semantic-layer-audit

name	semantic-layer-audit
version	1.0.0
tier	foundation
description	Audit and maintain a data semantic layer for AI agents. Scans BigQuery datasets, GCP APIs, secrets, and service accounts to keep your data catalog current.
requires	{"bins":["gcloud","bq","python3"],"env":["GCP_PROJECT_ID"]}

Semantic Layer Audit

Why this matters: AI agents can only use data they know about. This skill maintains a living data catalog so you never lose track of available datasets, APIs, or credentials.

Prerequisites

• GCP Project with BigQuery enabled
• gcloud CLI authenticated (gcloud auth login)
• bq CLI (comes with gcloud)
• Environment variable: GCP_PROJECT_ID set to your project

# Set your project
export GCP_PROJECT_ID="your-project-id"

# Verify access
gcloud config set project $GCP_PROJECT_ID
bq ls --project_id=$GCP_PROJECT_ID

Quick Start

# Run full infrastructure audit
python3 scripts/audit_infrastructure.py > audit-results.json

# Review and update your semantic layer doc
# (see templates/SEMANTIC-LAYER-TEMPLATE.md)

When to Run

Trigger	Action
"What data do we have?"	Full audit
"Audit data sources"	Full audit
After creating views	Document with full schema + usage guidance
After new integrations	Infrastructure scan
Weekly maintenance	Cron: catch schema changes
Before complex analysis	Verify sources exist

---

Documentation Standards

The Problem

Most data catalogs are just lists of table names. This leads to:

• Agents querying wrong tables
• Missing context about when to use what
• No guidance on data source transitions
• Confusion when numbers don't match between sources

The Solution: Documentation Levels

Level 1: Table Documentation (Minimum)

Every table needs these fields:

Field	Required	Example
Name	✅	orders
Description	✅	"All completed orders"
Key Fields	✅	order_id, created_at, total
Granularity	✅	Per-order
Source	✅	"Synced from Shopify API"

Level 2: View Documentation (Full Detail)

Views require MORE documentation because users need to know when to use them vs raw tables:

#### `daily_sales_summary` (VIEW)

**Purpose:** Pre-aggregated daily sales metrics. Faster than aggregating raw orders.

**When to use:**
- Daily/weekly/monthly revenue trends
- High-level reporting dashboards
- Quick "how did yesterday go?" questions

**When NOT to use:**
- Need individual order details → use `orders`
- Need customer-level data → use `orders` joined with `customers`

| Column | Type | Description |
|--------|------|-------------|
| `date` | DATE | Sales date |
| `total_revenue` | FLOAT64 | Sum of order totals (dollars) |
| `order_count` | INT64 | Number of orders |
| `avg_order_value` | FLOAT64 | Revenue / orders |

**Example:**
\`\`\`sql
-- Last 30 days revenue trend
SELECT date, total_revenue, order_count
FROM `project.dataset.daily_sales_summary`
WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
ORDER BY date
\`\`\`

Level 3: Decision Tables

When multiple tables/views could answer similar questions, add a decision table:

### Choosing the Right Sales Data Source

| Question | Use This | Why |
|----------|----------|-----|
| "Total revenue last month?" | `daily_sales_summary` | Pre-aggregated, fast |
| "Revenue by product category?" | `order_items` | Has product details |
| "Specific customer's orders?" | `orders` | Has customer_id |
| "Hour-by-hour sales today?" | `orders` + aggregate | Summary is daily only |

Level 4: Source System Transitions

Document when underlying systems changed (critical for historical analysis):

### 🚨 SYSTEM TRANSITION

| Entity | Old System | New System | Cutover Date |
|--------|------------|------------|--------------|
| Orders | Legacy POS | Shopify | Jan 2024 |

**Implications:**
- YoY comparisons spanning the cutover need both systems
- Pre-Jan-2024 data in `legacy_orders`, post in `orders`
- Some fields don't exist in legacy (e.g., `discount_code`)

Level 5: Data Reconciliation Notes

When different sources show different numbers, explain WHY:

### 💡 Why Shopify Revenue ≠ Accounting Revenue

**Observation:** Shopify shows $100k, QuickBooks shows $94k for same month.

**Explanation:** 
- Shopify = Gross merchandise value (what customers paid)
- QuickBooks = Net revenue (after refunds, chargebacks, payment fees)

**Neither is wrong.** Use Shopify for customer-facing metrics, QuickBooks for P&L.

---

Audit Process

1. Infrastructure Scan

python3 scripts/audit_infrastructure.py

The script scans:

• BigQuery: All datasets, tables, views, and row counts
• Secrets: All Secret Manager secrets (credential inventory)
• APIs: All enabled Google Cloud APIs
• Service Accounts: All service accounts in the project

Output is JSON for easy processing.

