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Found 164 Skills
Debug Next.js issues systematically. Use when encountering SSR errors, hydration mismatches like "Text content did not match", routing issues with App Router or Pages Router, build failures, dynamic import problems, API route errors, middleware issues, caching and revalidation problems, or performance bottlenecks. Covers both Pages Router and App Router architectures.
When validating system performance under load, identifying bottlenecks through profiling, or optimizing application responsiveness. Covers load testing (k6, Locust), profiling (CPU, memory, I/O), and optimization strategies (caching, query optimization, Core Web Vitals). Use for capacity planning, regression detection, and establishing performance SLOs.
Identify CPU and memory bottlenecks in Python code using cProfile or memory_profiler. Use to optimize mission-critical Python services.
Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.
Implement Mistral AI rate limiting, backoff, and request management. Use when handling rate limit errors, implementing retry logic, or optimizing API request throughput for Mistral AI. Trigger with phrases like "mistral rate limit", "mistral throttling", "mistral 429", "mistral retry", "mistral backoff".
Implement Ideogram rate limiting, backoff, and idempotency patterns. Use when handling rate limit errors, implementing retry logic, or optimizing API request throughput for Ideogram. Trigger with phrases like "ideogram rate limit", "ideogram throttling", "ideogram 429", "ideogram retry", "ideogram backoff".
Handle Evernote API rate limits effectively. Use when implementing rate limit handling, optimizing API usage, or troubleshooting rate limit errors. Trigger with phrases like "evernote rate limit", "evernote throttling", "api quota evernote", "rate limit exceeded".
Optimize Linux system performance. Configure kernel parameters, analyze bottlenecks, and tune resources. Use when improving system performance.
Optimizes Python library performance through profiling (cProfile, PyInstrument), memory analysis (memray, tracemalloc), benchmarking (pytest-benchmark), and optimization strategies. Use when analyzing performance bottlenecks, finding memory leaks, or setting up performance regression testing.
Optimize application performance and scalability. Use when investigating slow applications, scaling bottlenecks, or improving response times. Use for profiling, caching, database optimization, frontend performance, and backend tuning.
A framework for classifying product decisions based on impact and reversibility. Use this when you feel like a bottleneck for your team, when you have a massive backlog of choices to make, or when you need to justify spending weeks of research on a single high-stakes problem.
Python performance profiling with cProfile, tracemalloc, and line_profiler. Use for identifying bottlenecks and memory issues. USE WHEN: user mentions "Python profiling", "cProfile", "memory profiling", asks about "Python performance", "tracemalloc", "line_profiler", "py-spy", "Python optimization", "Python memory leak" DO NOT USE FOR: Java/Node.js profiling - use respective skills instead