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Analyze Modbus industrial network traffic to identify unauthorized commands, write operations, and unusual polling intervals. Compare activity with normal traffic patterns and allowed operations, then report anomalies by type and severity.
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Analyze AWS CloudTrail logs against normal activity patterns to spot unusual API calls, IP addresses, errors, and sensitive changes. Produce a JSON report with anomaly scores, suspicious users, and suggested investigation steps.
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Build normal traffic profiles for industrial control networks and identify unusual connections, polling intervals, and protocol operations. Use machine learning and process records to investigate deviations and produce anomaly reports with severity levels.
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Measures and interprets what a fuzzing campaign actually reaches, using llvm-cov, lcov, or a fuzzer's own coverage output. Covers baselining a new campaign, reading coverage reports, and turning uncovered regions into harness, seed, or dictionary work. Use when a fuzzer plateaus, when judging whether a harness is effective, after changing a harness, or when asking why some code is never reached.
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Ensures projects deliver the value they were approved on — defining measurable benefits, baselining, tracking after delivery, and honest post-implementation review. Use this to define benefits for a business case, set a baseline, track whether value actually landed, or run a post-implementation review that produces something useful.
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Set up and run rank tracking using Ahrefs MCP: pick the right keywords to track, segment them by purpose, set baselines, and define alert thresholds. Use this skill when starting a new tracking project, baselining for a campaign, choosing what to monitor, or building a rank reporting cadence. Triggers on rank tracking, keyword tracking, monitor rankings, track positions, what should we track, ranking dashboard, baseline rankings, alert thresholds. Also triggers when a stakeholder wants weekly or monthly ranking visibility.
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Hunts for Windows persistence across autostart locations: Run keys, services, scheduled tasks, WMI event subscriptions, and startup folders, baselining them to surface anomalous or recently added entries. Activates for requests to hunt persistence, audit autostart extensibility points, or find malware autoruns on Windows.
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Establishes behavioral baselines from historical telemetry (process, network, or logon events) so hunts can flag rare and first-seen activity instead of relying on static signatures. Activates for requests to build a telemetry baseline, find rare or first-seen activity, or compute frequency baselines for anomaly hunting.
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Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.
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Knowledge base from the NASA Schedule Management Handbook (2024 edition, Revision 2). Use for program/project schedule management of aerospace and large engineering efforts: the five sub-functions (Planning, Development, Assessment & Analysis, Maintenance & Control, Documentation & Communication); the Integrated Master Schedule (IMS) and the WBS/OBS/CBS breakdown structures; the Schedule Management Plan (SMP) and Basis of Estimate (BoE); Critical Path Method, logic, durations, float and margin; tiered schedule assessment (Requirements/Health/Basis checks, the Shock Test) and the Dimensions of Schedule Reliability; Schedule Risk Analysis (SRA), Integrated Cost-Schedule Risk Analysis (ICSRA), Monte Carlo, correlation, merge bias, and the Joint Confidence Level (JCL); baselining, replan vs. rebaseline, EVM schedule metrics (SPI, BEI, CEI, HMI, Earned Schedule, CPLI), and status/progress/forecast reporting. This is NASA's scheduling doctrine grounded in NPR 7120.5 and the GAO Schedule Assessment Guide. Thin on the underlying mechanics of CPM, statistics, and EVM (it points to companion handbooks); aerospace/space-flight-centric; anchored to the 2024 second edition.
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Establish build performance baselines and apply systematic optimization techniques. Only activate in MSBuild/.NET build context. USE FOR: diagnosing slow builds, establishing before/after measurements (cold, warm, no-op scenarios), applying optimization strategies like MSBuild Server, static graph builds, artifacts output, and dependency graph trimming. Start here before diving into build-perf-diagnostics, incremental-build, or build-parallelism. DO NOT USE FOR: non-MSBuild build systems, detailed bottleneck analysis (use build-perf-diagnostics after baselining).
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tespit etmeunusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.
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Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.
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Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.
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