'cpu' 검색 결과

Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps, and to provide guidance for user-run Instruments profiling when code review alone is insufficient.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Plan, diagnose, and verify performance budgets, latency targets, load tests, capacity estimates, bottleneck analysis, caching strategy, query efficiency, queue throughput, and regression gates. Use when a feature may be slow, a system must scale, a performance regression is suspected, or release readiness depends on throughput, cost, memory, CPU, or response time.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Diagnose slow or freezing VS Code-compatible editors with evidence-first, zero-hardcoded-assumption workflow. Use when the user reports editor lag, typing delay, UI freezes, extension host stalls, file watcher noise, high editor CPU/RSS, uses VS Code/Cursor as a file browser over a large folder, or wants a safe editor performance audit.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

系统性能诊断。当用户说电脑卡、系统慢、查看进程、CPU占用高、内存不够等性能问题时使用

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Think and work like an expert Ichthyologist. Use when a task calls for Ichthyologist judgment. Reasons from meristic fin formulae, sagittal otolith annuli/daily increments, larval flexion staging, and ICZN type discipline through Eschmeyer's Catalog, FishBase/WoRMS, MiFish/12S eDNA with blank controls, FSA/TropFishR/SS3 stock assessment, and Darwin Core museum metadata while treating CPUE catchability, unvalidated otolith ages, larval pigmentation loss, and eDNA false positives as first-class failure modes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Think and work like an expert Fisheries Scientist. Use when a task calls for Fisheries Scientist judgment. Reasons from recruitment, growth, and natural and fishing mortality through state-space assessment models (SS3, SAM, JABBA), CPUE/GLM standardization, and reference points like F_MSY and B_lim under ICES and Magnuson-Stevens frameworks, while treating hyperstability, retrospective bias (Mohn's rho), unaccounted discard mortality, and misspecified M or selectivity as first-class failure modes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Runs a long swarm of hundreds to thousands of cheap headless coding workers that drive a large failing-test backlog to green. Failures are clustered by signature, each worker fixes one cluster in its own git worktree, a serial no-regression merge gate lands the fixes, the loop resumes across restarts for hours, and it federates across sibling sessions over git branches when one container's CPU is not enough. Use for "fix these thousands of failing tests with a Haiku swarm", "drive the test suite to green with hundreds of agents", "build this compiler or interpreter against a conformance suite", "launch, relaunch or check in on the swarm loop", "the swarm is stuck, idle or rejecting everything", "parallel agents fixing tests keep hitting merge conflicts and regressions", "add sibling containers to the swarm", or "compare models on the tail the swarm could not fix". Ships the harness, a per-project TOML config, a fake-worker selftest, check-in signals and the 2026-10-12 haiku-cc failure catalogue.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Text-prompted image zone detection using TIPSv2 B/14 on CPU. Produces `focus_targets` / `focus_edges` bbox lists from natural-language labels, ready to feed into `svg-portrait-mode`. Use when you want automatic foreground/background separation from prompts like "dog face" + "wooden floor" instead of hand-annotating bboxes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Zero-shot univariate time series forecasting using the Reverso foundation model (NumPy/Numba CPU-only inference). Activate when users provide time series data and request forecasts, predictions, or extrapolations. Supports Reverso Small (550K params). Triggers on "forecast", "predict", "time series", "Reverso", or when tabular data with a temporal dimension needs future-value estimation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when writing or changing PyTorch code — model, Dataset/DataLoader, training and evaluation loops, determinism, mixed precision, torch.compile, checkpoints with safetensors, Hugging Face models, ONNX export, and tests that run on CPU

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Performance profiling and optimization for web apps — Core Web Vitals (LCP, INP, CLS), Lighthouse audits, bundle analysis, backend profiling (CPU, memory, DB queries), N+1 detection, caching strategies (Redis, CDN, HTTP), and performance budgets. Use when user asks to improve performance, run Lighthouse audit, profile a Node.js app, optimize Core Web Vitals, reduce bundle size, or investigate slow response times. Do NOT use for database schema optimization (use db-sculptor), Docker image optimization (use docker), or CDN configuration.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Diagnoses and improves measured latency, throughput, memory, CPU, bundle, rendering, query, or resource performance by defining a target, profiling a representative workload, locating the bottleneck, making one change at a time, and comparing before/after evidence. Use for performance work or regressions. Not for guessing optimizations without a reproducible workload or for generic refactoring.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Foundations of digital signal processing — sampling, aliasing, the Nyquist criterion, quantization, convolution, the discrete Fourier transform, FFT, FIR and IIR filter design, windowing, spectral leakage, and practical issues in fixed-point versus floating-point implementation. Use when designing filters, analyzing spectra, choosing sample rates, reasoning about aliasing, or implementing DSP algorithms on a microcontroller, DSP chip, or host CPU.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Understanding how digital systems actually work -- hardware, operating systems, networks, and information representation -- at a depth sufficient to reason about them rather than just use them. Covers CPU/RAM/storage/GPU roles, what an OS does, how packets travel the internet, and how computers represent numbers, text, images, and sound as binary. Use when a learner needs to build a mental model of computing rather than memorize menu paths.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

