「cluster」の検索結果

Analyze LLM eval results — score breakdowns by category, regression detection vs baseline, failure clustering. Use when asked to "analyze our eval results", "did the model regress", or "cluster the eval failures".

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。

Patterns for building entity actors with Akka.Hosting - GenericChildPerEntityParent, message extractors, cluster sharding abstraction, akka-reminders, and ITimeProvider. Supports both local testing and clustered production 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。

Akka.Management for cluster bootstrapping, service discovery (Kubernetes, Azure, Config), health checks, and dynamic cluster formation without static seed nodes.

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。

Run SQL against Amazon Redshift — submit statements, poll status, page through results, and browse databases/schemas/tables. Use this whenever the user wants to query Redshift (provisioned cluster or Serverless), ask "what tables are in this schema", check a query's status, or mentions `redshift-data`, a Redshift cluster identifier / workgroup name, or a `redshift-data.{region}.amazonaws.com` endpoint. Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.

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 an AEO-first topical map optimized for AI search citation — entity clusters, query fan-out coverage, information gain scoring, and multi-platform distribution. Produces entity map, content node architecture, schema blueprint, and 90-day publishing calendar. Use when "topical map", "content architecture", "site structure", "topic clusters", "pillar content plan", "AEO map", "AI search architecture", "entity map", "what content should we build", or any request to plan a site's topical structure for AI search visibility.

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for Kubernetes API analysis, service-account trust, RBAC edges, admission and controller behavior, cluster secrets, workload mutation, and namespace-scoped drift. Use when the user asks to inspect kube API permissions, service-account tokens, RoleBinding or ClusterRoleBinding edges, admission webhooks, controller-created pods, secret exposure, or why live workloads differ from manifests. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

Run CIS benchmark checks against cluster nodes and control planes when an agent needs a narrow Kubernetes hardening audit, not a general platform listing.

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。

Run K8sGPT as an MCP server so an agent can scan a Kubernetes cluster, explain unhealthy resources, and return prioritized remediation clues in natural language.

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。

Customer feedback analysis — sentiment detection, NPS/CSAT frameworks, feature request clustering, support ticket triage, churn signal detection, and feedback-to-roadmap translation

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 evidence-labeled semantic cores for SEO, LLM-readable architecture, and site information design. Use this skill when the user asks for semantic core, query clusters, search intents, audience segments, entity/topic maps, language or locale priorities, competitor/source evidence, pillar-topic planning, or a structured handoff before URL architecture and internal linking. It must mark unknown search volume, difficulty, and ranking data as unknown instead of inventing metrics.

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。

Deploy applications to Kubernetes clusters using kubectl manifests for Deployments, Services, ConfigMaps, Secrets, and Ingress resources. Implement health checks, resource limits, rolling updates, and Helm chart packaging for production deployments. Use when deploying new applications to EKS, GKE, AKS, or self-hosted clusters, migrating from Docker Compose to container orchestration, implementing zero-downtime rolling updates, or setting up multi-environment deployments across dev, staging, and production.

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。

Pythonic wrapper around RDKit with a simplified interface and sensible defaults. Preferred for standard drug discovery work — SMILES/SELFIES/InChI conversion, molecule standardization and sanitization, descriptors, ECFP and other fingerprints, Tanimoto distance matrices, Butina clustering and diverse subset picking, Bemis-Murcko scaffolds and scaffold splits, BRICS/RECAP fragmentation, 3D conformer generation, SDF/CSV/Excel and cloud I/O, and parallel processing via n_jobs. Returns native rdkit.Chem.Mol objects, so it composes with RDKit throughout. Also trigger on datamol, `import datamol as dm`, dm.to_mol, dm.standardize_mol, dm.cluster_mols, or dm.pick_diverse. For advanced control or custom parameters, use the rdkit skill directly.

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。

Analyze Ascend NPU collective communication profiling data with a DB-first workflow. Use when the user provides `cluster_analysis_output/cluster_analysis.db`, rank-level `analysis.db`, rank-level `ascend_pytorch_profiler_{rank_id}.db`, together with `profiler_info.json`, and asks about HCCL or hcom communication cost, collective communication TOP ops, wait time, slow rank/straggler, Notify Wait, bandwidth, retry, relay, SDMA/RDMA/HCCS links, communication matrix, or Ascend communication fault patterns.

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 documenting a named campaign across time and victims, the user asks to start or update a campaign record, or another skill identified a multi-incident cluster that warrants formal tracking. Provides the template (timeline, attribution, victimology, attack chain, Diamond Model mapping, IOC clusters) and the lifecycle from active to historical.

