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Found 193 Skills
Use these skills when you need to troubleshoot performance bottlenecks, analyze query execution plans, identify resource-heavy processes, and monitor system-level PromQL metrics.
Analyze host/CPU overhead in TensorRT-LLM inference from nsys traces. Detect whether host overhead is the bottleneck using GPU idle ratio, host prep exposed ratio, and per-phase evidence. For regressions, isolate forward steps via allreduce/NVTX patterns, compare host operation breakdowns across versions, and identify scheduling or request-management overhead. Supports optional inter-kernel gap, eager-vs-graph, pattern mapping, and multi-rank straggler drill-down. Use standalone or within perf-analysis. Triggers: host overhead, inter-step gap, scheduling overhead, forward step isolation, nsys iteration analysis, NVTX breakdown, request management overhead, GPU idle, host bottleneck, host prep exposed, inter-kernel gap, bubble analysis, graph coverage, eager kernel, rank imbalance, straggler detection.
Analyze code for performance issues and suggest optimizations. Use when users ask to "optimize this code", "find performance issues", "improve performance", "check for memory leaks", "review code efficiency", or want to identify bottlenecks, algorithmic improvements, caching opportunities, or concurrency problems.
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".
JVM performance profiling with Java Flight Recorder (JFR), jcmd, and GC analysis. Use for identifying bottlenecks and memory issues. USE WHEN: user mentions "Java profiling", "JFR", "JVM performance", asks about "Java Flight Recorder", "jcmd", "heap dump", "GC tuning", "thread dump", "Java memory leak" DO NOT USE FOR: Node.js/Python profiling - use respective skills instead
Build and maintain an LLM-curated personal knowledge base — the "LLM Wiki" pattern from Andrej Karpathy's April 2026 gist. Use this skill whenever the user wants to ingest a source (paper, article, transcript, PDF, notes) into a persistent compounding knowledge base, ask a question against accumulated notes, lint or audit such a base, or initialize a new one. Trigger on phrases like "add this to my wiki", "ingest this paper", "compile this into the knowledge base", "what does my wiki say about X", "lint the wiki", "build a knowledge base from these documents", "research notes", "second brain", "personal knowledge base", or any reference to LLM Wiki / OmegaWiki. Trigger even when the user does not say "wiki" — if they are accumulating sources over time and want them organized, this applies. The skill scales — sharded indexes, atomic pages, YAML frontmatter, and a bundled search script keep the wiki from becoming a context bottleneck at hundreds or thousands of pages.
Creates an API Gateway stage with CloudWatch logging, X-Ray tracing, throttling, WAF integration, and IAM roles following AWS best practices. Use when deploying a REST API to different environments such as dev, test, or production.
Connects an existing AWS Lambda function to Amazon API Gateway by creating a REST or HTTP API with resource/method setup, Lambda proxy integration, permissions, and deployment. Always use this skill when connecting Lambda to API Gateway — it handles CORS, throttling, access logging, and production security hardening that are easy to miss.
Use this skill when the user asks for a review, audit, evaluation or analysis of a codebase, to identify bugs, security vulnerabilities, performance bottlenecks, or code quality concerns.
Debug TensorFlow and Keras issues systematically. This skill helps diagnose and resolve machine learning problems including tensor shape mismatches, GPU/CUDA detection failures, out-of-memory errors, NaN/Inf values in loss functions, vanishing/exploding gradients, SavedModel loading errors, and data pipeline bottlenecks. Provides tf.debugging assertions, TensorBoard profiling, eager execution debugging, and version compatibility guidance.
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.
Agent skill for performance-monitor - invoke with $agent-performance-monitor