Loading...
Loading...
Found 147 Skills
Identify, validate, and ship production-safe Node.js optimizations with execution time as the primary objective. Use when users ask to reduce latency (p50/p95/p99), improve throughput, and then reduce CPU/memory/event-loop lag/FD pressure or retry amplification, using one-PR-per-improvement workflows with benchmarks.
Optimizes algorithms via autoresearch loop: benchmark, research, hypothesize, keep/discard
Research Xiaohongshu accounts from validated recent-post surfaces, then aggregate account-level content signals without pretending follower or bio metrics are available when the validated profile actor is empty.
Research TikTok Creative Center or ad-library style datasets for winning ad patterns, regions, objectives, hook language, and creative signals without mixing paid ads with organic creator discovery.
You are **Performance Benchmarker**, an expert performance testing and optimization specialist who measures, analyzes, and improves system performance across all applications and infrastructure. Yo...
Research best-in-class products using Browser MCP and WebSearch
Designs structured benchmarks for comparing algorithms, models, or implementations. Selects appropriate metrics (latency, throughput, memory, accuracy), designs representative test cases, captures hardware/software context, produces comparison tables with tradeoff analysis, and includes reproduction instructions. Triggers on: "benchmark", "compare performance", "which is faster", "latency comparison", "memory comparison", "run benchmark", "design benchmark", "compare implementations", "evaluate algorithms", "performance comparison", "throughput test", "speed test". Use this skill when comparing two or more implementations, algorithms, or models.
Benchmark CodeGraph retrieval quality on a real codebase by comparing agent behavior with vs without CodeGraph. Use when the user runs /agent-eval or asks to test, benchmark, audit, or validate a codegraph version (the local dev build or a published npm version) against a language's repo.
Audit how a brand appears in AI-powered search (ChatGPT, Perplexity, Claude, Gemini). Use when user mentions "AI search," "how do I show up in ChatGPT," "AI discoverability," "AEO," "LLM visibility," or wants to understand their brand's AI presence.
Run an autonomous Humanize-governed vLLM SOTA performance loop for one LLM model: first perform the fixed fair vLLM/SGLang/TensorRT-LLM deployment search and benchmark, then start one RLCR loop that repeatedly decides the gap, profiles the current bottleneck, runs layer/kernel pipeline analysis, patches vLLM code, optionally uses ncu-report-skill for kernel evidence, and revalidates until vLLM matches or beats the best observed framework under the same workload and SLA.
Pull live marketing metrics for a performance snapshot: KPIs vs targets, trend comparison, and cross-platform overview. Use when checking current marketing performance, monitoring KPI health, comparing to benchmarks, or getting a quick status update across analytics platforms.
Write Foundry-based tests and scripts. Trigger phrases - foundry testing, write test, fuzz test, fork test, invariant test, deploy script, gas benchmark, coverage, or when working in tests/ or scripts/ directories.