「monitoring」の検索結果

Multi-agent task orchestration pack. DAG-based parallel task execution and team status monitoring. 2 orchestration tools.

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。

Design KPI dashboards for executive monitoring. Use for performance tracking, strategic initiatives, and management reporting.

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。

Poll RSS, JSON APIs, and GitHub with watermark dedup.

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。

Install, configure, and manage 1-SEC — an open-source, all-in-one cybersecurity platform (16 modules, single binary) on Linux servers and VPS instances. Use when the user asks to secure a server, install security monitoring, set up intrusion detection, harden a VPS, protect an AI agent host, or deploy endpoint defense. Covers installation, setup, enforcement presets, module configuration, alert management, and ongoing security operations.

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。

Optional vendor skill for Parallel CLI — agent-native web search, extraction, deep research, enrichment, FindAll, and monitoring. Prefer JSON output and non-interactive flows.

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Public-records OSINT investigation framework — SEC EDGAR filings, USAspending contracts, Senate lobbying, OFAC sanctions, ICIJ offshore leaks, NYC property records (ACRIS), OpenCorporates registries, CourtListener court records, Wayback Machine archives, Wikipedia + Wikidata, GDELT news monitoring. Entity resolution across sources, cross-link analysis, timing correlation, evidence chains. Python stdlib only.

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Inspect, benchmark, and monitor Web3 RPC endpoints — latency checks, method support, chain detection, and health scoring.

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Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.

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You are an SLO (Service Level Objective) expert specializing in implementing reliability standards and error budget-based engineering practices. Design comprehensive SLO frameworks, establish meaningful SLIs, and create monitoring systems that balance reliability with feature velocity.

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Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.

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。

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.

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Migration monitoring, CDC, and observability infrastructure

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Expert database optimizer specializing in modern performance tuning, query optimization, and scalable architectures. Masters advanced indexing, N+1 resolution, multi-tier caching, partitioning strategies, and cloud database optimization. Handles complex query analysis, migration strategies, and performance monitoring. Use PROACTIVELY for database optimization, performance issues, or scalability challenges.

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Expert DevOps troubleshooter specializing in rapid incident response, advanced debugging, and modern observability. Masters log analysis, distributed tracing, Kubernetes debugging, performance optimization, and root cause analysis. Handles production outages, system reliability, and preventive monitoring. Use PROACTIVELY for debugging, incident response, or system troubleshooting.

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You are an error tracking and observability expert specializing in implementing comprehensive error monitoring solutions. Set up error tracking systems, configure alerts, implement structured logging, and ensure teams can quickly identify and resolve production issues.

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Detect OS credential dumping techniques targeting LSASS memory, SAM database, NTDS.dit, and cached credentials using EDR telemetry, Sysmon process access monitoring, and Windows security event correlation.

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Detect abuse of elevation control mechanisms including UAC bypass, sudo exploitation, and setuid/setgid manipulation by monitoring registry modifications, process elevation flags, and unusual parent-child process relationships.

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Analyzes and simulates BGP hijacking scenarios in authorized lab environments to assess route origin validation, RPKI deployment, and BGP monitoring defenses against prefix hijacking and route leak attacks on internet routing infrastructure.

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This skill covers detecting sophisticated cyber-physical attacks that follow the Stuxnet attack pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation from operators. It addresses PLC logic integrity monitoring, physics-based process anomaly detection, engineering workstation compromise indicators, USB-borne attack vectors, and multi-stage attack chain detection spanning IT-to-OT lateral movement through to process manipulation.

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Detect container escape attempts in real-time using Falco runtime security rules that monitor syscalls, file access, and privilege escalation.

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This skill covers detecting anomalies in Modbus/TCP and Modbus RTU communications in industrial control systems. It addresses function code monitoring, register range validation, timing analysis, unauthorized client detection, and deep packet inspection for malformed Modbus frames. The skill leverages Zeek with Modbus protocol analyzers, Suricata IDS with OT rules, and custom Python-based detection using Markov chain models for normal Modbus transaction sequences.

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Detect DCSync attacks where adversaries abuse Active Directory replication privileges to extract password hashes by monitoring for non-domain-controller accounts requesting directory replication via DsGetNCChanges.

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Detect Living Off the Land Binaries (LOLBins/LOLBAS) abuse including certutil, regsvr32, mshta, and rundll32 via process telemetry, Sigma rules, and parent-child process analysis

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Detect abuse of legitimate Windows binaries (LOLBins) used for living off the land attacks. Monitors process creation, command-line arguments, and parent-child relationships to identify suspicious LOLBin execution 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。