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Use when investigating application errors, slow response times, throughput issues, or transaction-level problems.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"new-relic","New Relic","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnew-relic.jpg","newrelic",[13,17,20,22],{"name":14,"slug":15,"type":16},"Observability","observability","tag",{"name":18,"slug":19,"type":16},"Monitoring","monitoring",{"name":21,"slug":4,"type":16},"APM",{"name":23,"slug":24,"type":16},"Debugging","debugging",0,"https:\u002F\u002Fgithub.com\u002Fnewrelic\u002Fclaude-code-plugin","2026-07-13T06:46:00.838801",null,1,[],{"repoUrl":26,"stars":25,"forks":29,"topics":32,"description":28},[],"https:\u002F\u002Fgithub.com\u002Fnewrelic\u002Fclaude-code-plugin\u002Ftree\u002FHEAD\u002Fskills\u002Fapm","---\nname: apm\ndescription: Application Performance Monitoring and transaction analysis. Use when investigating application errors, slow response times, throughput issues, or transaction-level problems.\nmetadata:\n  keywords:\n    - apm\n    - application performance\n    - app performance\n    - transaction error\n    - transaction latency\n    - transaction duration\n    - application error\n    - error rate\n    - response time\n    - latency\n    - throughput\n    - application throughput\n    - apdex\n    - slow transaction\n    - transaction timeout\n    - http transaction\n    - api endpoint\n    - application service\n    - microservice\n    - service map\n    - web transaction\n    - backend transaction\n  tool_groups:\n    - nrql_query\n    - account_entity\n    - alert_incidents\n    - change_deployment\n    - performance_analysis\n    - utilities\n  related_skills:\n    kubernetes: for container\u002Fpod problems\n    database: for slow queries or connection issues\n    network: for API timeout or connectivity issues\n  generic_skills_usage:\n    data_retrieval: for transaction data (Transaction, TransactionError, Span)\n    metric_analysis: for latency and throughput trend analysis\n    correlation_analysis: to link errors with deployments or infrastructure changes\nallowed-tools: execute_nrql_query lookup_entity search_entity_with_tag list_recent_issues list_recent_deployments list_related_entities\n---\n\n# Application Performance Monitoring\n\nAnalyze application performance focusing on error rates, latency, throughput, and transaction behavior.\n\n## Core Responsibility\n\nThis skill helps investigate and diagnose application-level performance issues including:\n- High error rates and error patterns\n- Slow response times and latency spikes\n- Throughput degradation\n- Transaction failures and timeouts\n- Apdex score degradation\n- Service dependencies and external calls\n\n## Critical Workflow Note\n\n**IMPORTANT:** Use specialized analysis tools FIRST before writing custom NRQL queries.\n\n**Preferred Workflow:**\n1. **Use `analyze_golden_metrics`** - Automatically fetches and analyzes throughput, error rate, latency, and resource usage for an entity\n2. **Use `list_top_transactions`** - Gets top transactions by volume, latency, or error rate\n3. **Use `analyze_transactions`** - Analyzes specific transaction patterns and performance\n4. **Use `execute_nrql_query`** - Only for custom queries not covered by specialized tools\n\n**Tool Selection Guide:**\n```\n✓ BEST: analyze_golden_metrics(entity_guid=\"...\")\n  → Returns comprehensive performance metrics automatically\n\n✓ GOOD: list_top_transactions(entity_guid=\"...\", metric=\"duration\")\n  → Returns top slow transactions\n\n⚠️ FALLBACK: execute_nrql_query(nrql_query=\"SELECT...\")\n  → Use only when specialized tools don't cover your need\n```\n\n**Why use specialized tools?