'ics' 검색 결과

Build repeatable business reports from connected data sources or uploaded CSV and XLSX files. Define metrics and comparisons, check the calculations, deliver a concise summary and Excel workbook, and save the report definition for future or scheduled runs.

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• Comparing sales by location with last year
• Reviewing overdue receivables by age
• Tracking labor costs as a percentage of revenue

Plan technology investments and engineering team growth using structured assessments. Evaluate technical debt, compare building versus buying, document architecture choices, and define engineering health metrics to produce prioritized plans grounded in costs, risks, and business needs.

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• Prioritize technical debt fixes by impact and effort
• Plan engineering hiring, timing, and budget
• Compare building internally with buying a service

Query Prometheus metrics through Grafana and manage dashboards as JSON files. Use it to investigate current values and trends, add or adjust panels, and preview dashboard updates before applying them.

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• Investigating error rates and response times
• Adding panels to a Grafana dashboard
• Managing dashboard configuration as JSON

Investigate stalled TPU training across multiple slices on GKE using hang reports, logs, and latency metrics. Separate compilation inconsistencies and data input stalls from hardware or network faults, and identify the relevant nodes for further investigation.

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• Investigating HANG_DETECTED logs during TPU training
• Separating input stalls from hardware faults
• Mapping reported faulty instances to GKE nodes

Investigate Google Cloud network problems using traffic logs, performance metrics, and connection path tests. Identify blocked traffic, packet loss, and latency, summarize traffic volumes, or estimate VPC Flow Logs costs.

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• Investigate blocked traffic and packet loss
• Check latency and throughput trends
• Analyze Cloud NAT port exhaustion

Design AI agent analytics workflows across distributed tables and documents. Combine Knowledge Catalog for metadata, meaning information about data and its usage rules, with managed Spark processing to produce architecture diagrams, deployment guidance, and validation plans.

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• Design analytics across data stored in multiple clouds
• Define metadata and governance with Knowledge Catalog
• Plan Data Agent Kit use in VS Code or Antigravity

Configure Cloud Logging within one Google Cloud project. Set storage locations, retention periods, routing rules, and viewing permissions; create metrics from logs; and adjust stored volume through exclusions or sampling, which retains only a portion of entries.

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• Set regional log storage and retention periods
• Restrict access to sensitive logs
• Count specific errors with logs-based metrics

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

Create weekly reports from Postgres metrics, comparing current figures with recent weeks. Add plain-language explanations for notable changes, flag missing data, and post one report to Slack while keeping database access read-only.

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• Preparing a weekly metrics report for Slack
• Reviewing weekly changes and recent trends
• Explaining notable changes and flagging data gaps

Turn Matomo analytics into reports on organic traffic, landing pages, devices, countries, referrers, and search keywords. Use data from a self-hosted instance or Matomo Cloud to support reporting alongside GA4 or as an alternative.

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• Review organic traffic trends in Matomo
• Identify popular landing pages
• Compare visits by device, country, and referrer

Add business knowledge missing from a Wren project's database definitions. Compare source documents with existing context, then add explanations, rules, SQL examples, and proposed metrics through guided questions or an automatic mode with an audit report.

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• Documenting status codes, units, and NULL meanings
• Adding business knowledge from handbooks and glossaries
• Defining aggregation metrics such as ARR or DAU

Find the Wren CLI workflow for answering data questions, connecting databases or SaaS sources, generating model definitions, adding business metrics, or building a shareable analytics app. Guides are retrieved from the installed CLI version.

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• Set up Wren and connect a database
• Load HubSpot or Stripe data through dlt
• Generate model definitions and add business metrics

Manually drive a vehicle in CARLA, set a target speed, operate lights and doors, and adjust mass or drag. You can also inspect the command topics used to control the main vehicle through ROS 2.

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• Manually test acceleration, steering, and braking
• Check vehicle lights and door operation
• Compare handling after changing mass or drag

Track machine learning experiments with Trackio by recording training measurements, viewing live dashboards, and flagging diagnostic events. Retrieve metrics and alerts through command-line queries, with JSON output for automated monitoring and experiment review.

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• Recording training loss and accuracy
• Flagging loss spikes or stalled training
• Monitoring cloud training through a Space dashboard

Review how evidence can be altered during authorized security exercises on Windows and Linux. Covers logs, timestamps, memory, disk records, and network traces, alongside the signs defenders use to detect manipulation.

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• Planning authorized evidence manipulation tests
• Reviewing Windows and Linux forensic artifacts
• Checking detection coverage for altered evidence

Evaluate SaaS customer economics and cash efficiency using acquisition costs, customer lifetime value, margins, and payback periods. Compare segments and channels, estimate how long cash will last, and assess whether to expand spending or improve efficiency.

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• Reviewing customer economics before increasing paid acquisition
• Comparing acquisition costs and payback across channels
• Estimating cash runway and funding needs

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

Build dashboards from verified product or business data, with clearly defined metrics, charts, and useful filters. Organize the view from headline results to supporting detail, and check data freshness, numerical consistency, and dashboard behavior before handoff.

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• Monitor key product or business metrics
• Build a dashboard for recurring operating reviews
• Explore trends and differences across segments

Inspect scientific data files to understand their format, structure, and quality. Covering over 200 formats, this skill produces Markdown reports with statistical summaries, potential issues, and recommendations for preprocessing, visualization, and further analysis.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Inspecting an unfamiliar scientific data file
• Checking missing values and duplicates in experimental tables
• Reviewing sequence lengths and quality scores in FASTQ files

Analyze empirical economics PDFs through five questions covering the research problem, causal identification, target effect, robustness, and contribution. Receive a structured Chinese report and a practical checklist of research methods to adapt to other studies.

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• Reading an empirical economics paper systematically
• Understanding how a paper identifies causal effects
• Examining robustness checks and research limitations

Build a Python workflow for empirical research, from data cleaning and statistical checks to estimation, robustness checks, and paper-ready tables and figures. Economics is the default, with additional modes for epidemiology, public health, and machine learning for causal inference.

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• Building an end-to-end empirical analysis workflow in Python
• Replicating an applied economics paper
• Producing regression tables and event-study plots

Proofread applied microeconomics manuscripts across prose, equations, tables, notes, and references. The workflow combines specialized reviews into a prioritized report with exact locations, suggested corrections, consistency checks, and items requiring author verification.

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• Proofreading an economics paper before submission
• Checking numbers against tables and figures
• Reviewing equations and terminology

Turn JSON data into browser-ready infographics with metric cards, grouped bar charts, process diagrams, or mixed dashboards. Outputs include all styling and SVG graphics in one HTML file, with preset palettes and icons and no external dependencies.

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• Display team KPIs as metric cards
• Compare monthly or quarterly figures
• Illustrate business processes as flow diagrams

Explore scientific data files to understand their structure, statistical patterns, and quality. References cover over 200 formats, with workflows for producing Markdown reports that summarize findings and recommend preprocessing, visualizations, and further analysis.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking missing values and outliers in experimental tables
• Examining sequence lengths and quality scores in FASTQ files
• Inspecting microscopy image dimensions and metadata