase 2: Semantic Enrichment (The Expert Interview)

Engage the domain expert to layer meaning on top of the technical schema.

1. Domain & Scope Definition

"I've analyzed the [Cube_Name] schema. It appears to cover [Subject]. To structure the domain knowledge correctly:

• What is the primary Business Domain this cube serves? (e.g., 'Global Supply Chain', 'Executive Finance')
• What are the Strategic KPIs driven by this model?"

2. Column Context & Dictionary (Crucial)

"Let's define the key columns found in the schema:

• Ambiguity Check: I see [Order Date] and [Ship Date]. Which one drives revenue recognition?
• Status Codes: For the [Status] dimension, what do values like 'X' or '99' actually mean?
• Measure Logic: Is [Sales_Amount] pre-tax or post-tax? Does it include returns?"

3. Dimensionality & Hierarchies

"The schema lists [Dim_A] and [Dim_B].

• In practice, what are the primary Drill-Down Paths analysts use?
• Are there 'Virtual Hierarchies' that aren't explicitly defined but logically exist (e.g., Product Category -> SKU)?"

4. The "Knowledge" Layer

"Every dataset has unwritten rules.

• Are there Standard Exclusions? (e.g., 'Exclude internal test accounts', 'Ignore transactions before 2020')
• Are there Data Quality caveats we should document? (e.g., 'Region X reporting is delayed by 2 days')"

5. Analytical Patterns

"How is this data typically consumed?

• Trend Analysis: Is this mostly for MoM/YoY comparisons?
• Cohort Analysis: Do we track entities over time?
• Snapshotting: Is this a point-in-time view?"

Phase 3: Domain Knowledge Modeling

Synthesize the findings into a structured "Domain Knowledge Graph" (represented as files).

Structure:

[company]-kyvos-analyst/
├── SKILL.md
└── references/
    ├── domains/             # Domain-Centric Documentation
    │   ├── [business_domain_1].md  # e.g., "revenue_operations.md"
    │   ├── [business_domain_2].md  # e.g., "customer_experience.md"
    │   └── [business_domain_3].md
    └── common-patterns.md   # Cross-domain analytical patterns

Templates:

• SKILL.md: Use references/skill-template.md
• Domain Reference: Use references/domain-template.md (Focus on mapping Business Concepts → Cube Objects)

Phase 4: Validation & Delivery

Step 1: Semantic Verification

"I have modeled the [Domain] context. Let's validate it against a real-world scenario. Ask me a complex business question, and I will demonstrate how I map it to the Kyvos schema using the new reference files."

Step 2: Refinement Adjust the domain mappings based on the test results.

Step 3: Packaging Deliver the finalized skill package.

Iteration Mode

Objective: User has an existing Kyvos skill but needs to add more context.

1. Gap Analysis: Analyze the failure point (e.g., "The model failed to calculate 'Net Churn' correctly").
2. Targeted Introspection: Re-examine the specific columns or prompt instructions involved.
3. Logic Injection: Update the specific [domain].md file with the missing business logic or concept mapping.
4. Re-alignment: Ensure the new logic doesn't conflict with existing patterns.

Reference File Standards

Domain Documentation (domains/[subject].md)

Create a Business-to-Technical Map that captures both high-level context and low-level column details:

• Business Concept: The term users say (e.g., "Gross Churn").
• Technical Implementation: The specific Kyvos measure/dimension + filter combination.
• Column Context:
  • Definition: Precise meaning of the column.
  • Valid Values: Enumeration of important categorical values.
  • Caveats: Null handling, data quality notes.
• Grain Implications: Does this concept only exist at a certain level of aggregation?
• Cross-Cube Logic: If the domain spans multiple cubes, explain the join/relationship logic (conceptually).

Query Patterns

Focus on Intent-Based Patterns:

• "Period-over-Period Growth"
• "Contribution to Total (Part-to-Whole)"
• "Ranking & Top-N"
• "Cohort Retention"

Quality Assurance Checklist

• Business-First: Does the documentation start with business terms?
• Column-Precise: Are key columns defined with specific context (not just generic names)?
• Prompt-Aligned: Does the skill explicitly defer SQL generation rules to the MCP prompt?
• Context-Rich: Are "Tribal Knowledge" filters and caveats clearly documented?
• Actionable: Can a user read the domain file and immediately understand what questions they can ask?