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.

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.

Scores competitors across nine weighted dimensions with explicit 1–5 rubrics and a tension plot, producing comparable profile cards for competitive analysis.

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.

Cross-model benchmark for gstack skills. (gstack)

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.

Diagnose MSBuild build performance bottlenecks using binary log analysis, covering timeline analysis, performance summary interpretation, and seven common bottleneck categories.

Diagnoses and resolves slow Qdrant search performance issues including high latency, low throughput, and slow filtered searches.

Systematically diagnose and resolve performance issues in Power BI models, reports, and queries using a structured troubleshooting methodology.

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.

Ranks any list of impediments and their countermeasures using a value-stream scoring model (ROI, Cost to Implement, Ease of Deployment, Risk Factor) and a fixed prioritization formula.

Diagnoses and improves Qdrant search relevance by isolating embedding model, configuration, or query strategy issues.

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

Run CIS Kubernetes Benchmark checks and remediate findings with kube-bench.

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".

Use to compare CX performance to a published or vendor benchmark without fooling yourself — scope mismatch, survivor bias, and definition mismatch usually make external benchmarks incomparable, and internal baselines often beat them. Trigger for "how do we compare to industry", "is our CSAT good", benchmark slide for the board, vendor benchmark report, "are we above average", outsourcing RFP benchmarks, or when someone cites a round-number industry standard.

Benchmark, load test, capacity plan, and cache with k6, JMeter, Locust, and pgbench. Use when the user says "slow", "performance", "load test", "stress test", "how many users can it handle", "capacity", "cache", "benchmark", "k6".

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)

`analysis-agent`/`task-agent`/`review-agent`: use when latency, throughput, bundle, memory, CPU, query, rendering, or resource cost needs a budget; skip without performance risk.

Use when the app is slow, pages take too long to load, API responses exceed 200ms, memory usage is high, or renders are excessive. For profiling, identifying bottlenecks, optimizing React renders, reducing bundle size, improving database query speed, and measuring before/after improvements. Activate when user says "it's slow", "optimize this", "reduce load time", or "too many re-renders".

Use to audit how payment disputes, chargebacks and unrecognised-transaction claims are handled against scheme deadlines and evidence standards. Trigger for "audit our dispute handling", "chargeback quality review", "are we meeting scheme deadlines", unrecognised transaction claims, provisional credit decisions, or a customer complaining their dispute was rejected unfairly.

Use in TURBO mode when Codex should compare top current solutions before choosing architecture, UI components, 3D stack, mobile stack, SEO/PPC strategy, or programming patterns.

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