「analyzing」の検索結果

Investigate compromised Docker containers by analyzing images, layers, volumes, logs, and runtime artifacts to identify malicious activity and evidence.

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。

Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly detection.

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特許分析・知的財産戦略支援スキル。先行技術調査、特許性評価、特許ポートフォリオ分析、 侵害リスク評価、ライセンス戦略策定を支援。特許出願戦略の立案から権利化まで包括的にサポート。 Use when conducting prior art searches, evaluating patentability, analyzing patent portfolios, assessing infringement risks, or developing IP strategies. Triggers: "patent search", "prior art", "特許調査", "patentability", "FTO", "特許ポートフォリオ", "IP strategy", "patent landscape".

システムログを分析してエラーの根本原因を特定し、段階的に深堀りしていくデバッグ専門家スキル。 アプリケーションログ、システムログ、クラウドサービスログなど様々な形式に対応。 5 Whys、タイムライン分析、Fishbone分析などのRCA（根本原因分析）手法を用いて 問題の本質を突き止め、再発防止策まで提案する。 Use when analyzing system logs to find error root causes, debugging application issues, or performing technical post-mortem analysis with log data. For organizational incident management processes (post-incident review, corrective action plans, incident reports without log data), use incident-rca-specialist instead. Triggers: "analyze this log", "find the root cause", "debug this error", "why is this failing", "log analysis", "what caused this crash", "troubleshoot this issue"

DuckDBを使用した大規模データ分析の専門スキル。DuckDBのアーキテクチャ、SQL構文、パフォーマンス最適化、各種ファイル形式（CSV、Parquet、JSON等）の効率的な読み込み・書き出しを熟知。メモリ効率の良い分析、複雑なクエリの最適化、データパイプライン構築を支援。Use when analyzing large datasets, querying CSV/Parquet/JSON files directly, building data pipelines, or optimizing analytical queries with DuckDB.

エラーや例外シナリオを顧客体験(CX)の観点で体系的に分析し、改善優先度を付ける スキル。6軸評価（影響度・頻度・復旧容易性・メッセージ品質・感情的影響・ ビジネスコスト）でスコアリングし、Impact vs Effort マトリクスで改善施策を 優先順位付けする。Use when analyzing error scenarios from customer experience perspective, evaluating error message quality, prioritizing error UX improvements, or creating CX-focused error analysis reports. Triggers: "CXエラー分析", "error experience", "エラーUX改善", "customer experience error", "エラーメッセージ品質", "error scenario analysis", "顧客体験エラー評価"

Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why.

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Use when working with Snyk — snyk security scanning and vulnerability analysis. Covers dependency vulnerability scanning, container image scanning, IaC security scanning, code analysis, license compliance, and fix recommendations. Use when scanning for vulnerabilities, analyzing dependency risks, reviewing container security, or auditing IaC configurations.

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Use when working with Aqua — aqua Security platform analysis. Covers container runtime protection, image assurance policies, compliance frameworks, vulnerability management, workload protection, and registry scanning. Use when analyzing container security posture, reviewing image compliance, investigating runtime alerts, or auditing security policies.

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Use when working with Kubescape — kubescape Kubernetes security posture analysis. Covers NSA/CISA framework assessment, MITRE ATT&CK mapping, CIS Kubernetes benchmarks, workload scanning, RBAC analysis, and network policy evaluation. Use when assessing Kubernetes cluster security, evaluating compliance frameworks, or analyzing workload configurations.

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。

Scans .NET code for ~50 performance anti-patterns across async, memory, strings, collections, LINQ, regex, serialization, and I/O with tiered severity classification. Use when analyzing .NET code for optimization opportunities, reviewing hot paths, or auditing allocation-heavy patterns.

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Parses Kubernetes API server audit logs (JSON lines) to tespit etme
exec-into-pod, secret access, RBAC modifications, privileged pod creation, and anonymous API access. Builds threat Tespit rules from audit event patterns. Use investigating yaparken Kubernetes cluster compromise or building k8s-specific SIEM Tespit rules.

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Analyze the threat landscape using MISP (Malware Information Sharing Platform) by querying event statistics, attribute distributions, threat actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data, compute IOC type breakdowns, identify top threat actors and malware families, and generate threat landscape reports with temporal trends.

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Parse Apache and Nginx access logs to detect SQL injection attempts, local file inclusion, directory traversal, web scanner fingerprints, and brute-force patterns. Uses regex-based pattern matching against OWASP attack signatures, GeoIP enrichment for source attribution, and statistical anomaly detection for request frequency and response size outliers.

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Queries Certificate Transparency logs via crt.sh and pycrtsh to detect phishing domains, unauthorized certificate issuance, and shadow IT. Monitors newly issued certificates for typosquatting and brand impersonation using Levenshtein distance. Use for proactive phishing domain detection and certificate monitoring.

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Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns. Uses the Python netflow library to decode flow records, builds traffic baselines, and applies statistical analysis to identify flows with abnormal byte counts, connection durations, and periodic timing patterns.

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。

Parse Windows PowerShell Script Block Logs (Event ID 4104) from EVTX files to detect obfuscated commands, encoded payloads, and living-off-the-land techniques. Uses python-evtx to extract and reconstruct multi-block scripts, applies entropy analysis and pattern matching for Base64-encoded commands, Invoke-Expression abuse, download cradles, and AMSI bypass attempts.

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。

Parses Kubernetes API server audit logs (JSON lines) to detect exec-into-pod, secret access, RBAC modifications, privileged pod creation, and anonymous API access. Builds threat detection rules from audit event patterns. Use when investigating Kubernetes cluster compromise or building k8s-specific SIEM detection rules.

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。

Detect sandbox evasion techniques in malware samples by analyzing timing checks, VM artifact queries, user interaction detection, and sleep inflation patterns from Cuckoo/AnyRun behavioral reports

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URLScan.io is a free service for scanning and analyzing suspicious URLs. It captures screenshots, DOM content, HTTP transactions, JavaScript behavior, and network connections of web pages in an isolat

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。

Investigate compromised Docker containers by analyzing images, layers, volumes, logs, and runtime artifacts to identify malicious activity and evidence.

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。

Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux systems.

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Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or building cloud SIEM detections.

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Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.

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。