「metric」の検索結果

Use when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.

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。

SaaS financial health advisor. Use when a user shares revenue or customer numbers, or mentions ARR, MRR, churn, LTV, CAC, NRR, or asks how their SaaS business is doing.

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。

Analyzes campaign performance with multi-touch attribution, funnel conversion analysis, and ROI calculation for marketing optimization. Use when analyzing marketing campaigns, ad performance, attribution models, conversion rates, or calculating marketing ROI, ROAS, CPA, and campaign metrics across channels.

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。

Design production-ready observability strategies combining metrics, logs, and traces. Includes SLI/SLO design, golden-signals monitoring, alert optimization. Use when adding observability to a new service, refactoring alerting that is too noisy, or designing an SLO program before scaling production load.

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 the user asks to optimize prompts, design prompt templates, evaluate LLM outputs with an eval set, measure RAG retrieval quality, validate agent/tool configurations, analyze token usage, or design structured-output contracts. Covers eval-driven prompt iteration, RAG metrics (relevance, faithfulness, coverage), agent workflow validation, and token/cost budgeting — all model-agnostic, with three stdlib Python tools.

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。

/cs:vpe-review <plan> — Throughput-first VP of Engineering interrogation of any plan that touches delivery, eng hiring, team structure, or production discipline. Use when cycle time balloons, DORA metrics slide, or before committing to an eng hiring wave or a reorg.

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。

VP of Engineering advisory for startups: delivery throughput (DORA 4 metrics + bottleneck identification), engineering hiring funnel (sourcing → screen → onsite → offer conversion + time-to-fill + pipeline gap), engineering team structure (squad/tribe/chapter design + tech-lead manager-trigger thresholds), and production discipline (on-call, deployment cadence, postmortem culture). Use when sprint velocity is dropping, eng hiring is broken, team structure is unclear, or deciding when to add a tech-lead manager. NOT a CTO skill (which owns architecture) — VPE owns delivery operations and how the team ships.

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。

/cs:post-mortem <decision> — Honest retrospective on an executed decision, scored against original assumptions and dissent. Closes the strategic sprint loop. Use when a decision hits its 90-day review checkpoint or its kill criteria trigger — e.g. scoring last quarter's pricing change against its pre-committed success 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。

Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success. Use when analyzing customer accounts, reviewing retention metrics, scoring at-risk customers, or when the user mentions churn, customer health scores, upsell opportunities, expansion revenue, retention analysis, or customer analytics. Runs three Python CLI tools to produce deterministic health scores, churn risk tiers, and prioritized expansion recommendations across Enterprise, Mid-Market, and SMB segments.

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。

Generate a Lean Canvas with problem, solution, metrics, cost structure, UVP, unfair advantage, channels, segments, and revenue. Use when exploring a lean startup canvas, testing a business hypothesis, or modeling a new venture.

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。

Create a comprehensive product strategy using the 9-section Product Strategy Canvas — vision, segments, costs, value propositions, trade-offs, metrics, growth, capabilities, and defensibility. Use when building a product strategy, creating a strategic plan, or defining product direction.

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。

Create a go-to-market strategy covering marketing channels, messaging, success metrics, and launch timeline. Use when planning a product launch, creating a GTM plan from scratch, or defining a launch strategy for a new market.

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。

Milestone progress review — completeness, quality metrics, risk, go/no-go recommendation. At checkpoints or before a deadline.

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 a named target with a measured loop: attribute a workload's cost, or score variants and keep winners. Use when a working system's metric should move and the winning change is not already known. Use ce-debug when the job is diagnosis; use ce-work when the change is already known.

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。

Discover keyword opportunities, evaluate metrics and SERPs, and save/tag promising terms.

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。

Generates Terraform HCL configurations for Cloud Monitoring notification channels (`google_monitoring_notification_channel`) and adds notification channels to alerting policies (`google_monitoring_alert_policy`) in Google Cloud projects. Use when creating or configuring email, SMS, Slack, PagerDuty, Pub/Sub, or webhook notification channels, verifying Pub/Sub topic compatibility (including cross-project topics, message storage policies, and in-transit enforcement) and publishing permissions for the Cloud Monitoring Service Agent, or adding notification channels to existing alert policies. Don't use for authoring or editing alerting policy conditions, selecting metric descriptors, or writing PromQL or Monitoring Filter queries.

