“correlation”的搜索结果

Investigate lateral movement, where attackers expand access across Azure identities and tenants. Correlate audit logs and unusual sign-ins, build Microsoft Sentinel detection queries, and report affected identities with recommended containment actions.

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• Investigating suspicious Azure sign-ins
• Building Microsoft Sentinel detection rules
• Reviewing suspicious OAuth app consent

Examine Windows and Active Directory logs for attempts to extract passwords and other credentials. Identify suspicious memory access and database exports, correlate events with user and host context, and produce findings with severity ratings and detection queries.

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• Investigating alerts about suspicious LSASS access
• Finding signs of SAM or NTDS.dit extraction
• Creating detection queries for Splunk or Elastic

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.

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

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.

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

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.

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

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.

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

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.

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• 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.

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• 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.

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• 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.

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• 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.

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• 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.

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

Plan, run, and interpret linear regression with guidance on model choices, diagnostic checks, and reporting. Python, R, and Stata examples help examine coefficients, assess assumptions, and choose standard errors that account for different error patterns.

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• Specify a regression model for an empirical study
• Check residuals and correlations among explanatory variables
• Choose robust or clustered standard errors

Guide economic time series analysis from initial plots to forecasting. Use Python, R, and Stata examples to check whether statistical properties remain stable over time, choose models, diagnose errors, and examine relationships between multiple series.

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• Checking trends and stationarity in economic data
• Building ARIMA forecasts and evaluating accuracy
• Examining relationships between multiple time series

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.

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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.

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

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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.

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

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

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

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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.

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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.

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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.

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