2. Memory Pattern Discovery

Search your agent's memory for data access patterns not yet documented:

# Find BigQuery queries in recent sessions
memory_search "BigQuery query SELECT FROM"

# Find new views or tables created
memory_search "CREATE VIEW CREATE TABLE"

# Find API usage patterns  
memory_search "API endpoint curl"

Look for:

• Tables queried but not in your semantic layer doc
• Views created that need documentation
• API endpoints used successfully
• Join patterns between datasets
• Explanations of data discrepancies you've figured out

3. Gap Analysis

Compare audit results against your SEMANTIC-LAYER.md:

Completeness:

• New BigQuery tables/views documented
• New secrets/credentials listed
• New APIs documented

Quality (the important part):

• Views have full schema + "When to use" guidance
• Decision tables exist for related data sources
• Source transitions documented
• Data reconciliation notes where numbers differ
• Deprecated items marked with replacement pointers

4. Update Semantic Layer

Update your data catalog with:

• New tables (Level 1 minimum)
• New views (Level 2 - full detail)
• Decision tables (Level 3)
• System transitions (Level 4)
• Reconciliation notes (Level 5)
• Updated Quick Reference routing table
• Deprecated items marked with ⚠️

5. Track Discoveries

Log ad-hoc discoveries for future audits:

## Discovery Log

| Date | Discovery | Source | Added |
|------|-----------|--------|-------|
| 2024-01-15 | Tips not in revenue | Debug session | ✅ |
| 2024-01-18 | New product_prices view | Created for analysis | ✅ |

---

Quick Reference Table Standards

Maintain a routing table at the TOP of your semantic layer doc:

| Data Need | Source | Notes |
|-----------|--------|-------|
| **Daily Sales** | `daily_sales_summary` | Preferred - pre-aggregated |
| **Order Details** | `orders` | Full order records |
| **Product Prices** | `product_prices` (VIEW) | Use this! |
| **Legacy Orders** | `legacy_orders` | Pre-2024 only |

Use "Preferred" or "Use this!" to guide agents to the right source.

---

Deprecation Standards

When a table/view is superseded:

In the table listing:

| `old_table` | ⚠️ **Use `new_view` instead** - Legacy | ... |

Add a deprecation section:

## ⚠️ Deprecated Sources

| Old Source | Use Instead | Reason |
|------------|-------------|--------|
| `order_line_items` | `product_sales` view | Better schema, faster |
| `sales_raw` | `orders` | Renamed, same data |

---

Configuration

Environment Variables

Variable	Required	Description
GCP_PROJECT_ID	Yes	Your GCP project ID
SEMANTIC_LAYER_PATH	No	Path to your doc (default: docs/SEMANTIC-LAYER.md)

Customizing the Script

Edit scripts/audit_infrastructure.py to:

• Skip certain datasets (add to SKIP_DATASETS)
• Add custom API checks
• Adjust row count timeout

---

Files

File	Purpose
scripts/audit_infrastructure.py	Infrastructure scanner
templates/SEMANTIC-LAYER-TEMPLATE.md	Starter template

---

Cron Schedule (Recommended)

Weekly audit to catch schema changes:

0 6 * * 0

Text: "Run semantic layer audit - check for new BigQuery tables, views, API changes, and undocumented data patterns"

---

Post-Audit Checklist

After each audit:

• All tables documented (Level 1)
• All views have full documentation (Level 2)
• Decision tables for related sources (Level 3)
• System transitions noted (Level 4)
• Reconciliation notes where needed (Level 5)
• Quick Reference routing table updated
• Deprecated items marked with replacements
• Changes committed to git
• User notified of significant changes

---

Example Output

{
  "project_id": "your-project",
  "audit_time": "2024-01-15T10:30:00",
  "bigquery": {
    "datasets": [
      {
        "dataset_id": "sales_data",
        "tables": [
          {"table_id": "orders", "type": "TABLE", "row_count": 50000},
          {"table_id": "daily_summary", "type": "VIEW"}
        ]
      }
    ]
  },
  "secrets": ["api-key-service-a", "oauth-token-service-b"],
  "apis": ["bigquery.googleapis.com", "secretmanager.googleapis.com"]
}