Armor Recommend

Monitoring Recommendations

Get AI-driven recommendations for what to monitor and how to configure thresholds based on historical patterns.

Prerequisites

• AnomalyArmor API key configured (~/.armor/config.yaml or ARMOR_API_KEY env var)
• Python SDK installed (pip install anomalyarmor)
• Data source connected with discovery completed

When to Use

• "What should I monitor?"
• "Suggest tables to monitor"
• "What are good thresholds for this table?"
• "What's missing from my monitoring?"
• "Help me set up monitoring for my warehouse"
• "Which tables are most critical?"

Steps

Get Freshness Recommendations

1. Call client.recommendations.freshness() for asset
2. Review prioritized list of tables with suggested thresholds
3. For each table, see why it's recommended
4. Use /armor:test to dry-run before enabling
5. Use /armor:monitor to enable

Get Metrics Recommendations

1. Specify the table to analyze (optional)
2. Call client.recommendations.metrics() with table details
3. Review suggested metrics based on column analysis
4. Create metrics with /armor:quality

Analyze Coverage

1. Call client.recommendations.coverage() for asset
2. Review coverage percentage and gaps
3. Prioritize high-importance unmonitored tables
4. Use recommendations to fill gaps

Example Usage

Get Freshness Recommendations

from anomalyarmor import Client

client = Client()

# Get recommendations for which tables need freshness monitoring
recommendations = client.recommendations.freshness(
    asset_id="asset-uuid",
    min_confidence=0.7,
    limit=10
)

print(f"Freshness Recommendations ({len(recommendations.recommendations)}):")
print()

for rec in recommendations.recommendations:
    print(f"Table: {rec.table_path}")
    print(f"  Suggested interval: {rec.suggested_check_interval}")
    print(f"  Suggested threshold: {rec.suggested_threshold_hours} hours")
    print(f"  Detected frequency: {rec.detected_frequency}")
    print(f"  Confidence: {rec.confidence:.0%}")
    print(f"  Reason: {rec.reasoning}")
    print(f"  Data points: {rec.data_points}")
    print()

print(f"\nSummary:")
print(f"  Tables analyzed: {recommendations.tables_analyzed}")
print(f"  Tables with recommendations: {recommendations.tables_with_recommendations}")

Get Metrics Recommendations

from anomalyarmor import Client

client = Client()

# Get recommended metrics for a specific table
recommendations = client.recommendations.metrics(
    asset_id="asset-uuid",
    table_path="public.orders"  # Optional: omit for all tables
)

print(f"Metrics Recommendations ({len(recommendations.recommendations)}):")
print()

for rec in recommendations.recommendations:
    print(f"Table: {rec.table_path}")
    print(f"  Column: {rec.column_name}")
    print(f"  Suggested metric: {rec.suggested_metric_type}")
    print(f"  Confidence: {rec.confidence:.0%}")
    print(f"  Reason: {rec.reasoning}")
    print()

print(f"\nSummary:")
print(f"  Columns analyzed: {recommendations.columns_analyzed}")
print(f"  Columns with recommendations: {recommendations.columns_with_recommendations}")

Analyze Coverage Gaps

from anomalyarmor import Client

client = Client()

# Analyze monitoring coverage
coverage = client.recommendations.coverage(
    asset_id="asset-uuid"
)

print(f"Monitoring Coverage Analysis")
print(f"=" * 40)
print()

print(f"Coverage: {coverage.coverage_percentage:.1f}%")
print(f"  Total tables: {coverage.total_tables}")
print(f"  Monitored: {coverage.monitored_tables}")
print()

if coverage.recommendations:
    print("High-Priority Gaps:")
    for rec in coverage.recommendations[:5]:
        print(f"  - {rec.table_path}")
        print(f"    Importance: {rec.importance_score:.0%}")
        print(f"    Row count: {rec.row_count:,}")
        print(f"    Reason: {rec.reasoning}")
        print()

Get Threshold Tuning Suggestions

from anomalyarmor import Client

client = Client()

# Get threshold adjustment suggestions based on alert history
suggestions = client.recommendations.thresholds(
    asset_id="asset-uuid",
    days=30
)

print(f"Threshold Tuning Suggestions ({len(suggestions.recommendations)}):")
print()

for rec in suggestions.recommendations:
    print(f"Table: {rec.table_path}")
    print(f"  Current threshold: {rec.current_threshold}")
    print(f"  Suggested threshold: {rec.suggested_threshold}")
    print(f"  Direction: {rec.direction}")
    print(f"  Historical alerts: {rec.historical_alerts}")
    print(f"  Projected reduction: {rec.projected_reduction}")
    print(f"  Confidence: {rec.confidence:.0%}")
    print(f"  Reason: {rec.reasoning}")
    print()

Recommendation Types

Importance Factors

The recommendation engine considers:

• Update patterns: Detected frequency and regularity
• Column types: Numeric, string, datetime patterns
• Naming patterns: *_id, *_count, *_amount conventions
• Historical alerts: False positive/negative rates
• Table size: Row count as importance proxy

Follow-up Actions

• For freshness recommendations: Use /armor:test then /armor:monitor
• For metrics recommendations: Use /armor:quality to create metrics
• For coverage gaps: Prioritize by importance score
• For threshold tuning: Apply via /armor:monitor

Integration with Other Skills

• /armor:recommend -> Get suggestions
• /armor:test -> Dry-run suggested thresholds
• /armor:monitor -> Enable if dry-run looks good
• /armor:coverage -> See current coverage status

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