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。

Diagnose GKE Cloud TPU multi-slice training hangs (Megascale `HANG_DETECTED` logs and hang monitored events) using the ML Diagnostics `Megascale XLA (MXLA) Hang Analyzer` (`gcloud alpha mldiagnostics monitored-events`) and 1-minute Cloud Monitoring multi-slice latency metrics (`kubernetes.io/container/multislice/*`). Distinguishes XLA compiler/HLO launch divergence and host data-input stalls from TPU chip, SparseCore, ICI, or network fabric faults. Use when multi-slice TPU training jobs freeze without progressing steps, emit `HANG_DETECTED`, or stall in collective operations. Don't use for gradual step-time throughput drops without hangs (use gke-ai-troubleshooting-tpu-performance-degradation) or pod preemption/eviction restarts (use gke-ai-troubleshooting-jobset-interruption).

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。

Diagnose GKE Cloud TPU training throughput drops and step-time regressions (15%+ TPU duty-cycle drop) using ML Diagnostics Workload Monitoring (`gcloud alpha mldiagnostics monitored-events` / `hypercomputecluster.googleapis.com/v1alpha`) and 1-minute Cloud Monitoring system metrics (`kubernetes.io/node/accelerator/*`). Distinguishes hardware and network fabric throttling from workload resource bottlenecks (HBM capacity, host memory, or host CPU saturation). Use when TPU training throughput or duty cycle drops without crashing pods, when `PERFORMANCE_DEGRADATION` monitored events fire, or when triaging slow multi-slice training steps. Don't use for complete multi-slice XLA execution stalls with `HANG_DETECTED` logs (use gke-ai-troubleshooting-tpu-mxla-hang) or pod eviction/interruption restarts (use gke-ai-troubleshooting-jobset-interruption).

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 and debug OAuth 2.0 DPoP (RFC 9449) refresh token sender-constraining for WebCrypto, Node.js ES6, and browser runtimes integrating with Google's OAuth platform. Use when configuring non-extractable asymmetric key pairs (P-256), generating DPoP Proof JWTs for authorization code exchange and token refresh, or handling 400 use_dpop_nonce challenge retry loops at oauth2.googleapis.com/token. Don't use for unconstrained OAuth 2.0 flows (where refresh tokens are not bound to a client key pair), or for Google Cloud IAM / service account authentication.

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。

Mounts Cloud Storage buckets on Google Cloud as a POSIX file system using Cloud Storage FUSE (and the gcsfuse CLI tool). Use when interacting with the gcsfuse CLI or Cloud Storage FUSE mounts: decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size file, stat, and list caches, tune mount flags (--implicit-dirs) or config-file settings, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace/HNS, close-time finalization, concurrent writers), or diagnose slow training, low throughput, or bill spikes with gcsfuse metrics. Covers mount semantics, gcsfuse CLI and config files, GKE gcsfuse CSI driver (Workload Identity principal:// bindings, profile StorageClasses, sidecar sizing), and Cloud Run volume mounts. Don't use for bucket administration or data management without a mount (google-cloud-storage-basics) or fully POSIX-compliant shared file systems (Filestore, Managed Lustre; use gke-storage).

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。

Answer natural language questions and perform analysis on GKE cluster and workload costs using BigQuery billing exports, cost allocation data, and live cluster monitoring metrics. Use when querying GKE costs across projects, namespaces, or workloads, analyzing billing reports in BigQuery (`bq`), checking cluster cost budgets (`gcloud billing`), or diagnosing cost drivers like pod requests vs. actual utilization (`kubectl top`). Don't use for applying cost optimization changes, creating rightsizing manifests (VPA/MPA), or selecting ComputeClasses (use gke-cost-optimization 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, predicts, and mitigates node disruptions during Compute Engine host maintenance and hardware or software maintenance events for GPU and TPU workloads on GKE. Use when diagnosing node disruptions, predicting host maintenance events on GPU/TPU nodepools, inspecting node interruption PromQL metrics, auditing node taints, or configuring workload protection strategies (graceful termination, opportunistic maintenance, PodDisruptionBudgets). Don't use for general GKE cluster creation, network policy configuration, or non-disruption workload deployment.

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 GKE nodes reporting NotReady or Unknown status by inspecting node conditions, events, kubelet/containerd logs, and node metrics, then proposing safe remediations. Use when nodes show NotReady, when the kubelet stops posting node status, or when workloads are evicted or stuck Pending due to node health. Don't use for pod-level application failures (use gke-workload-troubleshooting), autoscaler scale-up/scale-down decisions (use gke-cluster-autoscaler), or non-GKE compute.

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。

Guides developers through downloading, configuring, and installing the official open-source Google Ads MCP Server. Use this skill when a user wants to connect their AI assistant (such as Gemini, Claude Code, or Cursor) to their Google Ads account to query campaigns or retrieve reporting metrics using 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。