'metric' 검색 결과

Explore and query measurements stored in Axiom MetricsDB. The skill discovers metrics and tags, checks measurement types and units, and builds MPL queries, Axiom’s metrics query language, to retrieve values and aggregate them over a chosen period.

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• Explore available metrics and tag values
• Check metrics for a specific service or host
• Aggregate values over a selected time range

Collect and assess quality metrics for Simulink models using the metric.Engine API. Use when evaluating model design quality (maintainability, complexity, architecture) or testing completeness (unit testing, SIL, PIL, coverage, pass rates) after edits or test runs. Covers metric discovery, collection, report generation, and diagnostics. Triggers on: "collect metrics", "quality metrics", "metric.Engine", "testing metrics", "maintainability metrics", "SIL metrics", "PIL metrics", "what metrics are available", "assess quality", "coverage metrics", "generate metric report", "diagnose metric errors".

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Diagnose failures in GKE HorizontalPodAutoscaler (HPA), which adjusts Pod counts automatically. Investigate missing metrics, unexpected scaling behavior, and slow reactions, then receive proposed configuration fixes or commands for review before deployment.

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• Investigating unknown metrics and HPA errors
• Diagnosing workloads that will not scale up or down
• Troubleshooting scaling to and from zero

Choose one North Star Metric, a central measure of customer value, and three to five supporting measures that influence it. Classify the business by how customers use it and assess the proposed metric against seven criteria.

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• Choosing a central measure of customer value
• Organizing the metrics a team tracks
• Comparing candidates for a North Star Metric

Choose and audit startup metrics using Croll and Yoskovitz's "Lean Analytics". Use when the user mentions "what metrics should we track", "KPIs", "north star metric", "One Metric That Matters (OMTM)", "vanity metrics", "analytics dashboard", "DAU/MAU", "churn benchmark", or "measure product-market fit". Also trigger when choosing metrics for a startup or feature, auditing a dashboard for vanity metrics, setting metric targets and baselines, or instrumenting a product by business model and stage. Covers good-vs-vanity metrics, the One Metric That Matters, metrics by business model, the five startup stages, and benchmarks. For the build-measure-learn loop, see lean-startup. For fixing activation and retention, see improve-retention.

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Find relevant Google Cloud monitoring metrics for a service or resource. Retrieves current metric definitions through the API, filters them by the requested concepts, and presents selected metrics with their names, data types, units, and monitored resources.

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• Find CPU metrics for Compute Engine
• Identify Cloud Storage write throughput and request metrics
• Check metric data types and units

Add team-specific evaluation criteria to plugin-eval by designing a metric pack, a set of custom checks and measurements. Create its manifest and JSON-producing script, then run it through the evaluator using a consistent output structure.

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• Add team-specific evaluation criteria
• Define custom checks and numerical metrics
• Build a metric-pack manifest and script

Choose metrics for AI product revenue, sales efficiency, retention, and computing costs. Plan dashboards and weekly reviews, assess customer data quality, and decide how to credit marketing and sales interactions for completed deals.

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• Choosing revenue and sales metrics for an AI product
• Planning dashboards for executives and sales teams
• Assessing CRM data quality

Builds your north star metric, a metric tree with owned input metrics and guardrails, and a review cadence, as a one-page metrics spec you can paste into a doc. Use it when you ask 'what should our north star be', 'which metric should this team goal on', 'our dashboard is a mess', 'how do we measure AI productivity', 'activation metric', 'OKR metrics', 'vanity metrics', or 'metrics review cadence'. Draws on 254 insights from 118 Lenny's Podcast guests incl. Sarah Tavel, Crystal Widjaja and Jessica Lachs.

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Metrics architecture — produce a complete metrics plan given a product description. North Star, input metrics tree, instrumentation spec, action triggers, and counter-metrics. Use when asked to "design a metrics framework", "what should we measure", "build a metrics system", "define our KPIs", "what are our success metrics", "metrics strategy", or "what do we track".

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Quickly look up SaaS financial metrics, including recurring revenue and customer acquisition costs. Get definitions, formulas, benchmarks, and warning signs, plus reminders of which metrics matter when reviewing features, pricing, acquisition channels, or business health.

