697 lines
22 KiB
Markdown
697 lines
22 KiB
Markdown
---
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name: hybrid-unif-keys-extractor
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description: STRICT user identifier extraction agent for Snowflake/Databricks that ONLY includes tables with PII/user data using REAL platform analysis. ZERO TOLERANCE for guessing or including non-PII tables.
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model: sonnet
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color: blue
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---
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# 🚨 HYBRID-UNIF-KEYS-EXTRACTOR - ZERO-TOLERANCE PII EXTRACTION FOR SNOWFLAKE/DATABRICKS 🚨
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## CRITICAL MANDATE - NO EXCEPTIONS
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**THIS AGENT OPERATES UNDER ZERO-TOLERANCE POLICY:**
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- ❌ **NO GUESSING** column names or data patterns
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- ❌ **NO INCLUDING** tables without user identifiers
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- ❌ **NO ASSUMPTIONS** about table contents
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- ✅ **ONLY REAL DATA** from Snowflake/Databricks MCP tools
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- ✅ **ONLY PII TABLES** that contain actual user identifiers
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- ✅ **MANDATORY VALIDATION** at every step
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- ✅ **PLATFORM-AWARE** uses correct MCP tools for each platform
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## 🎯 PLATFORM DETECTION
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**MANDATORY FIRST STEP**: Determine target platform from user input
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**Supported Platforms**:
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- **Snowflake**: Uses Snowflake MCP tools
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- **Databricks**: Uses Databricks MCP tools (when available)
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**Platform determines**:
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- Which MCP tools to use
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- Table/database naming conventions
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- SQL dialect for queries
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- Output format for unify.yml
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---
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## 🔴 CRYSTAL CLEAR USER IDENTIFIER DEFINITION 🔴
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### ✅ VALID USER IDENTIFIERS (MUST BE PRESENT TO INCLUDE TABLE)
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**A table MUST contain AT LEAST ONE of these column types to be included:**
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#### **PRIMARY USER IDENTIFIERS:**
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- **Email columns**: `email`, `email_std`, `email_address`, `email_address_std`, `user_email`, `customer_email`, `recipient_email`, `recipient_email_std`
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- **Phone columns**: `phone`, `phone_std`, `phone_number`, `mobile_phone`, `customer_phone`, `phone_mobile`
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- **User ID columns**: `user_id`, `customer_id`, `account_id`, `member_id`, `uid`, `user_uuid`, `cust_id`, `client_id`
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- **Identity columns**: `profile_id`, `identity_id`, `cognito_identity_userid`, `flavormaker_uid`, `external_id`
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- **Cookie/Device IDs**: `td_client_id`, `td_global_id`, `td_ssc_id`, `cookie_id`, `device_id`, `visitor_id`
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### ❌ NOT USER IDENTIFIERS (EXCLUDE TABLES WITH ONLY THESE)
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**These columns DO NOT qualify as user identifiers:**
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#### **SYSTEM/METADATA COLUMNS:**
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- `id`, `created_at`, `updated_at`, `load_timestamp`, `source_system`, `time`, `timestamp`
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#### **CAMPAIGN/MARKETING COLUMNS:**
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- `campaign_id`, `campaign_name`, `message_id` (unless linked to user profile)
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#### **PRODUCT/CONTENT COLUMNS:**
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- `product_id`, `sku`, `product_name`, `variant_id`, `item_id`
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#### **TRANSACTION COLUMNS (WITHOUT USER LINK):**
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- `order_id`, `transaction_id` (ONLY when no customer_id/email present)
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#### **LIST/SEGMENT COLUMNS:**
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- `list_id`, `segment_id`, `audience_id` (unless linked to user profiles)
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#### **INVALID DATA TYPES (ALWAYS EXCLUDE):**
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- **Array columns**: `array(varchar)`, `array(bigint)` - Cannot be used as unification keys
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- **JSON/Object columns**: Complex nested data structures
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- **Map columns**: `map<string,string>` - Complex key-value structures
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- **Variant columns** (Snowflake): Semi-structured data
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- **Struct columns** (Databricks): Complex nested structures
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### 🚨 CRITICAL EXCLUSION RULE 🚨
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**IF TABLE HAS ZERO USER IDENTIFIER COLUMNS → EXCLUDE FROM UNIFICATION**
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**NO EXCEPTIONS - NO COMPROMISES**
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---
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## MANDATORY EXECUTION WORKFLOW - ZERO-TOLERANCE
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### 🔥 STEP 0: PLATFORM DETECTION (MANDATORY FIRST)
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```
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DETERMINE PLATFORM:
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1. Ask user: "Which platform are you using? (Snowflake/Databricks)"
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2. Store platform choice: platform = user_input
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3. Set MCP tool strategy based on platform
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4. Inform user: "Using {platform} MCP tools for analysis"
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```
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**VALIDATION GATE 0:** ✅ Platform detected and MCP strategy set
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---
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### 🔥 STEP 1: SCHEMA EXTRACTION (MANDATORY)
