Data Context Extractor

Data Context Extractor

A meta-skill that extracts company-specific data knowledge from analysts and generates tailored data analysis skills.

How It Works

This skill has two modes:

1. Bootstrap Mode: Create a new data analysis skill from scratch 2. Iteration Mode: Improve an existing skill by adding domain-specific reference files

---

Bootstrap Mode

Use when: User wants to create a new data context skill for their warehouse.

Phase 1: Database Connection & Discovery

Step 1: Identify the database type

Ask: "What data warehouse are you using?"

Common options:

• BigQuery
• Snowflake
• PostgreSQL/Redshift
• Databricks

Use ~~data warehouse tools (query and schema) to connect. If unclear, check available MCP tools in the current session.

Step 2: Explore the schema

Use ~~data warehouse schema tools to: 1. List available datasets/schemas 2. Identify the most important tables (ask user: "Which 3-5 tables do analysts query most often?") 3. Pull schema details for those key tables

Sample exploration queries by dialect:

-- BigQuery: List datasets
SELECT schema_name FROM INFORMATION_SCHEMA.SCHEMATA

-- BigQuery: List tables in a dataset
SELECT table_name FROM `project.dataset.INFORMATION_SCHEMA.TABLES`

-- Snowflake: List schemas
SHOW SCHEMAS IN DATABASE my_database

-- Snowflake: List tables
SHOW TABLES IN SCHEMA my_schema

Phase 2: Core Questions (Ask These)

After schema discovery, ask these questions conversationally (not all at once):

Entity Disambiguation (Critical)

"When people here say 'user' or 'customer', what exactly do they mean? Are there different types?"

Listen for:

• Multiple entity types (user vs account vs organization)
• Relationships between them (1:1, 1:many, many:many)
• Which ID fields link them together

Primary Identifiers

"What's the main identifier for a [customer/user/account]? Are there multiple IDs for the same entity?"

Listen for:

• Primary keys vs business keys
• UUID vs integer IDs
• Legacy ID systems

Key Metrics

"What are the 2-3 metrics people ask about most? How is each one calculated?"

Listen for:

• Exact formulas (ARR = monthly_revenue × 12)
• Which tables/columns feed each metric
• Time period conventions (trailing 7 days, calendar month, etc.)

Data Hygiene

"What should ALWAYS be filtered out of queries? (test data, fraud, internal users, etc.)"

Listen for:

• Standard WHERE clauses to always include
• Flag columns that indicate exclusions (is_test, is_internal, is_fraud)
• Specific values to exclude (status = 'deleted')

Common Gotchas

"What mistakes do new analysts typically make with this data?"

Listen for:

• Confusing column names
• Timezone issues
• NULL handling quirks
• Historical vs current state tables

Phase 3: Generate the Skill

Create a skill with this structure:

[company]-data-analyst/
├── SKILL.md
└── references/
    ├── entities.md          # Entity definitions and relationships
    ├── metrics.md           # KPI calculations
    ├── tables/              # One file per domain
    │   ├── [domain1].md
    │   └── [domain2].md
    └── dashboards.json      # Optional: existing dashboards catalog

SKILL.md Template: See references/skill-template.md

SQL Dialect Section: See references/sql-dialects.md and include the appropriate dialect notes.

Reference File Template: See references/domain-template.md

Phase 4: Package and Deliver

1. Create all files in the skill directory 2. Package as a zip file 3. Present to user with summary of what was captured

---

Iteration Mode

Use when: User has an existing skill but needs to add more context.

Step 1: Load Existing Skill

Ask user to upload their existing skill (zip or folder), or locate it if already in the session.

Read the current SKILL.md and reference files to understand what's already documented.

Step 2: Identify the Gap

Ask: "What domain or topic needs more context? What queries are failing or producing wrong results?"

Common gaps:

• A new data domain (marketing, finance, product, etc.)
• Missing metric definitions
• Undocumented table relationships
• New terminology

Step 3: Targeted Discovery

For the identified domain:

1. Explore relevant tables: Use ~~data warehouse schema tools to find tables in that domain 2. Ask domain-specific questions:

• "What tables are used for [domain] analysis?"
• "What are the key metrics for [domain]?"
• "Any special filters or gotchas for [domain] data?"