OpenStack capacity planning operations skill for resource planning, quota management, and scaling decisions. Covers compute capacity (allocation ratios, vCPU/RAM utilization, host aggregates, availability zones), storage capacity (Cinder pool sizing, thin provisioning, Glance storage, Swift rings), network capacity (floating IP pools, port quotas, subnet sizing), project quota management (templates for small/medium/large profiles), flavor sizing strategy, utilization analysis with placement service, growth forecasting from historical metrics, and right-sizing recommendations for underutilized resources.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

OpenStack Nova compute service skill for deploying, configuring, operating, and troubleshooting cloud compute infrastructure. Covers instance lifecycle management (create/stop/start/reboot/resize/migrate/evacuate), compute scheduling with filters and weights, flavor management, live migration procedures, hypervisor management (KVM/QEMU via libvirt), placement service for resource tracking, CPU pinning, NUMA topology, huge pages, cell mapping, VNC/SPICE console access, and capacity planning. Use when deploying compute via Kolla-Ansible, managing instances, debugging scheduler failures, or planning compute capacity.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill when controlling the AltirraSDL Atari emulator programmatically — driving the simulator from a script, capturing screenshots from a running Atari, injecting joystick/keyboard input, frame-stepping for deterministic testing, reading or writing CPU/memory/chip state, setting breakpoints or watchpoints, profiling 6502 code, loading symbols, or building automated tests against an Atari binary. Triggers on phrases like "AltirraBridge", "AltirraSDL --bridge", "AltirraBridgeServer", "Altirra Python SDK", "headless Atari emulator", "screenshot the Atari", "automate AltirraSDL", "drive the emulator from Python", "Atari debugger over a socket", "altirra_bridge package", as well as on tasks involving the bridge protocol, the Python `altirra_bridge` package, or the C `altirra_bridge.h` SDK.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

将文案与用户自己的参考录音合成为配音、SRT 和可交接时间轴。默认使用 MiniMax 与用户自己的音色；可显式选择免费本地 Qwen3-TTS（Apple Silicon 用 MLX）或 Nano CPU。适用于口播配音、声音复刻，以及为网页视频演示准备最终音频和字幕。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

调用 Yosys 0.35 对 RTL 进行逻辑综合并技术映射到 ASAP7 标准单元，产出门级网表 + QoR 报告。支持并行综合（基于空闲CPU数量）。Workflow: (1)生成综合脚本;(2)并行执行综合;(3)LLM检查结果并迭代优化。触发场景：(1) bba-guru-synthesis 已完成 SDC 与 CDC 检查、需要生成 netlist；(2) PD 反馈 area/path 超标需要重综合；(3) 显式 /bb-invoke-yosys。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Systems-level performance engineer: identifies hotspots, provides optimized code with resource justifications.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Forecast infrastructure and application metrics using Prophet or statsmodels for capacity planning, cost optimization, and proactive scaling. Visualize predictions in Grafana and set up alerts for projected resource exhaustion. Use when forecasting infrastructure capacity needs for CPU, memory, or disk, planning hardware procurement for next quarter, predicting cost trends to optimize cloud spending, or setting up proactive scaling policies based on predicted load.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when configuring DMA channels, circular buffer mode, double buffering, memory-to-peripheral transfers, or DMA IRQ completion on bare-metal MCUs. Not for cache theory: use cpu-cache-opt.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when diagnosing cache misses with perf, fixing false sharing, choosing AoS or SoA layout, or adding software prefetch. Not for cache theory: use memory-hierarchy-and-caches.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when explaining pipeline stages, data or control hazards, forwarding, stalls, or superscalar basics behind a counter reading. Not for mispredict cost: use branch-prediction-and-speculation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.