From newrelic
Application Performance Monitoring and transaction analysis. Use when investigating application errors, slow response times, throughput issues, or transaction-level problems.
How this skill is triggered — by the user, by Claude, or both
Slash command
/newrelic:apmThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Analyze application performance focusing on error rates, latency, throughput, and transaction behavior.
Analyze application performance focusing on error rates, latency, throughput, and transaction behavior.
This skill helps investigate and diagnose application-level performance issues including:
IMPORTANT: Use specialized analysis tools FIRST before writing custom NRQL queries.
Preferred Workflow:
analyze_golden_metrics - Automatically fetches and analyzes throughput, error rate, latency, and resource usage for an entitylist_top_transactions - Gets top transactions by volume, latency, or error rateanalyze_transactions - Analyzes specific transaction patterns and performanceexecute_nrql_query - Only for custom queries not covered by specialized toolsTool Selection Guide:
✓ BEST: analyze_golden_metrics(entity_guid="...")
→ Returns comprehensive performance metrics automatically
✓ GOOD: list_top_transactions(entity_guid="...", metric="duration")
→ Returns top slow transactions
⚠️ FALLBACK: execute_nrql_query(nrql_query="SELECT...")
→ Use only when specialized tools don't cover your need
Why use specialized tools?
CRITICAL: Always check dependencies when investigating application issues.
Workflow:
Discover Related Entities: Use list_related_entities with the application's entity GUID to find:
Check Dependency Health: For each related entity:
Correlate with Dependencies: Use the correlation_analysis skill to:
Example Query Pattern:
# After discovering database entity via list_related_entities
SELECT average(duration) FROM Span
WHERE entity.guid = '{DATABASE_GUID}'
FACET name
SINCE 1 hour ago
Common Dependency Issues:
Step 1: Use list_related_entities to discover dependencies
list_related_entities(entity_guid="{APP_GUID}")
This returns upstream/downstream services, databases, and external APIs.
Step 2: Query health of each dependency For each related entity, check:
Step 3: Activate correlation_analysis skill Pass application metrics + dependency metrics to correlation_analysis skill to:
Root Cause Categories:
Application code: Logic errors, inefficient algorithms, memory leaks
Database: Slow queries, connection pool exhaustion, deadlocks
list_related_entities to find database, query slow SQL statementsExternal service: Third-party API latency, timeouts, rate limiting
Infrastructure: Resource constraints, network issues, K8s problems
list_related_entitiesSegment Analysis:
Baseline Comparison:
Deployment Correlation:
list_recent_deploymentslist_related_entities to find related services, check their recent deploymentsExternal Dependencies:
list_related_entities(entity_guid="{APP_GUID}",
domain_filter=[{"domain": "EXT", "type": "SERVICE"}])
SELECT average(duration) FROM Span WHERE entity.guid = '{EXT_GUID}'For detailed performance patterns and optimization strategies, see Performance Metrics and Error Analysis.
Scenario: Application error rate increased from 0.5% to 8%
Step 1: Execute NRQL to get current state
SELECT count(*) as total, filter(count(*), WHERE error IS true) as errors,
percentage(count(*), WHERE error IS true) as errorRate
FROM Transaction WHERE appName = 'checkout-service'
SINCE 30 minutes ago COMPARE WITH 1 day ago
Step 2: Discover dependencies
list_related_entities(entity_guid="CHECKOUT_APP_GUID")
# Returns: postgres-db, payment-api (external), inventory-service (upstream)
Step 3: Check each dependency health
# Postgres
SELECT average(duration) FROM Span
WHERE entity.guid = 'POSTGRES_GUID' AND operation LIKE 'SELECT%'
TIMESERIES 5 minutes SINCE 30 minutes ago
# Payment API
SELECT count(*), percentage(count(*), WHERE error IS true)
FROM Span WHERE entity.guid = 'PAYMENT_API_GUID'
SINCE 30 minutes ago
Step 4: Activate correlation_analysis skill
correlation_analysis:
- Signal A: Checkout error rate 8% spike at 10:15 AM
- Signal B: Payment API error rate 12% at 10:14 AM
- Result: Strong temporal correlation, Payment API errors likely causing checkout errors
Step 5: Root Cause Identified Payment API (external dependency) experiencing issues → cascading to checkout service
Step 6: Recommendations
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