3. Generate new reference file: Create references/[domain].md using the domain template

Step 4: Update and Repackage

1. Add the new reference file 2. Update SKILL.md's "Knowledge Base Navigation" section to include the new domain 3. Repackage the skill 4. Present the updated skill to user

---

Reference File Standards

Each reference file should include:

For Table Documentation

• Location: Full table path
• Description: What this table contains, when to use it
• Primary Key: How to uniquely identify rows
• Update Frequency: How often data refreshes
• Key Columns: Table with column name, type, description, notes
• Relationships: How this table joins to others
• Sample Queries: 2-3 common query patterns

For Metrics Documentation

• Metric Name: Human-readable name
• Definition: Plain English explanation
• Formula: Exact calculation with column references
• Source Table(s): Where the data comes from
• Caveats: Edge cases, exclusions, gotchas

For Entity Documentation

• Entity Name: What it's called
• Definition: What it represents in the business
• Primary Table: Where to find this entity
• ID Field(s): How to identify it
• Relationships: How it relates to other entities
• Common Filters: Standard exclusions (internal, test, etc.)

---

Quality Checklist

Before delivering a generated skill, verify:

• [ ] SKILL.md has complete frontmatter (name, description)
• [ ] Entity disambiguation section is clear
• [ ] Key terminology is defined
• [ ] Standard filters/exclusions are documented
• [ ] At least 2-3 sample queries per domain
• [ ] SQL uses correct dialect syntax
• [ ] Reference files are linked from SKILL.md navigation section

Domain Reference File Template

Use this template when creating reference files for specific data domains (e.g., revenue, users, marketing).

---

# [DOMAIN_NAME] Tables

This document contains [domain]-related tables, metrics, and query patterns.

---

## Quick Reference

### Business Context

[2-3 sentences explaining what this domain covers and key concepts]

### Entity Clarification

**"[AMBIGUOUS_TERM]" can mean:**
- **[MEANING_1]**: [DEFINITION] ([TABLE]: [ID_FIELD])
- **[MEANING_2]**: [DEFINITION] ([TABLE]: [ID_FIELD])

Always clarify which one before querying.

### Standard Filters

For [domain] queries, always:

WHERE [STANDARD_FILTER_1] AND [STANDARD_FILTER_2]

---

## Key Tables

### [TABLE_1_NAME]
**Location**: `[project.dataset.table]` or `[schema.table]`
**Description**: [What this table contains, when to use it]
**Primary Key**: [COLUMN(S)]
**Update Frequency**: [Daily/Hourly/Real-time] ([LAG] lag)
**Partitioned By**: [PARTITION_COLUMN] (if applicable)

| Column | Type | Description | Notes |
|--------|------|-------------|-------|
| **[column_1]** | [TYPE] | [DESCRIPTION] | [GOTCHA_OR_CONTEXT] |
| **[column_2]** | [TYPE] | [DESCRIPTION] | |
| **[column_3]** | [TYPE] | [DESCRIPTION] | Nullable |

**Relationships**:
- Joins to `[OTHER_TABLE]` on `[JOIN_KEY]`
- Parent of `[CHILD_TABLE]` via `[FOREIGN_KEY]`

**Nested/Struct Fields** (if applicable):
- `[struct_name].[field_1]`: [DESCRIPTION]
- `[struct_name].[field_2]`: [DESCRIPTION]

---

### [TABLE_2_NAME]
[REPEAT FORMAT]

---

## Key Metrics

| Metric | Definition | Table | Formula | Notes |
|--------|------------|-------|---------|-------|
| [METRIC_1] | [DEFINITION] | [TABLE] | `[FORMULA]` | [CAVEATS] |
| [METRIC_2] | [DEFINITION] | [TABLE] | `[FORMULA]` | |

---

## Sample Queries

### [QUERY_PURPOSE_1]

-- [Brief description of what this query does] SELECT [columns] FROM [table] WHERE [standard_filters] GROUP BY [grouping] ORDER BY [ordering]

### [QUERY_PURPOSE_2]

[ANOTHER_COMMON_QUERY]

### [QUERY_PURPOSE_3]: [More Complex Pattern]

WITH [cte_name] AS ( [CTE_LOGIC] ) SELECT [final_columns] FROM [cte_name] [joins_and_filters]

---

## Common Gotchas

1. **[GOTCHA_1]**: [EXPLANATION]
   - Wrong: `[INCORRECT_APPROACH]`
   - Right: `[CORRECT_APPROACH]`

2. **[GOTCHA_2]**: [EXPLANATION]

