Results for “benchmark-problems”

Measures performance baselines, detects regressions before and after PRs, and compares stack alternatives.

Measure performance baselines, detect regressions before and after PRs, and compare stack alternatives using browser, API, and build benchmarks.

Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

Use when evaluating, measuring, or comparing the performance of systems, functions, or services. This skill provides a framework for establishing baselines, measuring performance, and validating that changes meet performance requirements.

Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.

Performance baseline measurement and regression detection. Use when measuring perf before/after a PR, setting up baselines, investigating "feels slow" reports, validating launch performance targets, or comparing your stack against alternatives.

Design and execute performance benchmarks for models and systems. Use when measuring throughput, latency, or comparing system variants.

Create, run, configure, and review BenchmarkDotNet microbenchmarks for .NET code, covering project setup, comparison strategies, and cost-aware execution.

Analyzes Perfetto traces to find the root cause of latency, memory, or jank issues in Android apps.

Diagnoses Qdrant production issues using metrics and observability tools, covering optimizer problems, memory spikes, and slow queries.

Measures application performance first, identifies bottlenecks, and applies targeted fixes for frontend and backend systems.

Performance regression detection using the browse daemon. Establishes baselines for page load times, Core Web Vitals, and resource sizes. Compares before/after on every PR. Tracks performance trends over time. Use when: "performance", "benchmark", "page speed", "lighthouse", "web vitals", "bundle size", "load time". (gstack) Voice triggers (speech-to-text aliases): "speed test", "check performance".

Diagnose frame-rate bottlenecks (CPU vs GPU bound FIRST), control Unreal Insights traces, sample live frame times, and annotate performance captures. Use when FPS is low/bad, the game is slow, or you need to find what is limiting the frame rate.

Strengthen interfaces against edge cases, errors, internationalization issues, and real-world usage scenarios that break idealized designs.

Performance regression detection using the browse daemon. (gstack)

Scan codebases for technical debt, score severity, track trends, and generate prioritized remediation plans.

동시에 굴리던 여러 워크트리를 한 번에 통합합니다. 각 워크트리와 연결된 이슈를 확인하고 증거로 실제 해결 여부를 판정한 뒤, merge 를 시도하기 전에 충돌을 확정하고 계획한 순서대로 누적 검증해 순서 때문에 깨지는 경우까지 잡아냅니다. 충돌은 작업 브랜치 쪽에서 양쪽 의도를 보존하는 방향으로 해소해 승인받고, 비판 서브에이전트로 모호성을 걸러낸 다음 PR 을 merge 하고 통합 테스트로 재검증하고 이슈를 닫습니다. `$issue-merge`, "워크트리 전부 merge", "이슈들 통합", "PR 충돌 해결하고 합쳐줘" 요청과 issue-end 에서 merge 를 선택했을 때 사용합니다.

Systematically profile Node.js, Python, and Go applications to identify CPU, memory, and I/O bottlenecks, generate flamegraphs, analyze bundle sizes, optimize database queries, and run load tests with k6 and Artillery.

Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system

Post-implementation quality gate that reviews changes, fixes small issues (scout rule), creates tasks for medium issues, and generates analysis for large issues.

Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.

Documents benchmark results for a multi-agent system and provides commands to run benchmark suites.

Diagnoses and fixes UI performance across loading speed, rendering, animations, images, and bundle size. Use when the user mentions slow, laggy, janky, performance, bundle size, load time, or wants a faster, smoother experience.

Improves measured performance while preserving correctness, reliability, and maintainability.

Use for identifying and resolving performance bottlenecks.

Diagnoses and fixes UI performance across loading speed, rendering, animations, images, and bundle size. Use when the user mentions slow, laggy, janky, performance, bundle size, load time, or wants a faster, smoother experience.

DataPerf: Benchmarks for Data-Centric AI Development

Strengthen interfaces against edge cases, errors, internationalization issues, and real-world usage scenarios that break idealized designs.

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

Solve single- and multi-objective optimization problems with NSGA-II/III, MOEA/D, and other evolutionary algorithms, including Pareto front analysis, constraint handling, and benchmarking on standard test problems.

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.