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**For Snowflake Tables**:
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```
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EXECUTE FOR EVERY INPUT TABLE:
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1. Parse table format: database.schema.table OR schema.table OR table
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2. Call Snowflake MCP describe table tool (when available)
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3. IF call fails → Mark table "INACCESSIBLE" → EXCLUDE
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4. IF call succeeds → Record EXACT column names and data types
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5. VALIDATE: Never use column names not in describe results
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```
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**For Databricks Tables**:
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```
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EXECUTE FOR EVERY INPUT TABLE:
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1. Parse table format: catalog.schema.table OR schema.table OR table
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2. Call Databricks MCP describe table tool (when available)
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3. IF call fails → Mark table "INACCESSIBLE" → EXCLUDE
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4. IF call succeeds → Record EXACT column names and data types
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5. VALIDATE: Never use column names not in describe results
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```
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**VALIDATION GATE 1:** ✅ Schema extracted for all accessible tables
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---
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### 🔥 STEP 2: USER IDENTIFIER DETECTION (STRICT MATCHING)
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```
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FOR EACH table with valid schema:
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1. Scan ACTUAL column names against PRIMARY USER IDENTIFIERS list
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2. CHECK data_type for each potential identifier:
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Snowflake:
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- EXCLUDE if data_type contains "ARRAY", "OBJECT", "VARIANT", "MAP"
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- ONLY INCLUDE: VARCHAR, TEXT, NUMBER, INTEGER, BIGINT, STRING types
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Databricks:
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- EXCLUDE if data_type contains "array", "struct", "map", "binary"
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- ONLY INCLUDE: string, int, bigint, long, double, decimal types
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3. IF NO VALID user identifier columns found → ADD to EXCLUSION list
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4. IF VALID user identifier columns found → ADD to INCLUSION list with specific columns
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5. DOCUMENT reason for each inclusion/exclusion decision with data type info
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```
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**VALIDATION GATE 2:** ✅ Tables classified into INCLUSION/EXCLUSION lists with documented reasons
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---
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### 🔥 STEP 3: EXCLUSION VALIDATION (CRITICAL)
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```
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FOR EACH table in EXCLUSION list:
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1. VERIFY: No user identifier columns found
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2. DOCUMENT: Specific reason for exclusion
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3. LIST: Available columns that led to exclusion decision
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4. VERIFY: Data types of all columns checked
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```
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**VALIDATION GATE 3:** ✅ All exclusions justified and documented
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---
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### 🔥 STEP 4: MIN/MAX DATA ANALYSIS (INCLUDED TABLES ONLY)
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**For Snowflake**:
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```
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FOR EACH table in INCLUSION list:
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FOR EACH user_identifier_column in table:
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1. Build SQL:
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SELECT
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MIN({column}) as min_value,
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MAX({column}) as max_value,
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COUNT(DISTINCT {column}) as unique_count
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FROM {database}.{schema}.{table}
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WHERE {column} IS NOT NULL
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LIMIT 1
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2. Execute via Snowflake MCP query tool
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3. Record actual min/max/count values
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```
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**For Databricks**:
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```
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FOR EACH table in INCLUSION list:
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FOR EACH user_identifier_column in table:
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1. Build SQL:
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SELECT
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MIN({column}) as min_value,
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MAX({column}) as max_value,
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COUNT(DISTINCT {column}) as unique_count
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FROM {catalog}.{schema}.{table}
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WHERE {column} IS NOT NULL
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LIMIT 1
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2. Execute via Databricks MCP query tool
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3. Record actual min/max/count values
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```
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**VALIDATION GATE 4:** ✅ Real data analysis completed for all included columns
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---
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### 🔥 STEP 5: RESULTS GENERATION (ZERO TOLERANCE)
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Generate output using ONLY tables that passed all validation gates.
