'logs' 검색 결과

Runs SQL queries on CloudWatch Logs data exported as Apache Iceberg tables in S3 Tables. Covers VPC Flow Logs, WAF logs, CloudFront access logs, Route 53 resolver logs, Network Firewall logs, EKS audit logs, Verified Access logs, SES logs, VPC Lattice logs, Step Functions logs, NLB access logs, and 20+ other AWS vended data sources. Applies when analyzing network traffic, investigating security incidents, querying exported logs with SQL, enabling S3 Tables integration, configuring log export, correlating logs with other data, or running Athena queries on the aws-cloudwatch table bucket. Trigger phrases: query logs with SQL, analyze logs in Athena, SQL on VPC flow logs, investigate network traffic, run SQL on exported logs, enable S3 Tables for CloudWatch, correlate logs, historical log analysis, set up log querying.

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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

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.

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

Respond to security incidents across AWS, Azure, and GCP by investigating audit logs, containing compromised identities, isolating resources, and preserving evidence. Organize recovery and follow-up security measures for credential leaks, unauthorized permission changes, and attacks spanning cloud services.

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• Responding to leaked cloud credentials
• Investigating unauthorized permission or resource changes
• Preserving audit logs and disk snapshots

Search and analyze Estuary task logs with jq filtering. Use when investigating task failures, debugging connector errors, analyzing behavior patterns, or streaming live logs. Use when user says "check logs", "show me logs", "task failing", "why is my capture failing", "why is my materialization failing", "error messages", "why did it fail", "search task logs", "shard failed", "follow logs", "increase log level", "debug logging", "is this log message normal", "what does this message mean", "connection timeout", or "i/o timeout".

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Review AI call logs by provider, model, time range, status, and cost. Aggregate token usage, export logs, configure budgets, and inspect response times and prompt cache statistics to understand usage and performance.

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• Investigate failed calls in filtered logs
• Aggregate token usage and costs across connections
• Configure budgets and token limits

Analyze Apache and Nginx access logs for attack attempts, scanner activity, and repeated login requests. Add geographic context to source IP addresses and produce a prioritized findings report to support intrusion investigations and monitoring reviews.

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• Investigating suspicious requests in web access logs
• Finding injection attempts and unauthorized file access attempts
• Identifying scanners and repeated login attempts

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

Collect and review SGLang startup logs to identify unnecessary output and trace its source. Get saved logs and specific cleanup proposals while preserving useful diagnostics, with runtime changes left for user approval.

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• Investigating repeated messages in startup logs
• Checking which startup warnings need attention
• Comparing startup logs across models and GPU counts

Set up Zeek to observe network traffic and produce structured logs for security analysis. Create custom detection scripts, investigate suspicious DNS activity and repeated external connections, and forward logs to a central security monitoring system.

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• Set up passive monitoring at key network points
• Create detection scripts for suspicious DNS and repeated connections
• Forward Zeek logs to a central security monitoring system

Analyze ModSecurity, AWS WAF, and Cloudflare logs for SQL injection, attempts to manipulate database queries through web requests. Groups related requests, tracks source IPs, classifies attack methods, and produces incident reports.

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• Investigating suspected SQL injection attempts
• Tracking repeated attacks from the same source
• Connecting requests across an attack campaign

Investigate security incidents in Splunk by connecting authentication, endpoint, and network logs. Use SPL, Splunk’s search language, to identify suspicious activity, reconstruct event timelines, document findings, and develop detection rules.

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• Investigating suspicious logins across multiple log sources
• Reconstructing an incident timeline
• Tracing suspicious processes and outbound traffic

Investigate suspicious Azure activity by querying administrative and sign-in logs. Create KQL queries, Azure's log search language, to examine role assignments, resource changes, unusual access, and other signals for cloud security monitoring.

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• Investigate suspicious administrative activity in Azure
• Check newly added administrator roles
• Review resource and network security rule changes

Configure Google Cloud logs across multiple projects, either by storing them centrally or querying them together where they already reside. The skill guides routing, access permissions, and log views, and helps diagnose missing logs and permission failures.

