From datadops
Deep performance analysis using APM traces, metrics, and logs to identify bottlenecks and optimization opportunities. Correlates database queries, external API calls, and resource usage to pinpoint root causes of slow performance. Use when investigating latency issues, throughput problems, or resource optimization needs.
npx claudepluginhub ahmidbbc/datadops --plugin datadopsThis skill uses the workspace's default tool permissions.
Comprehensive performance analysis across the full application stack to identify and resolve bottlenecks.
Creates isolated Git worktrees for feature branches with prioritized directory selection, gitignore safety checks, auto project setup for Node/Python/Rust/Go, and baseline verification.
Executes implementation plans in current session by dispatching fresh subagents per independent task, with two-stage reviews: spec compliance then code quality.
Dispatches parallel agents to independently tackle 2+ tasks like separate test failures or subsystems without shared state or dependencies.
Comprehensive performance analysis across the full application stack to identify and resolve bottlenecks.
When invoked directly with /datadops:performance-investigation, use $ARGUMENTS as the performance context.
If needed, ask for the service, endpoint, environment, and time window before starting the analysis.
Baseline Establishment
Bottleneck Identification
Request Flow Mapping
Database Performance
Infrastructure Metrics
Scaling Patterns
Symptoms:
- High database query latency
- Connection pool exhaustion
- Lock contention
Investigation:
- Slow query identification
- Query execution plan analysis
- Database resource utilization
- Connection pool metrics
Recommendations:
- Index optimization
- Query restructuring
- Connection pool tuning
- Read replica utilization
Symptoms:
- High memory utilization
- CPU throttling
- Garbage collection overhead
Investigation:
- Memory allocation patterns
- CPU utilization spikes
- GC frequency and duration
- Thread pool utilization
Recommendations:
- Memory optimization
- Vertical/horizontal scaling
- GC tuning
- Resource allocation adjustments
Symptoms:
- External service timeouts
- Cascading latency issues
- Dependency error propagation
Investigation:
- External API response times
- Circuit breaker effectiveness
- Retry pattern analysis
- Fallback mechanism performance
Recommendations:
- Timeout optimization
- Circuit breaker tuning
- Caching strategies
- Async processing patterns
"The /checkout endpoint is very slow. Investigate what's causing the latency."
Expected Analysis:
"Our user service performance has degraded over the past week. What's the root cause?"
Expected Analysis:
"Database queries are timing out frequently. Analyze the database performance."
Expected Analysis:
1. Algorithm Efficiency
- Time complexity analysis
- Data structure optimization
- Caching implementation
2. Concurrency Patterns
- Thread pool optimization
- Async processing adoption
- Parallelization opportunities
3. Resource Management
- Memory allocation optimization
- Connection reuse strategies
- Resource cleanup patterns
1. Scaling Strategies
- Horizontal vs vertical scaling
- Auto-scaling configuration
- Load distribution optimization
2. Resource Allocation
- CPU/memory right-sizing
- Storage optimization
- Network bandwidth planning
3. Caching Layers
- Application-level caching
- Database query caching
- CDN optimization