**\n- Pre-built queries optimized for performance\n- Consistent metric collection across investigations\n- Automatic baseline comparisons\n- Structured output for easier analysis\n\n## Key Performance Metrics\n\n### Error Rate\n- **Definition:** Percentage of failed requests\n- **Target:** Typically \u003C 1% for healthy applications\n- **Investigation:** Segment by transaction name, error type, host\n- **Correlate with:** Deployments, infrastructure changes, dependencies\n\n### Response Time \u002F Latency\n- **Metrics:** Average, median, p95, p99 response times\n- **Target:** Depends on SLAs (e.g., p95 \u003C 200ms for API endpoints)\n- **Investigation:** Identify slow transactions, analyze span timing\n- **Correlate with:** Database queries, external service calls, resource usage\n\n### Throughput\n- **Definition:** Requests per minute (rpm)\n- **Investigation:** Look for drops or spikes\n- **Correlate with:** Error rates, response times, infrastructure capacity\n\n### Apdex Score\n- **Definition:** User satisfaction metric (0.0 to 1.0)\n- **Target:** > 0.9 for good user experience\n- **Investigation:** Understand which transactions are degrading Apdex\n\n## Investigation Approach\n\n### 1. Identify Performance Baseline\n- Establish normal behavior (historical data)\n- Compare current metrics to baseline\n- Detect anomalies and deviations\n- Consider time-of-day and seasonal patterns\n\n### 2. Analyze Transaction Traces\n- Find slowest transactions\n- Examine span breakdown (app code, database, external services)\n- Identify bottlenecks in transaction flow\n- Look for N+1 query patterns\n\n### 3. Examine Error Patterns\n- Group errors by type and message\n- Analyze stack traces for root cause\n- Check error frequency and affected transactions\n- Look for cascading failures\n\n### 4. Correlate with Changes\n- Check recent deployments (code changes)\n- Review infrastructure changes (scaling, configuration)\n- Look for dependency changes (database, external APIs)\n- Consider traffic pattern changes\n\n### 5. Analyze Service Dependencies\n\n**CRITICAL:** Always check dependencies when investigating application issues.\n\n**Workflow:**\n1. **Discover Related Entities:** Use `list_related_entities` with the application's entity GUID to find:\n   - Upstream services (services this app calls)\n   - Downstream services (services that call this app)\n   - Connected databases\n   - External APIs and integrations\n\n2. **Check Dependency Health:** For each related entity:\n   - Query error rates and latency metrics\n   - Compare health status against normal baselines\n   - Look for cascading failures\n\n3. **Correlate with Dependencies:** Use the `correlation_analysis` skill to:\n   - Link application errors with database slow queries\n   - Correlate latency spikes with external service timeouts\n   - Identify if upstream service failures are causing downstream errors\n\n**Example Query Pattern:**\n```nrql\n# After discovering database entity via list_related_entities\nSELECT average(duration) FROM Span\nWHERE entity.guid = '{DATABASE_GUID}'\nFACET name\nSINCE 1 hour ago\n```\n\n**Common Dependency Issues:**\n- Database connection pool exhaustion causing app errors\n- External API timeouts cascading to app latency\n- Upstream service high error rate causing downstream failures\n- Cache service issues causing increased database load\n\n### 6. Identify Root Cause\n\n**Step 1: Use list_related_entities to discover dependencies**\n```\nlist_related_entities(entity_guid=\"{APP_GUID}\")\n```\nThis returns upstream\u002Fdownstream services, databases, and external APIs.\n\n**Step 2: Query health of each dependency**\nFor each related entity, check:\n- Error rates\n- Response times\n- Throughput changes\n- Recent deployments\n\n**Step 3: Activate correlation_analysis skill**\nPass application metrics + dependency metrics to correlation_analysis skill to:\n- Find temporal correlations (errors happen at same time)\n- Identify causal relationships (database slow → app slow)\n- Calculate correlation coefficients\n- Provide confidence levels\n\n**Root Cause Categories:**\n- **Application code:** Logic errors, inefficient algorithms, memory leaks\n  - *Action:* Review recent code changes, analyze slow transaction traces\n\n- **Database:** Slow queries, connection pool exhaustion, deadlocks\n  - *Action:* Use `list_related_entities` to find database, query slow SQL