---

## Related Dashboards (if applicable)

| Dashboard | Link | Use For |
|-----------|------|---------|
| [DASHBOARD_1] | [URL] | [DESCRIPTION] |
| [DASHBOARD_2] | [URL] | [DESCRIPTION] |

---

Tips for Creating Domain Files

1. Start with the most-queried tables - Don't try to document everything 2. Include column-level detail only for important columns - Skip obvious ones like created_at 3. Real query examples > abstract descriptions - Show don't tell 4. Document the gotchas prominently - These save the most time 5. Keep sample queries runnable - Use real table/column names 6. Note nested/struct fields explicitly - These trip people up

Suggested Domain Files

Common domains to document (create separate files for each):

• revenue.md - Billing, subscriptions, ARR, transactions
• users.md - Accounts, authentication, user attributes
• product.md - Feature usage, events, sessions
• growth.md - DAU/WAU/MAU, retention, activation
• sales.md - CRM, pipeline, opportunities
• marketing.md - Campaigns, attribution, leads
• support.md - Tickets, CSAT, response times

Example: Generated Skill

This is an example of what a generated skill looks like after the bootstrap process. This example is for a fictional e-commerce company called "ShopCo" using Snowflake.

---

Example SKILL.md

---
name: shopco-data-analyst
description: "ShopCo data analysis skill for Snowflake. Provides context for querying e-commerce data including customer, order, and product analytics. Use when analyzing ShopCo data for: (1) Revenue and order metrics, (2) Customer behavior and retention, (3) Product performance, or any data questions requiring ShopCo-specific context."
---

# ShopCo Data Analysis

## SQL Dialect: Snowflake

- **Table references**: `SHOPCO_DW.SCHEMA.TABLE` or with quotes for case-sensitive: `"Column_Name"`
- **Safe division**: `DIV0(a, b)` returns 0, `DIV0NULL(a, b)` returns NULL
- **Date functions**:
  - `DATE_TRUNC('MONTH', date_col)`
  - `DATEADD(DAY, -1, date_col)`
  - `DATEDIFF(DAY, start_date, end_date)`
- **Column exclusion**: `SELECT * EXCLUDE (column_to_exclude)`

---

## Entity Disambiguation

**"Customer" can mean:**
- **User**: A login account that can browse and save items (CORE.DIM_USERS: user_id)
- **Customer**: A user who has made at least one purchase (CORE.DIM_CUSTOMERS: customer_id)
- **Account**: A billing entity, can have multiple users in B2B (CORE.DIM_ACCOUNTS: account_id)

**Relationships:**
- User → Customer: 1:1 (customer_id = user_id for purchasers)
- Account → User: 1:many (join on account_id)

---

## Business Terminology

| Term | Definition | Notes |
|------|------------|-------|
| GMV | Gross Merchandise Value - total order value before returns/discounts | Use for top-line reporting |
| NMV | Net Merchandise Value - GMV minus returns and discounts | Use for actual revenue |
| AOV | Average Order Value - NMV / order count | Exclude $0 orders |
| LTV | Lifetime Value - total NMV per customer since first order | Rolling calc, updates daily |
| CAC | Customer Acquisition Cost - marketing spend / new customers | By cohort month |

---

## Standard Filters

Always apply these filters unless explicitly told otherwise:

-- Exclude test and internal orders WHERE order_status != 'TEST' AND customer_type != 'INTERNAL' AND is_employee_order = FALSE

-- Exclude cancelled orders for revenue metrics AND order_status NOT IN ('CANCELLED', 'FRAUDULENT')

---

## Key Metrics

### Gross Merchandise Value (GMV)
- **Definition**: Total value of all orders placed
- **Formula**: `SUM(order_total_gross)`
- **Source**: `CORE.FCT_ORDERS.order_total_gross`
- **Time grain**: Daily, aggregated to weekly/monthly
- **Caveats**: Includes orders that may later be cancelled or returned

### Net Revenue
- **Definition**: Actual revenue after returns and discounts
- **Formula**: `SUM(order_total_gross - return_amount - discount_amount)`
- **Source**: `CORE.FCT_ORDERS`
- **Caveats**: Returns can occur up to 90 days post-order; use settled_revenue for finalized numbers

---

## Knowledge Base Navigation

| Domain | Reference File | Use For |
|--------|----------------|---------|
| Orders | `references/orders.md` | Order tables, GMV/NMV calculations |
| Customers | `references/customers.md` | User/customer entities, LTV, cohorts |
| Products | `references/products.md` | Catalog, inventory, categories |

