performance

name: performance description: Identify and improve performance and scalability.

Performance Skill

Use this skill to evaluate and enhance the performance and scalability of the codebase. Follow these guidelines:

Measurement

• Profile first – Use profiling tools to measure CPU, memory and I/O usage. Benchmark with realistic workloads to identify hot spots.
• Establish baselines – Record current performance metrics (latency, throughput, resource usage) before optimising. Compare against targets.

Complexity & Algorithms

• Assess the algorithmic complexity of functions. Replace inefficient algorithms (e.g. O(n²) loops) with more scalable alternatives.
• Choose appropriate data structures for the task (e.g. hash maps for lookups, queues for FIFO processing).

Concurrency & Asynchrony

• Utilise concurrency primitives (threads, async/await, goroutines) where they improve throughput. Avoid blocking calls on the main thread.
• Ensure shared state is protected with locks or other synchronisation mechanisms. Prevent race conditions and deadlocks.

Resource Management

• Manage memory, file handles and network connections carefully. Allocate and release resources promptly (e.g. use context managers in Python or using statements in C#).
• Avoid unnecessary memory copies and large intermediate allocations.

Caching & Memoization

• Cache expensive computations or remote calls where appropriate. Use proper invalidation strategies (TTL, LRU) to avoid stale data.
• Prefer immutable data where possible to simplify caching and concurrency.

I/O Efficiency

• Batch operations to reduce overhead (e.g. bulk database writes, batch API requests). Use streaming for large files or payloads.
• Minimise network round trips. Use connection pooling for database and HTTP clients.

Scalability

• Design services to scale horizontally: keep them stateless where possible and externalise state to databases or caches.
• Implement back‑pressure mechanisms (queues, circuit breakers, rate limiting) to handle overload gracefully.

Observability

• Collect metrics, traces and logs to monitor performance in production. Dashboards and alerts help detect regressions early.
• Add instrumentation at key points (e.g. request start/end, database queries) to measure latency and throughput.

When suggesting optimisations, balance performance gains against code readability and maintainability. Document any trade‑offs or additional complexity introduced by optimisations.