「spike」の検索結果

Operate a live AWS workload with AWS DevOps Agent — incident investigation, root-cause analysis, and release-readiness review — for startups with no dedicated DevOps or SRE engineer. Use when a live AWS workload is misbehaving (production is down, 5xx errors, latency spike, timeouts, it broke after the last deploy, an alarm fired, a pasted CloudWatch alarm or stack trace, find the root cause, write a postmortem, no one on call), for pre-merge review (is this PR safe to ship, blast radius), or to set up, pause, stop, or check the cost of AWS DevOps Agent. Detects existing setup first and never re-onboards. Cost, account access, and code egress are each disclosed in one line with safe defaults: cost and access proceed, code egress stays off unless asked. Do not use for: new architecture (architect-for-startups), scaffolding (start-building-for-startups), migrations (gcp-to-aws, azure-to-aws, heroku-to-aws, llm-to-bedrock), or general Activate and credits questions (knowledge-base-for-startups).

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Run a deep root-cause investigation on the AWS DevOps Agent. Use when the user describes an incident, alarm, outage, or unexplained behavior — keywords like "5xx", "503", "OOM", "latency spike", "deployment failure", "rollback", "sev1", "investigate", "root cause", "debug", "alarm fired", "service down". Polls and streams progress, then surfaces recommendations.

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Implement load testing, auto-scaling, and capacity planning for Claude API. Use when running performance benchmarks, planning for traffic spikes, or configuring horizontal scaling for Claude-powered services. Trigger with phrases like "anthropic load test", "claude scaling", "anthropic capacity planning", "scale claude api".

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The SRE team's runbook for triaging production latency and error-rate incidents. Use this whenever investigating an incident, a latency spike, elevated error rates, or when asked "what caused X" about a production service.

日本語の概要は準備中です。原文の説明を表示しています。

AI DevKit · Guide structured debugging before code changes by clarifying expected behavior, reproducing issues, identifying likely root causes, and agreeing on a fix plan with validation steps. Use when users ask to debug bugs, investigate regressions, triage incidents, diagnose failing behavior, handle failing tests, analyze production incidents, investigate error spikes, or run root cause analysis (RCA).

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Optimize existing Azure resources and analyze Reservations or Savings Plans. WHEN: "optimize Azure costs", "reduce cloud spending", "rightsize resources", "find idle resources", "orphaned disk", "deleted VM still charged", "public IP still charging", "reservation utilization", "Savings Plan coverage", "commitment recommendation", "why is pay-as-you-go still charged". DO NOT USE FOR: cost spikes, forecasts, pricing estimates, budgets, or governance.

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Forecast Azure spend and price planned resources or workloads. WHEN: "forecast Azure spending", "project next month cost", "compare forecast to my planning target", "how much will Azure cost", "estimate VM cost", "compare storage tiers", "compare Azure regions", "retail price", "EA rate card", "negotiated pricesheet". DO NOT USE FOR: configured budget health or alerts, bill analysis, cost spikes, rightsizing, or commitments.

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Analyze actual Azure spend, bill changes, and AKS or AI costs. WHEN: "Azure cost breakdown", "most expensive resources", "why did my bill increase", "cost spike", "unexpected charge", "Cosmos DB charges", "Foundry model costs", "AKS cluster cost", "namespace cost", "enable AKS cost analysis", "idle AKS capacity". DO NOT USE FOR: forecasts, pricing estimates, rightsizing, commitments, or budgets.

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Performance, load, and reliability (SLO/SRE) planning for production readiness. Use when defining service level objectives, load characterization, capacity, latency budgets, stress/soak/spike test plans, false-positive baselines, and reliability targets. USE FOR: SLO/SLA definition, load testing plan, performance budget, capacity planning, reliability/SRE backlog, latency targets, error-budget policy. DO NOT USE FOR: executing load tests (use Azure Load Testing tooling), security threat modeling, RAI assessment, privacy/compliance planning, or authoring/restating PRD requirements (cite the PRD's existing NFR/FR ids instead).

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Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.

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Normalizes and transforms RNA-seq count matrices for DE, visualization, clustering, and ML. Covers between-sample (TMM, TMMwsp, RLE/median-of-ratios, upper quartile), within-sample (TPM, FPKM/RPKM), variance-stabilizing (VST, rlog, log-CPM), GC-content correction (cqn, EDASeq), and single-cell (scran deconvolution, scanpy normalize_total). Encodes the composition-bias rationale, the "most genes not DE" assumption and its catastrophic failure modes (MYC amplification, apoptosis, viral host shutoff, prokaryotic stress), the "lengthScaledTPM is not TPM" naming trap, the "TPM is not for DE" rule, the blind=TRUE vs FALSE decision, ERCC spike-in normalization (SBN), and the single-cell zero-inflation breakdown of TMM/RLE. Use when choosing or applying normalization, debugging shifted-MA-plot diagnostics, handling zero-heavy single-cell data, or correcting GC bias.