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• Collect logs from multiple projects in one central bucket
• Query distributed logs without copying their data
• Set log access boundaries by project or log type

Record and query iii engine logs, traces that show how calls flow, and operational metrics. Set up reactions to incoming errors, inspect alerts, and review metrics aggregated over time windows.

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• Investigating errors through related logs and traces
• Triggering notifications when error logs arrive
• Reviewing metrics aggregated over time

Search and aggregate Datadog logs through the Composio CLI. Filter by service, environment, and time range, follow requests across services, and export structured JSON for incident analysis or scheduled daily digests.

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• Investigate sudden increases in errors or latency
• Compare logs around a deployment
• Follow one request across multiple services

Hunt for signs of compromise in web and application logs without an initial alert. Establish normal activity, record investigative hypotheses, check source code, and reproduce issues locally before producing incident records and a report.

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• Hunting for compromise without an initial alert
• Tracing related activity across application logs
• Distinguishing attack attempts from successful exploitation

Read from a UModel object-graph semantic layer with the `umctl` CLI (MCP alternative noted). Three kinds of read: (1) entities & relationships / topology (`.entity`, `.topo`) and (2) the model itself (`.umodel`, and `.entity_set` methods, including entity-linked `list_skills`) return real rows; (3) metrics & logs (`get_metrics` / `get_logs`) return an executable *plan* — PromQL / Elasticsearch DSL with the entity id pre-substituted — that you run against the backend. Against a PaaS endpoint the same calls return data rows instead of a plan. Use to query or read UModel entities, relations, topology, or model metadata; to read a service's metrics or logs; to look up services and dependencies; or to discover what objects, datasets, and methods exist. For root-cause analysis on top of these reads, see the `umodel-rca` skill. Triggers: UModel, object graph, .entity / .topo / .umodel / .entity_set, query entities, read topology, read metrics / logs, get_metrics / get_logs, list services / dependencies / datasets / skills, 实体查询, 关系/拓扑查询, 读模型, 读指标, 读日志, 查指标, 查日志, 查服务依赖.

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VictoriaLogs provider for obz. Supports log search using LogsQL, a purpose-built query language with _msg, _time, and _stream filters plus pipes for transformation. This skill should be used when the user mentions "VictoriaLogs", "LogsQL", "obz log search -p vl", or needs to search logs from a VL backend.

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Use when implementing PyQt/PySide6 dialogs - standard dialogs (QFileDialog, QMessageBox, QInputDialog, QColorDialog, QFontDialog), custom QDialog patterns, modal vs modeless dialogs, input validation, Qt 6 dialog APIs

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블로그와 RSS/Atom 피드를 등록하고 업데이트를 검색해 새 글을 찾습니다. 글 목록 확인, 읽음 상태 관리, 구독 대상 추가와 삭제를 지원합니다.

• 즐겨 읽는 블로그의 새 글 확인
• 여러 RSS/Atom 피드 추적
• 확인한 글을 읽음으로 표시

Review dialogs to check whether screen readers can announce their purpose when they open. Identify missing or unclear labels, suggest title or label associations, and explain how to verify the changes with accessibility tools.

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• Checking names on modal dialogs
• Reviewing labels on alert dialogs
• Auditing shared dialog components

Inspect NSLogger logs from iOS and macOS apps, filter messages by severity, tag, text, or thread, summarize counts, export text, JSON, or CSV, and receive live logs for debugging.

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• Review errors in a captured app session
• Find network failures by tag and message text
• Export logs as JSON or CSV for analysis

Convert Simplicité text logs into structured JSON while keeping only the fields needed for troubleshooting. Preserve multiline errors and stack traces, and reduce the amount of log content passed to an AI agent for analysis.

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• Preparing large logs for AI analysis
• Extracting timestamps, severity levels, and messages
• Preserving multiline errors during conversion