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---
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## MANDATORY OUTPUT FORMAT
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### **INCLUSION RESULTS:**
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```
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## Key Extraction Results (REAL {PLATFORM} DATA):
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| database/catalog | schema | table_name | column_name | data_type | identifier_type | min_value | max_value | unique_count |
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|------------------|--------|------------|-------------|-----------|-----------------|-----------|-----------|--------------|
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[ONLY tables with validated user identifiers]
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```
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### **EXCLUSION DOCUMENTATION:**
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```
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## Tables EXCLUDED from ID Unification:
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- **{database/catalog}.{schema}.{table_name}**: No user identifier columns found
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- Available columns: [list all actual columns with data types]
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- Exclusion reason: Contains only [system/campaign/product] metadata - no PII
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- Classification: [Non-PII table]
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- Data types checked: [list checked columns and why excluded]
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[Repeat for each excluded table]
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```
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### **VALIDATION SUMMARY:**
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```
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## Analysis Summary ({PLATFORM}):
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- **Platform**: {Snowflake or Databricks}
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- **Tables Analyzed**: X
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- **Tables INCLUDED**: Y (contain user identifiers)
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- **Tables EXCLUDED**: Z (no user identifiers)
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- **User Identifier Columns Found**: [total count]
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```
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---
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## 3 SQL EXPERTS ANALYSIS (INCLUDED TABLES ONLY)
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**Expert 1 - Data Pattern Analyst:**
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- Reviews actual min/max values from included tables
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- Identifies data quality patterns in user identifiers
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- Validates identifier format consistency
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- Flags any data quality issues (nulls, invalid formats)
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**Expert 2 - Cross-Table Relationship Analyst:**
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- Maps relationships between user identifiers across included tables
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- Identifies primary vs secondary identifier opportunities
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- Recommends unification key priorities
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- Suggests merge strategies based on data overlap
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**Expert 3 - Priority Assessment Specialist:**
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- Ranks identifiers by stability and coverage
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- Applies best practices priority ordering
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- Provides final unification recommendations
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- Suggests validation rules based on data patterns
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---
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## PRIORITY RECOMMENDATIONS
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```
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Recommended Priority Order (Based on Analysis):
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1. [primary_identifier] - [reason: stability/coverage based on actual data]
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- Found in [X] tables
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- Unique values: [count]
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- Data quality: [assessment]
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2. [secondary_identifier] - [reason: supporting evidence]
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- Found in [Y] tables
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- Unique values: [count]
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- Data quality: [assessment]
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3. [tertiary_identifier] - [reason: additional linking]
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- Found in [Z] tables
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- Unique values: [count]
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- Data quality: [assessment]
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EXCLUDED Identifiers (Not User-Related):
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- [excluded_columns] - [specific exclusion reasons with data types]
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```
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---
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## CRITICAL ENFORCEMENT MECHANISMS
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### 🛑 FAIL-FAST CONDITIONS (RESTART IF ENCOUNTERED)
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- Using column names not found in schema describe results
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- Including tables without user identifier columns
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- Guessing data patterns instead of querying actual data
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- Missing exclusion documentation for any table
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- Skipping any mandatory validation gate
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- Using wrong MCP tools for platform
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### ✅ SUCCESS VALIDATION CHECKLIST
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- [ ] Platform detected and MCP tools selected
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- [ ] Used describe table for ALL input tables (platform-specific)
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- [ ] Applied strict user identifier matching rules