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• Looking up NRR formulas and benchmarks
• Preparing a SaaS metrics sheet for a business review
• Checking metrics before expanding an acquisition channel

Conducts Data Protection Impact Assessments for biometric identification and authentication systems under GDPR Article 35 and Article 9 special category rules. Covers facial recognition, fingerprint, iris scanning, voice recognition, and behavioural biometrics. Applies EDPB Guidelines 3/2019, CNIL Reglement Type Biometrie, and ISO/IEC 24745 biometric template protection. Keywords: DPIA biometric, facial recognition, fingerprint, Art. 35, Art. 9, biometric template, special category.

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VictoriaMetrics provider for obz. Covers all 6 metric commands (query, list, info, labels, label-values, series) using MetricsQL, a PromQL superset with extensions like keep_metric_names and range_normalize. This skill should be used when the user mentions "VictoriaMetrics", "MetricsQL", "obz metric -p vm", or needs to query metrics from a VM backend.

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Build graph neural networks, models that learn from connected nodes, with PyTorch Geometric. Supports node and graph classification, link prediction, custom datasets, and training approaches for large graphs or graphs containing multiple types of nodes and connections.

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• Build node and graph classification models
• Predict links between nodes
• Train models on graphs with multiple node and connection types

Choose and track startup metrics suited to the business model and funding stage. Calculate customer acquisition cost, customer lifetime value, retention, and cash usage, then organize trends and targets for operational reviews and investor reporting.

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• Choose core metrics for a startup's stage and business model
• Calculate customer acquisition cost and lifetime value
• Review retention, growth efficiency, and cash usage

Monitor TPU workloads on GKE using system metrics and PromQL, a metrics query language. Check utilization, memory, node availability, and interruptions to investigate infrastructure issues and calculate average recovery times and intervals between interruptions.

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• Checking TPU utilization and memory usage
• Investigating node and node pool availability
• Tracing maintenance and preemption interruptions

Configure GKE logging, metrics collection, dashboards, and alerts with Cloud Logging, Cloud Monitoring, and managed Prometheus. Diagnose missing measurements, collection failures, permission errors, and alert rule problems to identify appropriate configuration changes.

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• Setting up GKE logging and metrics collection
• Investigating missing Prometheus metrics
• Planning dashboards and alerts for cluster health

Turn Cloud Monitoring metric definitions and resource filters into validated PromQL queries, a language for querying monitoring data. Choose suitable aggregation functions, preserve supplied resource names, and format queries for use in Cloud Monitoring.

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• Convert Cloud Monitoring metric definitions into PromQL
• Filter monitoring queries to specific resources
• Choose aggregations that match metric types

Produce a complete metrics definition doc — metric name, formula, data source, segmentation, SQL or event tracking spec, and what good/bad looks like. Given a product area, outputs the full metrics spec. Use when asked to "define KPIs", "metrics framework", "what should we measure", "north star metric", or "instrument this feature".

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Analyze flow metrics to identify delivery bottlenecks and improve throughput. Use when the user says "flow metrics", "why is delivery slow", "cycle time analysis", "WIP analysis", "throughput review", "what's our lead time", "DORA metrics", "where is work getting stuck", "Kanban metrics", or wants to diagnose delivery performance using data - even if they don't explicitly say "flow metrics review". Based on "Making Work Visible" by Dominica DeGrandis (WIP limits, flow blockers, time thieves) and "Accelerate" by Forsgren, Humble & Kim (DORA metrics, delivery performance indicators).

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Define a practical KPI dashboard, a view of key business measures, by organizing metric definitions, sources, formulas, periods, targets, and owners. Produces empty CSV, SQL, JSON Schema, or Notion import mappings when requested.

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• Choose metrics for a business decision
• Review an overcrowded KPI dashboard
• Create a CSV register of metric definitions

Design KPI dashboards that help teams make decisions using clearly defined performance measures. Choose relevant metrics, organize summary and detail views, document calculations, and address inconsistent figures, database load, and excessive alerts.

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• Design a SaaS executive metrics dashboard
• Build a live service health monitoring view
• Analyze retention by signup cohort

Plan technology strategy and engineering team growth with frameworks for technical debt, hiring, architecture decisions, and vendor evaluation. Use Python tools and decision templates to prioritize improvements, structure teams, and track engineering performance.

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• Prioritizing technical debt reduction
• Planning hiring and engineering team structure
• Comparing technologies and vendors

Review product performance by comparing usage, retention, revenue, and other metrics with previous periods and targets. Turn the numbers into a scorecard, explain meaningful trends and uncertainties, and recommend investigations or improvement experiments.

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• Prepare weekly, monthly, or quarterly metrics reviews
• Investigate sudden spikes or drops
• Compare product performance with targets