「correlation」の検索結果

Risk Assessment & Position Sizing — analyzes volatility, drawdown, correlation, liquidity, smart contract, regulatory, concentration, and unlock risks with crypto-specific position sizing calculators and a composite Risk Score (0-100, higher = safer)

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for DFIR chronology, cross-artifact correlation, persistence chains, and incident timeline reconstruction. Use when the user asks to build a forensic timeline, correlate EVTX, PCAP, registry, disk, memory, mailbox, or browser artifacts, explain the order of attacker actions, or pinpoint the stage where the decisive artifact appears. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for packet capture analysis, session reconstruction, application-protocol decoding, stream reassembly, beacon timing, and packet-to-process correlation. Use when the user asks to analyze a PCAP, rebuild TCP or UDP sessions, decode HTTP, WebSocket, DNS, custom C2, or binary protocols, extract transferred artifacts, or tie packet sequences to host or malware behavior. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

Triages AWS CloudWatch alarms using the CloudWatch DescribeAlarms API, GetMetricData for historical analysis, and CloudTrail LookupEvents for root cause correlation. Prioritizes alerts by blast radius and provides remediation playbooks.

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。

Evaluates rating agency methodology application with loss model inputs, correlation assumptions, and tranche-level credit assessment. Use when analyzing structured product ratings, comparing agency methodologies, or assessing rating sensitivity.

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 market regime signals with volatility clustering, correlation dynamics, and liquidity condition assessment. Use when analyzing market regimes, detecting regime shifts, or adjusting strategy for market conditions.

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。

Structured debug log analysis for Claude Code sessions — auto-discovers most recent log, runs reducer, extracts error patterns, correlates with full log, produces observability report. Fills 5 identified gaps: hook error body capture, agent identity, file path tracking, stall correlation, success visibility.

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 analyzing published content (blog posts, social media, newsletters) for sentiment, structural quality, hook effectiveness, readability, topic classification, and engagement correlation. Invoke for daily post analysis, content audits, or engagement driver identification.

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。

Systematically interpret nuclear magnetic resonance spectra (1H, 13C, DEPT, and 2D experiments) to elucidate molecular structure. Covers chemical shift assignment, coupling pattern analysis, integration, and correlation of multi-dimensional data into coherent structural proposals.

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。

Set up centralized log aggregation with Loki and Promtail (or ELK stack), including log parsing, label extraction, retention policies, and integration with metrics for correlation. Use when consolidating logs from multiple services into a searchable system, replacing local log files with centralized queryable storage, correlating logs with metrics and traces, implementing structured logging with label extraction, or troubleshooting production incidents requiring cross-service log analysis.

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。

Unify metrics, logs, and traces for cohesive debugging. Implement exemplars for log-to-trace linking, build unified dashboards using RED/USE methods, and enable rapid root cause analysis across observability signals. Use when investigating complex incidents spanning multiple systems, reducing mean time to resolution, implementing distributed tracing, or moving from siloed tools to a unified observability platform.

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。

Portfolio Analyzer — sector allocation analysis vs SPY benchmark, geographic exposure, correlation matrix, concentration risk (HHI), beta-weighted portfolio delta, dividend/income analysis, and prioritized rebalancing recommendations. Triggered by "trade portfolio" or "analyze my portfolio".

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。

Risk Assessment & Position Sizing — volatility profile (beta/ATR/HV), historical drawdowns, correlation analysis, liquidity risk, position sizing tables (fixed %, volatility-adjusted, Kelly Criterion), and VaR estimates. Returns Risk Score (0-100, higher = safer). Triggered by "trade risk <TICKER>".

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。

Expert error coordinator specializing in distributed error handling, failure recovery, and system resilience. Masters error correlation, cascade prevention, and automated recovery strategies across multi-agent systems with focus on minimizing impact and learning from failures.

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。

銘柄間・クロスアセット相関分析。個別株同士、個別株と指数、セクター間の相関係数と回帰分析を行い、分散投資やヘッジの判断材料を提供する。「相関」「連動性」「β（ベータ）」「分散投資」「ヘッジ」「回帰分析」と聞かれたときに使用。

"Apply Hierarchical Linear Modeling (HLM) to analyze nested data structures with random intercepts and slopes, accounting for intra-class correlation and cross-level interactions. Use this skill when the user has students nested in schools, employees in firms, or repeated measures in individuals, needs to partition variance across levels, or when they ask 'how do I handle nested data', 'what is ICC', or 'do group-level factors moderate individual-level relationships'.".

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。

Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.

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。

Pino logging, Sentry error tracking, Axiom - structured logging with correlation IDs, error boundaries, performance monitoring, alerting

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。

Pivots on indicators of compromise across multiple data sources by correlating a seed set of IOCs against logs to find co-occurring indicators, hosts, and timeframes that expand the investigation. Activates for requests to pivot on IOCs, correlate indicators across data sources, or expand an investigation from a seed indicator set.

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。

Apply source-aware mechanical-engineering judgment to research, analysis, coding, writing, teaching, research identity, and release work. Use for thermal-fluid systems, heat transfer, fluid mechanics, thermodynamics, HVAC, energy systems, turbomachinery, piping, multiphase flow, experiments, correlations, CFD, reduced-order models, AI/ML, uncertainty, engineering datasets, literature reviews, citations, manuscripts, reviewer revisions, Overleaf packages, figures, proposals, research software, reproducibility, public releases, engineering teaching materials, or a research-project logo and visual identity.

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。

Analyze thermal-fluid engineering systems, experiments, CFD, and correlations with explicit assumptions, validity limits, uncertainty, and mechanism-based interpretation. Use for heat transfer, fluid mechanics, phase change, HVAC, energy systems, turbomachinery, piping, or thermal management.

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。

Plan and execute integrative analysis combining multiple ENCODE experiments for cross-dataset or multi-omic workflows. Use when the user wants to combine experiments, perform cross-dataset comparison, multi-omic integration, peak overlap analysis, differential binding, signal correlation, chromatin state segmentation, enhancer-gene linkage, or any analysis that requires merging or comparing data from two or more ENCODE experiments. Covers same-assay cross-sample, multi-omic same-sample, cross-organism, and perturbation integration designs. Guides compatibility checks, batch effect detection, normalization, integration strategy selection, and provenance documentation.

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。

Estimate causal effects from observational and experimental data — DAGs, confounding control, quasi-experimental designs, and sensitivity analysis. Use when you need "what causes what," not just correlation.

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 SIEM alert pipelines for rule optimization, alert fatigue reduction, criticality scoring, asset-based prioritization, and correlation rule design using NIST CSF and detection engineering principles..

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。