statements\n\n- **External service:** Third-party API latency, timeouts, rate limiting\n  - *Action:* Check related external entities for health degradation\n\n- **Infrastructure:** Resource constraints, network issues, K8s problems\n  - *Action:* Activate kubernetes skill if related pods found via `list_related_entities`\n\n## Best Practices\n\n**Segment Analysis:**\n- Break down by transaction name to identify problematic endpoints\n- Segment by host to find infrastructure issues\n- Group by region\u002Fdatacenter for geographic patterns\n- Analyze by customer\u002Ftenant for multi-tenant applications\n\n**Baseline Comparison:**\n- Compare current behavior with historical baselines\n- Use similar time windows (e.g., same day of week, same hour)\n- Account for seasonal patterns and known events\n- Set dynamic thresholds based on historical variance\n\n**Deployment Correlation:**\n1. Check for recent deployments using `list_recent_deployments`\n2. **Use correlation_analysis skill to determine if deployment caused issues:**\n   - Pass deployment timestamp + error rate spike timestamp\n   - Check for temporal correlation (errors within 5 minutes of deployment)\n   - Calculate confidence level (strong\u002Fmoderate\u002Fweak)\n3. Analyze deployment diff for risky changes\n4. Check if issues isolated to canary\u002Fspecific hosts\n5. **Check dependency deployments:** Use `list_related_entities` to find related services, check their recent deployments\n\n**External Dependencies:**\n1. **Discover dependencies:**\n   ```\n   list_related_entities(entity_guid=\"{APP_GUID}\",\n                         domain_filter=[{\"domain\": \"EXT\", \"type\": \"SERVICE\"}])\n   ```\n2. **For each external dependency:**\n   - Query response times: `SELECT average(duration) FROM Span WHERE entity.guid = '{EXT_GUID}'`\n   - Check error rates\n   - Look for timeout patterns\n3. **Activate correlation_analysis:**\n   - Correlate app latency with external service latency\n   - Identify if cascading failures from dependencies\n4. Verify circuit breaker behavior and fallback mechanisms\n\n## Common Performance Issues\n\nFor detailed performance patterns and optimization strategies, see [Performance Metrics](references\u002FPERFORMANCE_METRICS.md) and [Error Analysis](references\u002FERROR_ANALYSIS.md).\n\n## Complete Investigation Example\n\n**Scenario:** Application error rate increased from 0.5% to 8%\n\n**Step 1: Execute NRQL to get current state**\n```nrql\nSELECT count(*) as total, filter(count(*), WHERE error IS true) as errors,\n       percentage(count(*), WHERE error IS true) as errorRate\nFROM Transaction WHERE appName = 'checkout-service'\nSINCE 30 minutes ago COMPARE WITH 1 day ago\n```\n\n**Step 2: Discover dependencies**\n```\nlist_related_entities(entity_guid=\"CHECKOUT_APP_GUID\")\n# Returns: postgres-db, payment-api (external), inventory-service (upstream)\n```\n\n**Step 3: Check each dependency health**\n```nrql\n# Postgres\nSELECT average(duration) FROM Span\nWHERE entity.guid = 'POSTGRES_GUID' AND operation LIKE 'SELECT%'\nTIMESERIES 5 minutes SINCE 30 minutes ago\n\n# Payment API\nSELECT count(*), percentage(count(*), WHERE error IS true)\nFROM Span WHERE entity.guid = 'PAYMENT_API_GUID'\nSINCE 30 minutes ago\n```\n\n**Step 4: Activate correlation_analysis skill**\n```\ncorrelation_analysis:\n- Signal A: Checkout error rate 8% spike at 10:15 AM\n- Signal B: Payment API error rate 12% at 10:14 AM\n- Result: Strong temporal correlation, Payment API errors likely causing checkout errors\n```\n\n**Step 5: Root Cause Identified**\nPayment API (external dependency) experiencing issues → cascading to checkout service\n\n**Step 6: Recommendations**\n- Activate circuit breaker for payment API calls\n- Implement graceful degradation\n- Contact payment provider about outage\n\n## Related Skills\n\n- **Kubernetes Skill:** Activate when performance issues are caused by container\u002Fpod problems\n- **Database Skill:** Activate when seeing slow database queries or connection issues\n- 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