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Build genome-browser-style multi-track figures with pyGenomeTracks (config-driven), Gviz (R), and IGV batch screenshotting. Covers BigWig coverage tracks, BED/peak overlays, gene-model rendering, Hi-C matrix tracks, BedPE link arcs, spike-in-aware normalization, and the bamCoverage --normalizeUsing trap. Use when producing publication figures of genomic loci with stacked aligned tracks (coverage, peaks, genes, interactions) for ChIP-seq, ATAC-seq, RNA-seq, Hi-C, or generic locus visualization.

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Analyzes pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.

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Identifies super-enhancers from H3K27ac, MED1, or BRD4 ChIP-seq using ROSE, ROSE2, LILY, HOMER -style super, and ENCODE dELS cross-referencing. Handles peak stitching parameters, ranking choices, hockey-stick inflection, marker choice (H3K27ac vs MED1/BRD4), and cross-condition comparison with spike-in normalization. Constructs core regulatory circuitry (Saint-Andre 2016) from SE-encoded TFs. Use when identifying cell-identity / cancer-associated regulatory domains, comparing super-enhancers between conditions, identifying master transcription factor networks, or predicting BET-inhibitor responsiveness.

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Profile, diagnose, and remediate SwiftUI runtime performance using code review, Instruments, and repeatable measurements. Use when a SwiftUI screen renders slowly, scrolling or animations hitch, view bodies update excessively, list identity churns, layout work spikes, or broad Observation dependencies raise CPU cost. Covers evidence-based triage, SwiftUI Instruments lanes, lazy-container guardrails, state lifetime, and before/after verification.

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Auto-scale LLM inference clusters on Kubernetes using KEDA, custom GPU metrics, and horizontal pod autoscaling. Handle traffic spikes, implement queue-based scaling, and optimize cost with spot instances for AI workloads.

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Use this skill when conditioning, loading, preparing, or labeling signal data for analysis or ML training. Covers: cleaning a single signal (fill gaps, remove drift, deoutlier, denoise, resample/align a time base) BEFORE analysis; building a `signalDatastore` pipeline; creating a `labeledSignalSet` for Signal Labeler; deriving labels (filename, folder, in-file, ROI, time-frequency ROI); stratified train/val/test splits; framing long signals; parallel processing; and shaping datastore output for `trainnet`. Triggers include "clean up this signal", "remove drift / detrend", "fill gaps", "remove spikes / outliers", "denoise", "resample to a uniform rate", "align channels", "labels from filenames", "stratified split", "prepare for Signal Labeler", and function names like `fillgaps`, `fillmissing`, `detrend`, `filloutliers`, `smoothdata`, `resample`, `synchronize`, `signalDatastore`, `labeledSignalSet`, `filenames2labels`, `folders2labels`, `splitlabels`, `framesig`, `framelbl`, `createDatastores`.

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Set up systematic review monitoring across e-commerce platforms. Track new reviews, detect negative review spikes, monitor competitor reviews, and automate review response workflows.

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Produce a detailed report on APIError events from Agent Monitor data — counts over time, which sessions and models are affected, and the likely root cause (rate limits, overload/529, or context-window pressure) inferred from each event's summary and data payload. Use when API errors spike or when you need to explain why requests are failing.

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Identify anomalous sessions using Agent Monitor data — cost outliers from the pricing engine, token anomalies (cache miss spikes, compaction baseline surges), unusual event type ratios (PreToolUse/PostToolUse gaps, APIError clusters), behavioral deviations from workflow intelligence (complexity score outliers, error propagation anomalies), and sessions with abnormal metadata (extreme turn_count, high thinking_blocks, zero turn_duration).

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Use when building a 2D game in Unity 6.3 URP: pixel-art or HD sprite import and PPU, sprite atlases, Tilemaps, Rule Tiles and reskins, the 2D Renderer and 2D lights with normal maps, 2D animation, a platformer or top-down controller ("make a platformer controller that feels good", coyote time, jump buffer, one-way platforms, spikes), Pixel Perfect Camera, a follow or room camera, hit stop and screen shake; or when pixel art looks blurry or shimmers, tiles show seams, 2D lights do nothing or cost too much, the jump feels floaty, or the camera loses the player when falling.

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Use when building or fixing game UI in Unity 6.3: main menu, settings screen (resolution, quality, volume, key rebinding), HUD, health bar, minimap frame; choosing uGUI or UI Toolkit or mixing both; 'UI scales wrong on phones', portrait to 4K, Canvas Scaler, anchors, layout groups collapsing, sort order, UXML/USS, data binding, TextMeshPro or UI Toolkit fonts missing glyphs, localized menus, gamepad navigation, UI draw calls, canvas rebuild spikes, UI tests, or capturing UI to check it.

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Run a bounded customer proof of concept to test a consequential uncertainty. Use for a spike or pilot with a question and decision deadline, not a full rollout.

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Performance profiling for Node.js, Python, and Go: CPU flamegraphs, memory leak detection, bundle analysis, query optimization, and k6 load testing. Use when diagnosing slow endpoints, memory growth, large bundles, or traffic spikes.

日本語の概要は準備中です。原文の説明を表示しています。