「indicators」の検索結果

Statically analyzes Linux ELF malware: ELF header and sections, dynamic symbols and imports, segment permissions, embedded strings, and packing indicators to infer capability without execution. Activates for requests to analyze an ELF binary, Linux malware, or shared object.

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Track, categorize, and prioritize technical debt across the codebase. Scans for debt indicators, maintains a debt register, and recommends repayment scheduling.

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Malware reverse engineering and suspicious artifact analysis skill for defensive triage, static analysis, dynamic analysis planning, unpacking strategy, indicators of compromise, behavior summaries, YARA/Sigma ideas, and remediation guidance. Use for suspicious binaries, scripts, documents, logs, memory artifacts, sandbox reports, and malware family analysis in isolated environments.

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Spring Boot 4 observability with Actuator, Micrometer, and OpenTelemetry. Use when configuring health indicators, health checks, custom metrics, distributed tracing, production endpoint exposure, Kubernetes probes, Cloud Run probes, logging, or monitoring. Covers Actuator endpoint security, Micrometer Timer/Counter/Gauge patterns, OpenTelemetry span customization, liveness/readiness probe setup, and metric tag best practices.

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Comprehensive K-12 or higher-ed system analysis: predict student dropout risk with early warning indicators, optimize curriculum alignment and pacing, personalize learning paths for at-risk and diverse populations, and audit school operations for resource efficiency..

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Audit rehabilitation setback prediction systems for clinical risk modeling and early intervention. Triggers: you need to evaluate risk factor models (Charlson, Elixhauser), early warning indicators for functional decline.

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Audit a student information system for dropout risk prediction. Analyzes attendance patterns, grade trajectories, behavioral indicators, early warning model accuracy, intervention tracking, equity in risk scoring, and socioeconomic factor integration..

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Analyze flow metrics to identify delivery bottlenecks and improve throughput. Use when the user says "flow metrics", "why is delivery slow", "cycle time analysis", "WIP analysis", "throughput review", "what's our lead time", "DORA metrics", "where is work getting stuck", "Kanban metrics", or wants to diagnose delivery performance using data - even if they don't explicitly say "flow metrics review". Based on "Making Work Visible" by Dominica DeGrandis (WIP limits, flow blockers, time thieves) and "Accelerate" by Forsgren, Humble & Kim (DORA metrics, delivery performance indicators).

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Expert guidance for proactive threat hunting in Google SecOps. Use when proactively hunting for threats, retroactively analyzing indicators of compromise (IoCs), performing prevalence searches across enterprise events, hunting for MITRE ATT&CK techniques, or detecting behavioral and statistical outliers using UDM queries. Don't use for incoming alert triage (use secops-triage), active incident response and timeline deep-dives on a known breach (use secops-investigate), or detection rule authoring (use secops-detection-engineering).

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Configuring or troubleshooting the Nonprofit Success Pack Program Management Module (PMM) - program delivery, cohort setup, service scheduling, service delivery recording, and attendance tracking for beneficiaries. Trigger keywords: program management module, PMM, Program__c, ProgramEngagement__c, ServiceDelivery__c, cohort setup, bulk service delivery, service schedule, service participant. NOT for designing which outcomes and indicators to measure - use admin/program-outcome-tracking-design. NOT for NPSP donor, household and giving-rollup data - use data/nonprofit-data-architecture.

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Audit designs, color palettes, and UI components for WCAG 2.1 accessibility compliance, providing specific fixes for contrast, touch targets, focus indicators, and screen reader support. NOT for color harmony aesthetics or design system creation.

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Expert in making CLI tools visually stunning and delightful — not just functional. Covers ANSI color systems (16/256/truecolor with graceful degradation), Unicode box drawing, progress indicators, tables, animated output, ASCII art, rich text, interactive prompts, and full TUI frameworks (Ink, Bubble Tea, Ratatui, Rich, Clack). Activate on: "make this CLI beautiful", "terminal UI", "CLI design", "pretty output", "progress bar", "spinner", "CLI table", "terminal colors", "TUI framework", "interactive prompts", "command line aesthetics", "CLI polish", "terminal styling". NOT for: web UI design (use web-design-expert), GUI desktop apps (use rust-tauri-development), API design (use api-architect), general shell scripting (use devops-expert).

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Load this skill for every project containing interactive UI elements — buttons, links, modals, dropdowns, sliders, tabs, carousels, or any custom widget. Under no circumstances create an interactive component that cannot be fully operated by keyboard alone. Absolutely always ensure visible focus indicators, logical tab order, and no keyboard traps. Apply these rules to every interactive element without exception.

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Load this skill whenever the project contains text, UI components, icons, form controls, data visualisations, or focus indicators — in short, almost every project. Under no circumstances hard-code colour values without verifying contrast ratios. Absolutely always ensure text meets 4.5:1, large text meets 3:1, and non-text UI elements meet 3:1 against adjacent colours. Test in light mode, dark mode, and forced-colors (high contrast) mode.

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Load this skill whenever the project contains dynamic content updates, status messages, alerts, notifications, loading indicators, or any use of aria-live, role="status", role="alert", or role="log". Under no circumstances add or modify live-region markup without applying these rules. Prioritize correct politeness levels and avoid redundant announcements. Never use a fixed-delay clear-and-reinsert workaround — no standard guarantees it works.

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Use when designing or implementing feedback, status, progress, alerts, errors, success states, and live activity indicators in Apple-platform apps — inline status, banners, alerts, notifications, and loading states.

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Measure and audit C/C++ code size, namespace distribution, function complexity, nesting, recurrence, and namespace coupling in the EMULATORS framework. Use when Codex needs codebase statistics, complexity baselines, namespace comparisons, duplication indicators, hotspots, architectural growth analysis, or before/after complexity reports while excluding third-party code.

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Detect Mimikatz execution through command-line patterns, LSASS access signatures, binary indicators, and in-memory detection of known modules.

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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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Detects and analyzes malicious behavior in mobile applications through behavioral analysis, permission abuse detection, network traffic monitoring, and dynamic instrumentation. Use when analyzing suspicious mobile applications for data exfiltration, command-and-control communication, credential stealing, SMS interception, or other malware indicators. Activates for requests involving mobile malware analysis, app behavior monitoring, trojan detection, or suspicious app investigation.

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Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.

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Detect process hollowing (T1055.012) by analyzing memory-mapped sections, hollowed process indicators, and parent-child process anomalies in EDR telemetry.

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Detect insider threat behavioral indicators including unusual data access, off-hours activity, mass file downloads, privilege abuse, and resignation-correlated data theft.

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Detecting compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible travel patterns, unauthorized resource provisioning, and credential abuse indicators using GuardDuty, Defender for Identity, and SCC Event Threat Detection.

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