「traffic」の検索結果

Traffic splitting, health checks, automated rollback, progressive delivery, and canary analysis for safe deployments.

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。

Implement production-ready service mesh deployments with Istio, Linkerd, or Cilium. Configure mTLS, authorization policies, traffic routing, and progressive delivery patterns for secure, observable microservices. Use when setting up service-to-service communication, implementing zero-trust security, or enabling canary deployments.

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。

When distributing traffic across multiple servers or regions, use this skill to select and configure the appropriate load balancing solution (L4/L7, cloud-managed, self-managed, or Kubernetes ingress) with proper health checks and session management.

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。

Build and govern a 50-200 example domain-specific held-out benchmark sampled from real traffic. Distinct from public benchmarks (MMLU/HumanEval/GSM8K via lm-evaluation-harness) which measure GENERAL capability. Only a held-out domain set predicts whether THIS system works on YOUR data. Collect real examples, label, hold out (never train/prompt on it), size 50-200, version it, refresh on drift.

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。

Enhancement-overlay SOP for adding sparse (BM25 / keyword) retrieval alongside dense (embedding) retrieval. Activate when a calling agent is building, reviewing, or debugging a retrieval pipeline whose corpus contains exact-match tokens — identifiers, error codes, SKUs, API/function names, proper nouns, citations, rare jargon — that pure dense embedding silently misses. Encodes the single decision rule (**hybrid is traffic-driven, not theoretical: add sparse only when the query share that depends on exact tokens is non-trivial**), the wiring of QueryFusionRetriever-style fusion (RRF vs alpha-weighted), and per-query-type alpha tuning. Frame the work as recovering lexical identity that dense pooling destroys, not as "add keyword search for completeness". Cross-links [[llamaindex]].

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。

Model-guided autonomous root-cause analysis over a UModel object-graph semantic layer. Use with `umodel-query` when diagnosing service degradation, SLO breach, latency/error spikes, incidents, outages, or questions like why a service is slow, degraded, erroring, or timing out. The skill guides evidence collection across object graph, metrics, logs, topology, runbooks, deployments, config changes, business traffic, and runtime capacity; it is especially useful for ruling out red herrings such as recent but unrelated deployments. Triggers: root cause, RCA, incident, SLO breach, outage, postmortem, latency spike, error spike, timeout, 根因分析, 故障排查, 告警定位, 为什么慢, SLO 击穿.

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。

Browser automation, debugging, and performance analysis using Puppeteer CLI scripts. Use for automating browsers, taking screenshots, analyzing performance, monitoring network traffic, web scraping, form automation, and JavaScript debugging.

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。

Use when reverse-engineering or detecting malware — static triage + capa/YARA-X, emulation/DBI/.NET unpacking, dynamic/fileless/Volatility 3 memory analysis, C2 config extraction (Cobalt Strike/CAPE), C2 traffic detection (JA4+, beaconing)

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。

Use when about to take any outward or offensive action (request, payload, persistence, lateral movement, exfil, or feeding captured traffic to the model) — to decide detection footprint, cleanup, and secret redaction first

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。

Process-based discrete-event simulation. Model queues, shared resources, timed events: manufacturing, service ops, network traffic, logistics. Processes are Python generators yielding events. Resources: capacity-limited (Resource/Priority/Preemptive), bulk (Container), objects (Store, FilterStore). For continuous use SciPy ODEs; for agent-based use Mesa.

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。

Optimize construction site logistics including material delivery scheduling, crane positioning, storage area allocation, and traffic flow using operations research and simulation.

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。

Databricks Model Serving endpoint lifecycle and ops. Use when asked to: CRUD serving endpoints (CLI or MLflow Deployments client); configure traffic routing for A/B / canary deploys and zero-downtime version swaps; retrieve OpenAPI schemas; inspect logs, metrics, or permissions; manage legacy AI Gateway rate limits (not Unity Gateway); discover Foundation Model API endpoints at runtime; integrate endpoints into Databricks Apps; or stream from off-platform clients (Vercel AI SDK v6, standalone Node.js). NOT for: Unity Gateway CRUD and management (databricks-unity-gateway), training, MLflow autologging, UC registration, custom PyFunc/ResponsesAgent authoring (databricks-ml-training); Knowledge Assistants/Supervisor Agents (databricks-agent-bricks); MLflow evaluation (databricks-mlflow-evaluation).

