'correlation' 검색 결과

Investigate Entra ID reconnaissance by searching Graph API logs for tool signatures and unusual directory queries. Use KQL, a log query language, to correlate suspicious requests with sign-ins and turn useful searches into Sentinel detection rules.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating Graph API activity after suspected credential theft
• Checking whether offensive tool activity is visible in logs
• Creating and tuning scheduled Sentinel detection rules

Set up MISP to collect threat feeds and centrally manage indicators such as suspicious IP addresses and domains. Configure automated synchronization, connect related findings, and export indicators to security monitoring platforms or blocklists.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Centralizing threat indicators from multiple feeds
• Deploying MISP with Docker
• Automating threat feed synchronization

Build Splunk pipelines that import threat intelligence and match security events against indicators such as malicious IP addresses, domains, and file hashes. Add source, confidence, and severity details so analysts have context when investigating matches.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Importing external threat feeds into Splunk
• Matching network and endpoint logs against threat indicators
• Adding threat and asset context to alerts

Analyze Windows Amcache records to investigate applications, deleted executable traces, and drivers. Extract file details, compare SHA-1 hashes with malware databases, and combine records into a timeline to support incident investigations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating traces of deleted executables
• Checking software installed during an incident
• Comparing file hashes with malware databases

Analyze a software bill of materials, an inventory of software components, against known vulnerability records. Trace indirect dependencies, assess risk, and produce reports that help teams prioritize fixes and review supplier software.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing vulnerabilities in vendor-provided software inventories
• Checking whether a newly disclosed CVE affects deployed software
• Adding SBOM vulnerability checks to CI/CD

Analyze data distributions, trends, unusual values, and differences between groups using suitable statistical methods. The skill guides interpretation of effect sizes, meaning the magnitude of differences, alongside uncertainty and business impact, while checking for misleading conclusions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Summarize business metrics beyond averages
• Assess differences in A/B test results
• Investigate outliers and unusual time-series values

Write SQL that uses BigQuery’s built-in machine learning and generative AI for forecasting, anomaly detection, identifying drivers of change, searching by meaning, and classifying or summarizing text stored in your data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Forecasting future values from time-series data
• Detecting anomalies and investigating drivers of change
• Searching documents by meaning

Structure open-source intelligence research, meaning investigation using publicly available information, for authorized external assessments. Map organizational assets, record evidence and confidence levels, prioritize exposures, and prepare reports that explain technical findings and business risks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Mapping an organization's externally exposed assets
• Recording evidence sources and confidence levels
• Prioritizing exposures within a limited research budget

Support authorized external security reconnaissance, the discovery of exposed systems and information. The skill provides search queries, probe references, secret detection patterns, and scoring guidance to help identify assets and organize findings with evidence and confidence levels.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Discovering public subdomains, APIs, and cloud storage
• Investigating exposed API keys and other secrets
• Identifying SSO services and Microsoft 365 exposure

Assess Microsoft 365 and Entra ID authentication using tenant discovery, login error analysis, and browser-based verification. It provides attempt tracking and evidence templates to distinguish credential results from access-policy restrictions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Map tenants associated with corporate domains
• Interpret login errors and access-policy blocks
• Manage attempt limits during credential testing

Create statistical charts from tabular data to explore distributions, relationships, and group differences. This skill guides plot selection, multi-panel layouts, and styling, including box plots, heatmaps, and charts with estimates and uncertainty bands.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Exploring data distributions and unusual values
• Comparing measurements across groups
• Visualizing correlations between variables

Econometrics skill for time series analysis. Activates when the user asks about: "time series", "stationarity", "unit root test", "ADF test", "KPSS test", "ARIMA", "ARMA", "autocorrelation", "ACF", "PACF", "VAR model", "VECM", "Granger causality", "cointegration", "impulse response function", "forecast", "seasonal decomposition", "ARCH", "GARCH", "时间序列", "平稳性检验", "单位根", "自回归", "格兰杰因果", "协整", "脉冲响应", "预测", "向量自回归"

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Econometrics skill for descriptive statistics and summary tables. Activates when the user asks about: "descriptive statistics", "summary statistics", "summary table", "Table 1", "balance table", "means and standard deviations", "correlation matrix", "data summary", "sample characteristics", "variable distributions", "描述性统计", "描述统计", "汇总统计", "统计表", "均值标准差", "平衡性检验", "相关矩阵", "样本特征", "变量分布"

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Sample-size and statistical power calculations for planning studies. Use whenever someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers closed-form power for t-tests, ANOVA, proportions, correlations, chi-square, and regression, plus simulation-based (Monte Carlo) power for designs with no formula — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, and interactions. Use this skill even when the request only mentions an effect size, alpha, or "80% power" without saying "power analysis" explicitly. For laying out the study (randomization, blocking, factorial/DOE, crossover, sequential designs) use experimental-design; for analyzing data already collected and reporting it use statistical-analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc skills.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Braintrust tracing for Claude Code - hook architecture, sub-agent correlation, debugging

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Investigate fast-agent session and history files to diagnose issues. Use when a session ended unexpectedly, when debugging tool loops, when correlating sub-agent traces with main sessions, or when analyzing conversation flow and timing. Covers session.json metadata, history JSON format, message structure, tool call/result correlation, and common failure patterns.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Statistical analysis toolkit. Hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, Bayesian stats, power analysis, assumption checks, APA reporting, for academic research.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run Megatron-LM (MLM) and Megatron Bridge training with mock or real data. Covers correlation testing, available recipes, and multi-GPU examples.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Fetch financial and market data with the yfinance Python library (Yahoo Finance). Use this skill whenever the user wants stock data: current quotes and price history, financial statements (income statement, balance sheet, cash flow), options chains, dividends and splits, earnings and analyst estimates, price targets and ratings, institutional and insider holdings, news, multi-ticker comparisons, stock screens, or sector and industry data. Use it even when the user gives only a ticker symbol (AAPL, MSFT, TSLA) and the intent has to be inferred. For earnings previews or recaps, estimate revisions, valuation, correlation, liquidity, or ETF premium analysis, prefer the dedicated skill.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create text-driven visuals in Markdown: charts, diagrams, cards, architecture and page layouts. Charts and analytical views (bar, line, pie, heatmap, correlation, regression); reliability and operations (latency, incident, throughput, cycle time, OKR, standup, on-call); product and finance (funnel, retention, revenue, budget); process and workflow (approval, BPMN); software design and behaviour (class, state machine, sequence, C4); dependencies and impact (dependency graph, ER, causality); system architecture as HTML (layer stack, wings, zones, topology, service catalogue, request paths); infrastructure (cloud, Kubernetes, ETL, network, security, IAM, compliance); governance and people (ArchiMate, org chart, hiring); knowledge and planning (mind map, roadmap, Gantt, migration); documents (comparison, SWOT, memo, policy, catalogue, case study). Use when a document needs a diagram, chart or card. Figures render live. Not for slide decks or math notation. Not recommended: mermaid / canvas / drawio / dot.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Quality control and assessment for proteomics data. Use when evaluating proteomics data quality before downstream analysis. Covers sample metrics, missing value patterns, replicate correlation, batch effects, and intensity distributions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Quality metrics for IMC data including signal-to-noise, channel correlation, tissue integrity, and acquisition QC. Use when assessing data quality before analysis or troubleshooting problematic acquisitions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.