Does it address blocking code in async contexts?

Absolutely. It helps identify blocking operations that stall the runtime and provides solutions like using spawn_blocking for CPU-bound tasks or non-blocking alternatives.

Can it help detect deadlocks in async code?

Yes, the skill includes techniques for consistent lock ordering, using try_lock with timeouts to detect hangs, and migrating from nested locks to message passing.

How do I use this skill to find memory leaks?

It provides patterns for identifying task leaks (tasks that never exit), finding Arc reference cycles using weak references, and monitoring system memory usage within the app.

How does this skill help with tokio-console?

It provides standardized implementation patterns for console-subscriber and guidance on interpreting real-time metrics like poll duration, task spawn rates, and waker operations.

Tokio Troubleshooting & Observability

Name: Tokio Troubleshooting & Observability
Author: geoffjay

bygeoffjay

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Analytics & Monitoring

Diagnoses and resolves performance bottlenecks, deadlocks, and memory leaks in Rust applications using the Tokio async runtime.

About

This skill empowers developers to debug complex asynchronous Rust applications built on the Tokio runtime. It provides comprehensive guidance on utilizing tokio-console for real-time monitoring, identifying common deadlock patterns, detecting task leaks, and profiling performance using tracing instrumentation. Whether you are dealing with hung tasks, high CPU usage from blocking code, or unexpected memory growth, this skill provides the specific implementation patterns and best practices needed to maintain high-performance, production-grade async systems.

Key Features

Identification and mitigation of task and memory leaks
Performance profiling using structured tracing and spans
2 GitHub stars
Advanced async testing strategies including time mocking
Deadlock detection and consistent lock ordering patterns
Real-time runtime monitoring with tokio-console integration

Use Cases

Identifying CPU-intensive blocking operations that stall the async executor
Debugging production hangs and resource contention in high-concurrency microservices
Optimizing Rust backends by visualizing task poll durations and waker efficiency

About

Key Features

Identification and mitigation of task and memory leaks
Performance profiling using structured tracing and spans
2 GitHub stars
Advanced async testing strategies including time mocking
Deadlock detection and consistent lock ordering patterns
Real-time runtime monitoring with tokio-console integration

Use Cases

Identifying CPU-intensive blocking operations that stall the async executor
Debugging production hangs and resource contention in high-concurrency microservices
Optimizing Rust backends by visualizing task poll durations and waker efficiency