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。

Applies data-grid architecture for high-traffic stateful workloads. Use when a single database cannot scale and in-memory partitioning is needed.

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。

Use AlpaSim repository guidance for autonomous-driving simulation setup, Hydra wizard runs, runtime services, ego drivers and plugins, ASL evaluation, controller/physics/traffic components, gRPC contracts, and operational troubleshooting.

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。

Turn a curious developer into an activated one: the docs, the quickstart, and the first-run experience that gets them to their first real win fast, and back again. Use when people sign up or star the repo but never get it working, come once and never return, or you're about to pour traffic into a first-run that leaks.

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。

Use service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.

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。

Content web architecture framework. Use when diagnosing offer doc usage, content-to-conversion pathways, or why someone isn't getting sales despite traffic.

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。

Fix false-positive Cloud Run deploys where `gcloud builds submit --tag` silently fails but the follow-up `gcloud run services update --image :latest` succeeds against a stale image, producing a confident "Deploying... Done" message with pre-fix code still live. Use when: (1) gcloud builds submit prints "ERROR: (gcloud.builds.submit) Invalid value for [source]: Dockerfile required when specifying --tag" but the shell scrolls past it, (2) code changes verified locally aren't visible in production after a deploy that printed "revision ... has been deployed and is serving 100 percent of traffic", (3) Chaining `gcloud builds submit` and `gcloud run services update` in a single copy-paste block, (4) Using the mutable `:latest` tag for Cloud Run deploys.

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。

WordPress performance code review and optimization analysis. Use when reviewing WordPress PHP code for performance issues, auditing themes/plugins for scalability, optimizing WP_Query, analyzing caching strategies, checking code before launch, or detecting anti-patterns, or when user mentions "performance review", "optimization audit", "slow WordPress", "slow queries", "high-traffic", "scale WordPress", "code review", "timeout", "500 error", "out of memory", or "site won't load". Detects anti-patterns in database queries, hooks, object caching, AJAX, and template loading.

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。

Write and ship a blog post for your project that reads like a human wrote it and pulls SEO traffic to your site. Use when a contributor wants to draft, edit, or publish a blog post, add a post to the site, write about the project for search/traction, or make an existing draft sound less like AI. Enforces the house voice (no em dashes), picks a non-cannibalizing angle, uses ASD-STE100 Simplified Technical English for technical prose, wires the post into your site, and runs the humanizer pass before shipping.

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。

Think and work like an expert Molecular Neuroscientist. Use when a task calls for Molecular Neuroscientist judgment. Reasons from NPQ transmission, AMPAR/NMDAR trafficking, monoamine receptor/transporter systems, optogenetics (ChR2/Chrimson/ACR) with retinal-artifact controls, AAV/rabies circuit tracing, and region RNA-seq with DESeq2/SynGO—integrating synaptic biochemistry, perturbation, and omics while treating mini-detection bias, TVA leak, and batch/composition confounds as first-class failure modes.

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。

Think and work like an expert Computer Hardware Engineer. Use when a task calls for Computer Hardware Engineer judgment. Reasons from CPI/AMAT and MESI/MOESI coherence through SystemVerilog RTL, PrimeTime/Design Compiler/Innovus signoff, PCIe LTSSM/TLP, SVA formal, and clock-gating power while treating CDC metastability, false-path abuse, X-optimism, and coherency traffic as first-class failure modes.

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。

Search articles, videos, and content within the WeChat ecosystem. Covers cross-content-type search including Official Account articles, video channels, and mini-programs — the gateway to China's largest private-traffic platform.

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。

Provider-independent media quality control and delivery direction for generated images, video, audio, ads, product content, social clips, broadcast and streaming assets, localization packages, and mixed-source edits. Use when an agent must define final export specs, inspect technical and perceptual quality, check captions/audio/color/accessibility/provenance/rights, create delivery sheets, version filenames, checksums, manifests, package deliverables, set acceptance or rejection criteria, and perform final delivery QA before handoff to a platform, broadcaster, streamer, client, or ad trafficker.

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。