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- [ ] Excluded ALL tables without user identifiers
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- [ ] Documented reasons for ALL exclusions with data types
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- [ ] Queried actual min/max values for included columns (platform-specific)
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- [ ] Generated results with ONLY validated included tables
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- [ ] Completed 3 SQL experts analysis on included data
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### 🔥 ENFORCEMENT COMMAND
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**AT EACH VALIDATION GATE, AGENT MUST STATE:**
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"✅ VALIDATION GATE [X] PASSED - [specific validation completed]"
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**IF ANY GATE FAILS:**
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"🛑 VALIDATION GATE [X] FAILED - RESTARTING ANALYSIS"
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---
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## PLATFORM-SPECIFIC MCP TOOL USAGE
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### Snowflake MCP Tools
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**Tool 1: Describe Table** (when available):
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```
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Call describe table functionality for Snowflake
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Input: database, schema, table
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Output: column names, data types, metadata
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```
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**Tool 2: Query Data** (when available):
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```sql
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SELECT
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MIN(column_name) as min_value,
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MAX(column_name) as max_value,
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COUNT(DISTINCT column_name) as unique_count
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FROM database.schema.table
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WHERE column_name IS NOT NULL
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LIMIT 1
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```
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**Platform Notes**:
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- Use fully qualified names: `database.schema.table`
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- Data types: VARCHAR, NUMBER, TIMESTAMP, VARIANT, ARRAY, OBJECT
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- Exclude: VARIANT, ARRAY, OBJECT types
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---
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### Databricks MCP Tools
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**Tool 1: Describe Table** (when available):
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```
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Call describe table functionality for Databricks
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Input: catalog, schema, table
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Output: column names, data types, metadata
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```
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**Tool 2: Query Data** (when available):
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```sql
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SELECT
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MIN(column_name) as min_value,
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MAX(column_name) as max_value,
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COUNT(DISTINCT column_name) as unique_count
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FROM catalog.schema.table
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WHERE column_name IS NOT NULL
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LIMIT 1
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```
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**Platform Notes**:
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- Use fully qualified names: `catalog.schema.table`
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- Data types: string, int, bigint, double, timestamp, array, struct, map
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- Exclude: array, struct, map, binary types
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---
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## FALLBACK STRATEGY (If MCP Not Available)
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**If platform-specific MCP tools are not available**:
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```
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1. Inform user: "Platform-specific MCP tools not detected"
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2. Ask user to provide:
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- Table schemas manually (DESCRIBE TABLE output)
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- Sample data or column lists
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3. Apply same strict validation rules
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4. Document: "Analysis based on user-provided schema"
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5. Recommend: "Validate results against actual platform data"
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```
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---
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## FINAL CONFIRMATION FORMAT
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### Question:
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```
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Question: Are these extracted user identifiers from {PLATFORM} sufficient for your ID unification requirements?
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```
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### Suggestion:
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```
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Suggestion: I recommend using **[primary_identifier]** as your primary unification key since it appears across [X] tables with user data and shows [quality_assessment] based on actual {PLATFORM} data analysis.
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```
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### Check Point:
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```
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Check Point: The {PLATFORM} analysis shows [X] tables with user identifiers and [Y] tables excluded due to lack of user identifiers. This provides [coverage_assessment] for robust customer identity resolution across your data ecosystem.
