1.3천건 · 관련도순
Create, update, run, and debug shell-based performance scenarios for the but CLI. It separates repository setup from the operation being measured, uses Hyperfine for timing, and guides focused validation and inspection of test output.
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Turn a broad robotics research direction into ranked ideas with concrete benchmarks and simulation plans. The workflow combines literature review, novelty checks, and external critique while accounting for hardware access, safety, evaluation metrics, and failure cases.
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Build Go services and command-line tools with clear interfaces, explicit error handling, and concurrent tasks using goroutines and channels. Use pprof to investigate performance, then check behavior with benchmarks, structured test cases, fuzzing, and race detection.
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Run concurrent Omnigent conversations with real hosts and runners while replacing the LLM with a zero-latency mock. Read the generated report to explain response-time distributions, throughput, and failures, helping assess Omnigent's own processing overhead.
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Refresh evidence behind paid advertising guidance by checking platform changes, APIs, policies, regulations, and benchmarks. Record sources and contradictions, flag outdated or unsupported claims, and propose updates grounded in current primary sources.
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Evaluate a local Codex skill for metadata, structure, reference links, size, and helper code quality. Get prioritized fixes and explanations, with benchmark setup for deeper analysis and measurement recommendations when observed usage is available.
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Evaluate a local Codex plugin to understand its scores and identify what to fix first. Review stronger and weaker skills, prepare benchmark runs, compare saved results across versions, and produce a readable report.
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Investigate observed slowdowns or increased CPU, memory allocation, and IO costs in a process you control. Use controlled timing and runtime-specific profiling tools to test a cause, then check performance and behavior after a fix.
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Optimize .NET loops with SIMD, which processes multiple values in one operation, or review existing SIMD code. Check behavioral equivalence, memory safety, platform fallbacks, and benchmarks to establish whether the optimization is correct and worthwhile.
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Measure and optimize claude-flow v3 startup, search, attention processing, and memory use. Compare benchmarks against performance targets, track changes over time, and identify regressions to guide further optimization.
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Improve vllm-mlx performance through research, small experiments, and benchmarks on real models. The skill records results, keeps changes that improve measured performance, and carries successful work through implementation, testing, review, and pull request creation.
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Evaluate answer quality in NVIDIA RAG Blueprint using local documents and question data. The skill guides dataset preparation, RAGAS scoring, configuration comparisons, and error diagnosis for RAG systems that retrieve documents to support generated answers.
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Benchmark Jetson LLM/VLM serving performance across vLLM, llama.cpp, and Ollama with structured JSON output.
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Measure how quickly a host or DPU CPU installs and deletes DOCA Flow rules. Choose a JSON workload and backend, run repeated benchmarks, and record hardware, software, and execution settings so results can be assessed.
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Integrate a supported object detection model from HuggingFace or NVIDIA NGC into NVIDIA DeepStream. Automates model acquisition and conversion, TensorRT engine building, detection output parsing, multi-stream performance benchmarks, and reports with charts in Markdown, HTML, and PDF.
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Golang performance optimization patterns and methodology - if X bottleneck, then apply Y. Covers allocation reduction, CPU efficiency, memory layout, GC tuning, pooling, caching, and hot-path optimization. Use when profiling or benchmarks have identified a bottleneck and you need the right optimization pattern to fix it. Also use when performing performance code review to suggest improvements or benchmarks that could help identify quick performance gains. Not for measurement methodology (→ See `samber/cc-skills-golang@golang-benchmark` skill) or debugging workflow (→ See `samber/cc-skills-golang@golang-troubleshooting` skill).
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VLM indoor safety hazard detection benchmark inspired by HomeSafeBench (arXiv 2509.23690)
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Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for lab data management automation.
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Use when running performance benchmarks, establishing baselines, or validating regressions with sequential runs. Enforces 60s minimum runs (30s only for binary search) and no parallel benchmarks.
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Operational workflow for hard integer-programming optimization tasks: selecting an installed solver, preserving solver/incumbent certificates, extracting feasible schedules, recomputing metrics from final outputs, and writing consistent reports. Use when a task requires a MIP, solver status, objective value, bound, gap, formulation write-up, or benchmark output files.
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Use for parsing structured unit commitment input data from JSON, CSV, benchmark cases, spreadsheets, databases, or nested tables; finding fields for time periods, resources, load, reserve, generator limits, initial conditions, startup data, renewable availability, and production costs without assuming one source-specific schema.
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An overview of the core data API of ObsPy, a Python framework for processing seismological data. It is useful for parsing common seismological file formats, or manipulating custom data into standard objects for downstream use cases such as ObsPy's signal processing routines or SeisBench's modeling API.
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Run all reference benchmarks under benchmarks/eval/ and refresh the reference-table cells in benchmark_*.py. Auto-detects host hardware (H200/H100/H800/...). For each row's matching hw entry: replace cells in place. For new hardware or a new workload tag: append a new row to the section's table. Local-Pipeline-Result tables are skipped. One slash command runs everything end-to-end and auto-commits.
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**WORKFLOW SKILL** - Evaluate AI agent skills using structured benchmarks with YAML specs, fixture isolation, and pluggable validators. USE FOR: run waza, waza help, run eval, run benchmark, evaluate skill, test agent, generate eval suite, init eval, compare results, score agent, agent evaluation, skill testing, cross-model comparison. DO NOT USE FOR: improving skill frontmatter (use waza dev), creating new skills from scratch (use skill-creator), token counting or budget checks (use waza tokens). INVOKES: Copilot SDK executor, mock engine, code/regex validators. FOR SINGLE OPERATIONS: use waza run directly for a single benchmark.
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