---

## Common Query Patterns

### Daily GMV by Channel

SELECT DATE_TRUNC('DAY', order_timestamp) AS order_date, channel, SUM(order_total_gross) AS gmv, COUNT(DISTINCT order_id) AS order_count FROM SHOPCO_DW.CORE.FCT_ORDERS WHERE order_status NOT IN ('TEST', 'CANCELLED', 'FRAUDULENT') AND order_timestamp >= DATEADD(DAY, -30, CURRENT_DATE()) GROUP BY 1, 2 ORDER BY 1 DESC, 3 DESC

### Customer Cohort Retention

WITH cohorts AS ( SELECT customer_id, DATE_TRUNC('MONTH', first_order_date) AS cohort_month FROM SHOPCO_DW.CORE.DIM_CUSTOMERS ) SELECT c.cohort_month, DATEDIFF(MONTH, c.cohort_month, DATE_TRUNC('MONTH', o.order_timestamp)) AS months_since_first, COUNT(DISTINCT c.customer_id) AS active_customers FROM cohorts c JOIN SHOPCO_DW.CORE.FCT_ORDERS o ON c.customer_id = o.customer_id WHERE o.order_status NOT IN ('TEST', 'CANCELLED') GROUP BY 1, 2 ORDER BY 1, 2

---

Example references/orders.md

# Orders Tables

Order and transaction data for ShopCo.

---

## Key Tables

### FCT_ORDERS
**Location**: `SHOPCO_DW.CORE.FCT_ORDERS`
**Description**: Fact table of all orders. One row per order.
**Primary Key**: `order_id`
**Update Frequency**: Hourly (15 min lag)
**Partitioned By**: `order_date`

| Column | Type | Description | Notes |
|--------|------|-------------|-------|
| **order_id** | VARCHAR | Unique order identifier | |
| **customer_id** | VARCHAR | FK to DIM_CUSTOMERS | NULL for guest checkout |
| **order_timestamp** | TIMESTAMP_NTZ | When order was placed | UTC |
| **order_date** | DATE | Date portion of order_timestamp | Partition column |
| **order_status** | VARCHAR | Current status | PENDING, SHIPPED, DELIVERED, CANCELLED, RETURNED |
| **channel** | VARCHAR | Acquisition channel | WEB, APP, MARKETPLACE |
| **order_total_gross** | DECIMAL(12,2) | Pre-discount total | |
| **discount_amount** | DECIMAL(12,2) | Total discounts applied | |
| **return_amount** | DECIMAL(12,2) | Value of returned items | Updates async |

**Relationships**:
- Joins to `DIM_CUSTOMERS` on `customer_id`
- Parent of `FCT_ORDER_ITEMS` via `order_id`

---

## Sample Queries

### Orders with Returns Rate

SELECT DATE_TRUNC('WEEK', order_date) AS week, COUNT() AS total_orders, SUM(CASE WHEN return_amount > 0 THEN 1 ELSE 0 END) AS orders_with_returns, DIV0(SUM(CASE WHEN return_amount > 0 THEN 1 ELSE 0 END), COUNT()) AS return_rate FROM SHOPCO_DW.CORE.FCT_ORDERS WHERE order_status NOT IN ('TEST', 'CANCELLED') AND order_date >= DATEADD(MONTH, -3, CURRENT_DATE()) GROUP BY 1 ORDER BY 1

---

This example demonstrates:

• Complete frontmatter with triggering description
• Dialect-specific SQL notes
• Clear entity disambiguation
• Terminology glossary
• Standard filters as copy-paste SQL
• Metric definitions with formulas
• Navigation to reference files
• Real, runnable query examples

Generated Skill Template

Use this template when generating a new data analysis skill. Replace all [PLACEHOLDER] values.