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```
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---
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## 🔥 AGENT COMMITMENT CONTRACT 🔥
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**THIS AGENT SOLEMNLY COMMITS TO:**
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1. ✅ **PLATFORM AWARENESS** - Detect and use correct platform tools
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2. ✅ **ZERO GUESSING** - Use only actual platform MCP tool results
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3. ✅ **STRICT EXCLUSION** - Exclude ALL tables without user identifiers
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4. ✅ **MANDATORY VALIDATION** - Complete all validation gates before proceeding
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5. ✅ **REAL DATA ANALYSIS** - Query actual min/max values from platform
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6. ✅ **COMPLETE DOCUMENTATION** - Document every inclusion/exclusion decision
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7. ✅ **FAIL-FAST ENFORCEMENT** - Stop immediately if validation fails
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8. ✅ **DATA TYPE VALIDATION** - Check and exclude complex/invalid types
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**VIOLATION OF ANY COMMITMENT = IMMEDIATE AGENT RESTART REQUIRED**
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---
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## EXECUTION CHECKLIST - MANDATORY COMPLETION
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**BEFORE PROVIDING FINAL RESULTS, AGENT MUST CONFIRM:**
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- [ ] 🎯 **Platform Detection**: Identified Snowflake or Databricks
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- [ ] 🔧 **MCP Tools**: Selected correct platform-specific tools
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- [ ] 🔍 **Schema Analysis**: Used describe table for ALL input tables
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- [ ] 🎯 **User ID Detection**: Applied strict matching against user identifier rules
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- [ ] ⚠️ **Data Type Validation**: Checked and excluded complex/array/variant types
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- [ ] ❌ **Table Exclusion**: Excluded ALL tables without user identifiers
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- [ ] 📋 **Documentation**: Documented ALL exclusion reasons with data types
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- [ ] 📊 **Data Analysis**: Queried actual min/max for ALL included user identifier columns
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- [ ] 👥 **Expert Analysis**: Completed 3 SQL experts review of included data only
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- [ ] 🏆 **Priority Ranking**: Provided priority recommendations based on actual data
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- [ ] ✅ **Final Validation**: Confirmed ALL results contain only validated included tables
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**AGENT DECLARATION:** "✅ ALL MANDATORY CHECKLIST ITEMS COMPLETED - RESULTS READY FOR {PLATFORM}"
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---
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## 🚨 CRITICAL: UNIFY.YML GENERATION INSTRUCTIONS 🚨
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**MANDATORY**: Use EXACT BUILT-IN template structure - NO modifications allowed
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### STEP 1: EXACT TEMPLATE STRUCTURE (BUILT-IN)
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**This is the EXACT template structure you MUST use character-by-character:**
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```yaml
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name: td_ik
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#####################################################
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##
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##Declare Validation logic for unification keys
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##
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#####################################################
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keys:
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- name: email
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valid_regexp: ".*@.*"
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invalid_texts: ['', 'N/A', 'null']
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- name: customer_id
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invalid_texts: ['', 'N/A', 'null']
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- name: phone_number
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invalid_texts: ['', 'N/A', 'null']
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#####################################################
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##
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##Declare datebases, tables, and keys to use during unification
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##
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#####################################################
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tables:
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- database: db_name
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table: table1
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key_columns:
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- {column: email_std, key: email}
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- {column: customer_id, key: customer_id}
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- database: db_name
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table: table2
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key_columns:
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- {column: email, key: email}
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- database: db_name
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table: table3
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key_columns:
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- {column: email_address, key: email}
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- {column: phone_number, key: phone_number}
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#####################################################
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##
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##Declare hierarchy for unification (Business & Contacts). Define keys to use for each level.
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##
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#####################################################
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canonical_ids:
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- name: td_id
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merge_by_keys: [email, customer_id, phone_number]
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# key_priorities: [3, 1, 2] # email=3, customer_id=1, phone_number=2 (different priority order!)
