performance-benchmark
Generate and run ad hoc performance benchmarks to validate code changes. Use this when asked to benchmark, profile, or validate the performance impact of a code change in dotnet/runtime.
Generate and run ad hoc performance benchmarks to validate code changes. Use this when asked to benchmark, profile, or validate the performance impact of a code change in dotnet/runtime.
Analyze behavior under adversarial or extreme API inputs
Hostile full-codebase review by parallel adversarial agents with no design context — finds bugs that domain familiarity masks
Audit for arithmetic and boundary bugs (overflow, off-by-one, drift)
Attempt formal correctness proofs for all public cache methods
Analyze concurrent iteration and view consistency guarantees
Analyze shutdown, close, and GC-of-cache lifecycle correctness
Analyze the cache for linearizability violations across all public methods
Analyze the cache for liveness defects (progress, termination, starvation)
Audit ConcurrentMap and Map contract compliance for asMap() view
Analyze user callbacks for re-entrancy defects (deadlock, corruption)
Multi-layer adversarial code review of a diff or branch using parallel specialized reviewers with triage
Analyze a cache trace file to understand its characteristics and recommend policies
Run cache policy hit rate comparison across multiple cache sizes with charts
Analyze Google Analytics data, review website performance metrics, identify traffic patterns, and suggest data-driven improvements. Use when the user asks about analytics, website metrics, traffic analysis, conversion rates, user behavior, or performance optimization.
Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
Diagnose and fix Agent Zero plugin problems. Covers plugin not appearing, won't enable, API endpoints not responding, frontend store errors, extension point injection, settings resolution, hooks.py issues, and log inspection. Use when a plugin is not working, not loading, crashing, missing from the list, or behaving unexpectedly.
Use when installing or configuring the OpenSearch extension for KubeSphere, which provides distributed search and analytics engine for storing logs, events, auditing, and notification history
Download WebGPU and WGSL specifications for use as a reference
Generate an explorable HTML report of Claude Code session usage (tokens, cache, subagents, skills, expensive prompts) from ~/.claude/projects transcripts.