---

---
name: [company]-data-analyst
description: "[COMPANY] data analysis skill. Provides context for querying [WAREHOUSE_TYPE] including entity definitions, metric calculations, and common query patterns. Use when analyzing [COMPANY] data for: (1) [PRIMARY_USE_CASE_1], (2) [PRIMARY_USE_CASE_2], (3) [PRIMARY_USE_CASE_3], or any data questions requiring [COMPANY]-specific context."
---

# [COMPANY] Data Analysis

## SQL Dialect: [WAREHOUSE_TYPE]

[INSERT APPROPRIATE DIALECT SECTION FROM sql-dialects.md]

---

## Entity Disambiguation

When users mention these terms, clarify which entity they mean:

[EXAMPLE FORMAT - customize based on discovery:]

**"User" can mean:**
- **Account**: An individual login/profile ([PRIMARY_TABLE]: [ID_FIELD])
- **Organization**: A billing entity that can have multiple accounts ([ORG_TABLE]: [ORG_ID])
- **[OTHER_TYPE]**: [DEFINITION] ([TABLE]: [ID])

**Relationships:**
- [ENTITY_1] → [ENTITY_2]: [RELATIONSHIP_TYPE] (join on [JOIN_KEY])

---

## Business Terminology

| Term | Definition | Notes |
|------|------------|-------|
| [TERM_1] | [DEFINITION] | [CONTEXT/GOTCHA] |
| [TERM_2] | [DEFINITION] | [CONTEXT/GOTCHA] |
| [ACRONYM] | [FULL_NAME] - [EXPLANATION] | |

---

## Standard Filters

Always apply these filters unless explicitly told otherwise:

-- Exclude test/internal data WHERE [TEST_FLAG_COLUMN] = FALSE AND [INTERNAL_FLAG_COLUMN] = FALSE

-- Exclude invalid/fraud AND [STATUS_COLUMN] != '[EXCLUDED_STATUS]'

-- [OTHER STANDARD EXCLUSIONS]

**When to override:**
- [SCENARIO_1]: Include [NORMALLY_EXCLUDED] when [CONDITION]

---

## Key Metrics

### [METRIC_1_NAME]
- **Definition**: [PLAIN_ENGLISH_EXPLANATION]
- **Formula**: `[EXACT_CALCULATION]`
- **Source**: `[TABLE_NAME].[COLUMN_NAME]`
- **Time grain**: [DAILY/WEEKLY/MONTHLY]
- **Caveats**: [EDGE_CASES_OR_GOTCHAS]

### [METRIC_2_NAME]
[REPEAT FORMAT]

---

## Data Freshness

| Table | Update Frequency | Typical Lag |
|-------|------------------|-------------|
| [TABLE_1] | [FREQUENCY] | [LAG] |
| [TABLE_2] | [FREQUENCY] | [LAG] |

To check data freshness:

SELECT MAX([DATE_COLUMN]) as latest_data FROM [TABLE]

---

## Knowledge Base Navigation

Use these reference files for detailed table documentation:

| Domain | Reference File | Use For |
|--------|----------------|---------|
| [DOMAIN_1] | `references/[domain1].md` | [BRIEF_DESCRIPTION] |
| [DOMAIN_2] | `references/[domain2].md` | [BRIEF_DESCRIPTION] |
| Entities | `references/entities.md` | Entity definitions and relationships |
| Metrics | `references/metrics.md` | KPI calculations and formulas |

---

## Common Query Patterns

### [PATTERN_1_NAME]

[SAMPLE_QUERY]

### [PATTERN_2_NAME]

[SAMPLE_QUERY]

---

## Troubleshooting

### Common Mistakes
- **[MISTAKE_1]**: [EXPLANATION] → [CORRECT_APPROACH]
- **[MISTAKE_2]**: [EXPLANATION] → [CORRECT_APPROACH]

### Access Issues
- If you encounter permission errors on `[TABLE]`: [WORKAROUND]
- For PII-restricted columns: [ALTERNATIVE_APPROACH]

### Performance Tips
- Filter by `[PARTITION_COLUMN]` first to reduce data scanned
- For large tables, use `LIMIT` during exploration
- Prefer `[AGGREGATED_TABLE]` over `[RAW_TABLE]` when possible

---

Customization Notes

When generating a skill:

1. Fill all placeholders - Don't leave any [PLACEHOLDER] text 2. Remove unused sections - If they don't have dashboards, remove that section 3. Add specificity - Generic advice is less useful than specific column names and values 4. Include real examples - Sample queries should use actual table/column names 5. Keep it scannable - Use tables and code blocks liberally

SQL Dialect Reference

Include the appropriate section in generated skills based on the user's data warehouse.