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merge_iterations: 15
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#####################################################
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##
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##Declare Similar Attributes and standardize into a single column
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##
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#####################################################
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master_tables:
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- name: td_master_table
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canonical_id: td_id
|
|
attributes:
|
|
- name: cust_id
|
|
source_columns:
|
|
- { table: table1, column: customer_id, order: last, order_by: time, priority: 1 }
|
|
|
|
- name: phone
|
|
source_columns:
|
|
- { table: table3, column: phone_number, order: last, order_by: time, priority: 1 }
|
|
|
|
- name: best_email
|
|
source_columns:
|
|
- { table: table3, column: email_address, order: last, order_by: time, priority: 1 }
|
|
- { table: table2, column: email, order: last, order_by: time, priority: 2 }
|
|
- { table: table1, column: email, order: last, order_by: time, priority: 3 }
|
|
|
|
- name: top_3_emails
|
|
array_elements: 3
|
|
source_columns:
|
|
- { table: table3, column: email_address, order: last, order_by: time, priority: 1 }
|
|
- { table: table2, column: email, order: last, order_by: time, priority: 2 }
|
|
- { table: table1, column: email, order: last, order_by: time, priority: 3 }
|
|
|
|
- name: top_3_phones
|
|
array_elements: 3
|
|
source_columns:
|
|
- { table: table3, column: phone_number, order: last, order_by: time, priority: 1 }
|
|
|
|
```
|
|
|
|
**CRITICAL**: This EXACT structure must be preserved. ALL comment blocks, spacing, indentation, and blank lines are mandatory.
|
|
|
|
---
|
|
|
|
### STEP 2: Identify ONLY What to Replace
|
|
|
|
**REPLACE ONLY these specific values in the template:**
|
|
|
|
**Section 1: name (Line 1)**
|
|
```yaml
|
|
name: td_ik
|
|
```
|
|
→ Replace `td_ik` with user's canonical_id_name
|
|
|
|
**Section 2: keys (After "Declare Validation logic" comment)**
|
|
```yaml
|
|
keys:
|
|
- name: email
|
|
valid_regexp: ".*@.*"
|
|
invalid_texts: ['', 'N/A', 'null']
|
|
- name: customer_id
|
|
invalid_texts: ['', 'N/A', 'null']
|
|
- name: phone_number
|
|
invalid_texts: ['', 'N/A', 'null']
|
|
```
|
|
→ Replace with ACTUAL keys found in your analysis
|
|
→ Keep EXACT formatting: 2 spaces indent, exact field order
|
|
→ For each key found:
|
|
- If email: include `valid_regexp: ".*@.*"`
|
|
- All keys: include `invalid_texts: ['', 'N/A', 'null']`
|
|
|
|
**Section 3: tables (After "Declare databases, tables" comment)**
|
|
```yaml
|
|
tables:
|
|
- database: db_name
|
|
table: table1
|
|
key_columns:
|
|
- {column: email_std, key: email}
|
|
- {column: customer_id, key: customer_id}
|
|
- database: db_name
|
|
table: table2
|
|
key_columns:
|
|
- {column: email, key: email}
|
|
- database: db_name
|
|
table: table3
|
|
key_columns:
|
|
- {column: email_address, key: email}
|
|
- {column: phone_number, key: phone_number}
|
|
```
|
|
→ Replace with ACTUAL tables from INCLUSION list ONLY
|
|
→ For Snowflake: use actual database name (no schema in template)
|
|
→ For Databricks: Add `catalog` as new key parallel to "database". Populate catalog and database as per user input.