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 Louvain community detection to discover densely connected groups in networks. Use this skill when the user needs to find communities or clusters in social/organizational networks, segment customers by interaction patterns, or analyze network modular structure — even if they say 'find groups in this network', 'community detection', or 'network clustering'.".

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。

Exploit misconfigured Kubernetes Role-Based Access Control (RBAC) to escalate privileges within a cluster. This skill covers identifying overly permissive roles and bindings, and leveraging them to gain cluster-admin access or compromise the host nodes.

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。

Kubernetes and container security skill for cluster hardening, RBAC review, admission control (OPA Gatekeeper, Kyverno), Pod Security Standards, network policies, secrets management, runtime defense (Falco, Tetragon), image supply chain (cosign, SLSA, in-toto), and CIS benchmark compliance. Use to assess and harden clusters you operate.

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。

Builds Flux manifests locally, diffs YAML files, patches FluxInstance upgrades, creates authentication secrets, traces GitOps delivery pipelines, and bootstraps clusters with the Flux Operator. Use this skill whenever the user mentions flux-operator, FluxInstance, FluxReport, ResourceSet, ResourceSetInputProvider, Flux CD operator management, or asks about GitOps CLI tooling for Kubernetes with Flux. Also trigger when users ask about building Flux manifests, diffing YAML, patching Flux instances, creating Flux secrets, tracing GitOps delivery pipelines, or bootstrapping clusters with Flux. Even if the user just says "flux operator" or "flux-operator cli" without details, this skill has the authoritative reference.

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 deciding WHERE to point SEO effort, not how to write a page. Triggers: a new site or brand with no rankings and no authority ("cold start", "starting from zero", "nobody knows us"), deciding what to double down on, a page or cluster that ranks but earns nothing, hunting emerging or newly-coined keywords before competitors arrive, "should we build a cluster or one page", "what do we write next", "we get impressions but no clicks", "our traffic plateaued despite publishing", whether to chase a head term at all, or turning any of it into recurring automation. Also use when asked why an SEO effort stalled despite consistent output. NOT for writing an article, keyword expansion mechanics, or auditing a single tactic for penalty risk.

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。

Full-lifecycle blog engine with 31 sub-skills, 12 templates, 100-point scoring, and 5 agents. Routes requests to the right sub-skill: writing, rewriting, analysis, outlines, audits, schema, charts, images, repurposing, AI citation SEO, FLOW prompts, topic clusters, and multilingual publishing. Optimized for Google rankings, E-E-A-T, and AI citations. Supports any platform. Use when user says "blog", "blog post", "blog audit", "topic cluster", "multilingual blog", or any /blog subcommand.

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。

Orchestrates comprehensive production readiness reviews and assessments for GKE clusters and workloads across scalability, security, reliability, observability, backup/DR, and cost optimization. Use when asked to productionize, prepare, assess, audit, or review a GKE cluster or workload before going live to production. Don't use for deep-dive single-domain implementation (use specific domain skills like gke-workload-scaling, gke-platform-security, gke-workload-security, gke-service-networking, gke-reliability instead).

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。

Diagnoses Workload Identity Federation for GKE authentication failures for Pods (403 "iam.serviceAccounts.getAccessToken" / permission denied, "could not find default credentials", or GKE metadata server unreachable) by verifying cluster and node-pool Workload Identity configuration, the Kubernetes ServiceAccount (KSA) to IAM binding (direct principal binding and legacy Google ServiceAccount impersonation), target-resource IAM roles, and gke-metadata-server health. Use when a Pod cannot authenticate to Google Cloud APIs even though Workload Identity is expected to be in effect. Don't use for in-cluster Kubernetes RBAC errors (API-server authorization), general workload crashes (use gke-workload-troubleshooting), or Workload Identity setup and hardening (use gke-workload-security).

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。

Provides GKE golden path configuration defaults, production readiness checklists, and cluster default patterns. Use when designing GKE clusters, verifying GKE production readiness, or checking configurations against GKE defaults. Don't use for setting up workload autoscaling specifically (use gke-workload-scaling instead).

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。

Configures alerting policies in Terraform for Google Kubernetes Engine (GKE) clusters, workloads, and services using PromQL and Google Cloud Managed Service for Prometheus. Use when writing, analyzing, validating, or deploying Terraform alerting policies to monitor GKE service latency, traffic, error rates using Multi-Window Multi-Burn-Rate SLO alerts, memory saturation, and cluster health such as CrashLoopBackOff and Node NotReady conditions. Don't use for non-GKE compute runtimes such as standalone Compute Engine VMs or standalone Cloud Run services without GKE.

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。