---

BigQuery

## SQL Dialect: BigQuery

- **Table references**: Use backticks: \`project.dataset.table\`
- **Safe division**: `SAFE_DIVIDE(a, b)` returns NULL instead of error
- **Date functions**:
  - `DATE_TRUNC(date_col, MONTH)`
  - `DATE_SUB(date_col, INTERVAL 1 DAY)`
  - `DATE_DIFF(end_date, start_date, DAY)`
- **Column exclusion**: `SELECT * EXCEPT(column_to_exclude)`
- **Arrays**: `UNNEST(array_column)` to flatten
- **Structs**: Access with dot notation `struct_col.field_name`
- **Timestamps**: `TIMESTAMP_TRUNC()`, times in UTC by default
- **String matching**: `LIKE`, `REGEXP_CONTAINS(col, r'pattern')`
- **NULLs in aggregations**: Most functions ignore NULLs; use `IFNULL()` or `COALESCE()`

---

Snowflake

## SQL Dialect: Snowflake

- **Table references**: `DATABASE.SCHEMA.TABLE` or with quotes for case-sensitive: `"Column_Name"`
- **Safe division**: `DIV0(a, b)` returns 0, `DIV0NULL(a, b)` returns NULL
- **Date functions**:
  - `DATE_TRUNC('MONTH', date_col)`
  - `DATEADD(DAY, -1, date_col)`
  - `DATEDIFF(DAY, start_date, end_date)`
- **Column exclusion**: `SELECT * EXCLUDE (column_to_exclude)`
- **Arrays**: `FLATTEN(array_column)` to flatten, access with `value`
- **Variants/JSON**: Access with colon notation `variant_col:field_name`
- **Timestamps**: `TIMESTAMP_NTZ` (no timezone), `TIMESTAMP_TZ` (with timezone)
- **String matching**: `LIKE`, `REGEXP_LIKE(col, 'pattern')`
- **Case sensitivity**: Identifiers are uppercase by default unless quoted

---

PostgreSQL / Redshift

## SQL Dialect: PostgreSQL/Redshift

- **Table references**: `schema.table` (lowercase convention)
- **Safe division**: `NULLIF(b, 0)` pattern: `a / NULLIF(b, 0)`
- **Date functions**:
  - `DATE_TRUNC('month', date_col)`
  - `date_col - INTERVAL '1 day'`
  - `DATE_PART('day', end_date - start_date)`
- **Column selection**: No EXCEPT; must list columns explicitly
- **Arrays**: `UNNEST(array_column)` (PostgreSQL), limited in Redshift
- **JSON**: `json_col->>'field_name'` for text, `json_col->'field_name'` for JSON
- **Timestamps**: `AT TIME ZONE 'UTC'` for timezone conversion
- **String matching**: `LIKE`, `col ~ 'pattern'` for regex
- **Boolean**: Native BOOLEAN type; use `TRUE`/`FALSE`

---

Databricks / Spark SQL

## SQL Dialect: Databricks/Spark SQL

- **Table references**: `catalog.schema.table` (Unity Catalog) or `schema.table`
- **Safe division**: Use `NULLIF`: `a / NULLIF(b, 0)` or `TRY_DIVIDE(a, b)`
- **Date functions**:
  - `DATE_TRUNC('MONTH', date_col)`
  - `DATE_SUB(date_col, 1)`
  - `DATEDIFF(end_date, start_date)`
- **Column exclusion**: `SELECT * EXCEPT (column_to_exclude)` (Databricks SQL)
- **Arrays**: `EXPLODE(array_column)` to flatten
- **Structs**: Access with dot notation `struct_col.field_name`
- **JSON**: `json_col:field_name` or `GET_JSON_OBJECT()`
- **String matching**: `LIKE`, `RLIKE` for regex
- **Delta features**: `DESCRIBE HISTORY`, time travel with `VERSION AS OF`

---

MySQL

## SQL Dialect: MySQL

- **Table references**: \`database\`.\`table\` with backticks
- **Safe division**: Manual: `IF(b = 0, NULL, a / b)` or `a / NULLIF(b, 0)`
- **Date functions**:
  - `DATE_FORMAT(date_col, '%Y-%m-01')` for truncation
  - `DATE_SUB(date_col, INTERVAL 1 DAY)`
  - `DATEDIFF(end_date, start_date)`
- **Column selection**: No EXCEPT; must list columns explicitly
- **Arrays**: Limited native support; often stored as JSON
- **JSON**: `JSON_EXTRACT(col, '$.field')` or `col->>'$.field'`
- **Timestamps**: `CONVERT_TZ()` for timezone conversion
- **String matching**: `LIKE`, `REGEXP` for regex
- **Case sensitivity**: Table names case-sensitive on Linux, not on Windows

---

Common Patterns Across Dialects