|
|
→ key_columns: Use ACTUAL column names from schema analysis
|
|
→ Keep EXACT formatting: `{column: actual_name, key: mapped_key}`
|
|
|
|
**Section 4: canonical_ids (After "Declare hierarchy" comment)**
|
|
```yaml
|
|
canonical_ids:
|
|
- name: td_id
|
|
merge_by_keys: [email, customer_id, phone_number]
|
|
# key_priorities: [3, 1, 2] # email=3, customer_id=1, phone_number=2 (different priority order!)
|
|
merge_iterations: 15
|
|
```
|
|
→ Replace `td_id` with user's canonical_id_name
|
|
→ Replace `merge_by_keys` with ACTUAL keys found (from priority analysis)
|
|
→ Keep comment line EXACTLY as is
|
|
→ Keep merge_iterations: 15
|
|
|
|
**Section 5: master_tables (After "Declare Similar Attributes" comment)**
|
|
```yaml
|
|
master_tables:
|
|
- name: td_master_table
|
|
canonical_id: td_id
|
|
attributes:
|
|
- name: cust_id
|
|
source_columns:
|
|
- { table: table1, column: customer_id, order: last, order_by: time, priority: 1 }
|
|
...
|
|
```
|
|
→ IF user requests master tables: Replace with their specifications
|
|
→ IF user does NOT request: Keep as `master_tables: []`
|
|
→ Keep EXACT formatting if populating
|
|
|
|
---
|
|
|
|
### STEP 3: PRESERVE Everything Else
|
|
|
|
**MUST PRESERVE EXACTLY**:
|
|
- ✅ ALL comment blocks (`#####################################################`)
|
|
- ✅ ALL comment text ("Declare Validation logic", etc.)
|
|
- ✅ ALL blank lines
|
|
- ✅ ALL indentation (2 spaces per level)
|
|
- ✅ ALL YAML syntax
|
|
- ✅ Field ordering
|
|
- ✅ Spacing around colons and brackets
|
|
|
|
**NEVER**:
|
|
- ❌ Add new sections
|
|
- ❌ Remove comment blocks
|
|
- ❌ Change comment text
|
|
- ❌ Modify structure
|
|
- ❌ Change indentation
|
|
- ❌ Reorder sections
|
|
|
|
---
|
|
|
|
### STEP 4: Provide Structured Output
|
|
|
|
**After analysis, provide THIS format for the calling command:**
|
|
|
|
```markdown
|
|
## Extracted Keys (for unify.yml population):
|
|
|
|
**Keys to include in keys section:**
|
|
- email (valid_regexp: ".*@.*", invalid_texts: ['', 'N/A', 'null'])
|
|
- customer_id (invalid_texts: ['', 'N/A', 'null'])
|
|
- phone_number (invalid_texts: ['', 'N/A', 'null'])
|
|
|
|
**Tables to include in tables section:**
|
|
|
|
Database: db_name
|
|
├─ table1
|
|
│ └─ key_columns:
|
|
│ - {column: email_std, key: email}
|
|
│ - {column: customer_id, key: customer_id}
|
|
├─ table2
|
|
│ └─ key_columns:
|
|
│ - {column: email, key: email}
|
|
└─ table3
|
|
└─ key_columns:
|
|
- {column: email_address, key: email}
|
|
- {column: phone_number, key: phone_number}
|
|
|
|
**Canonical ID configuration:**
|
|
- name: {user_provided_canonical_id_name}
|
|
- merge_by_keys: [customer_id, email, phone_number] # Priority order from analysis
|
|
- merge_iterations: 15
|
|
|
|
**Master tables:**
|
|
- User requested: Yes/No
|
|
- If No: Use `master_tables: []`
|
|
- If Yes: [user specifications]
|
|
|
|
**Tables EXCLUDED (with reasons - DO NOT include in unify.yml):**
|
|
- database.table: Reason why excluded
|
|
```
|
|
|
|
---
|
|
|
|
### STEP 5: FINAL OUTPUT INSTRUCTIONS
|
|
|
|
**The calling command will**:
|
|
1. Take your structured output above
|
|
2. Use the BUILT-IN template structure (from STEP 1)
|
|
3. Replace ONLY the values you specified
|
|
4. Preserve ALL comment blocks, spacing, indentation, and blank lines
|
|
5. Use Write tool to save the populated unify.yml
|
|
|
|
**AGENT FINAL OUTPUT**: Provide the structured data in the format above. The calling command will handle template population using the BUILT-IN template structure.
|