'detection' 검색 결과

Adapt a C/C++ vulnerability pipeline to another language, target, or detection method. The workflow gathers requirements, proposes file changes for approval, updates the implementation, and validates it against a test target containing known bugs.

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• Adapt the pipeline for web application vulnerabilities
• Plan a port for smart contract auditing
• Change detection signals and report criteria

Routes security work to specialized guides for web and API testing, code review, threat intelligence, and incident response. Helps organize assessments from both attack and defense perspectives, including coordinated testing of detection capabilities.

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• Planning a web or API security assessment
• Reviewing code for potential vulnerabilities
• Organizing detection rules and incident response

Plan explicitly permitted credential testing, from preparing password candidates and usernames to choosing a login mode. Covers breach-frequency ranking, attempt pacing, success detection, audit logs, and conditions for stopping the test.

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• Assess whether credential testing is permitted and practical
• Prepare target-specific candidates and usernames
• Choose a mode for the login system

Develop literature reviews on AI for medical imaging, including segmentation, detection, and classification. Organize research from multiple sources into a structured manuscript with method comparisons, evaluation metrics, supporting citations, and limitations.

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• Drafting literature reviews on medical imaging AI
• Comparing methods for segmentation, detection, and classification
• Combining literature from ArXiv, PubMed, and Zotero

Detects whether a task concerns software development or research and strategy by examining the request and project files. Workflow skills can use the result to choose agents, output formats, and review criteria suited to the work.

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• Switching between coding and strategy work in one project
• Choosing output formats for research or implementation
• Overriding an incorrect automatic context detection

Configure fault detection and recovery for distributed training in Megatron Bridge. The skill covers slow GPU detection, automatic restarts, job interruption handling, and checkpoint saving, with configuration examples and guidance for investigating hangs or failed recovery.

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• Configuring automatic recovery after training failures
• Detecting slow GPUs in distributed training
• Preparing for scheduled job interruptions

Deploy and operate DOCA Argus Service on BlueField to monitor suspicious activity. This skill guides detection settings, forwarding findings to a security monitoring system, and troubleshooting missing alerts, excessive noise, or performance impact.

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• Deploying runtime security on BlueField
• Forwarding security findings to Splunk
• Investigating missing or excessive alerts

Build DeepStream commands for image and video inference, object detection, tracking, and streaming. The skill asks about missing requirements, assembles a GStreamer processing pipeline, and checks its syntax, components, properties, and connections where supported.

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• Build a pipeline for inference on local images or video
• Detect and track objects across multiple RTSP streams
• Save processed output to an MP4 file

Build video analytics applications with NVIDIA DeepStream and Python's pyservicemaker API. The skill guides video input, object detection, optional tracking, annotated output, and Kafka messaging, with configuration guidance and fixes for common pipeline errors.

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• Detect objects in video files or camera streams
• Track objects across video frames
• Send detection results to Kafka

Analyzes cfDNA methylation patterns for cancer detection using cfMeDIP-seq or bisulfite sequencing with MethylDackel. Identifies cancer-specific methylation signatures and performs tissue-of-origin deconvolution. Use when using methylation biomarkers for early cancer detection or minimal residual disease.

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Builds the capability to notice an attack in progress — deciding what to log and retain, centralizing it somewhere tamper-resistant, writing detections that fire on attacker behavior rather than on individual events, tuning out the noise that trains people to ignore alerts, and defining what happens when something fires. Use this to design or assess monitoring coverage, work out why an incident went unnoticed, cut alert volume without losing signal, or decide what a detection capability should cost.

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Detects somatic mutations in circulating tumor DNA, treating low-VAF detection as a signal-versus-noise problem set by error suppression and molecules sampled, not by the choice of caller. Distinguishes de novo CALLING (scanning a panel for unknown variants, bounded by per-locus error and multiple testing) from tumor-informed DETECTION (tracking a pre-specified variant set, where panel integration reaches single-ppm). Covers VarDict and Mutect2 for de novo calling, UMI-aware callers, and a pysam-based known-variant VAF tracker, with matched-WBC subtraction as the mandatory defense against clonal hematopoiesis (the dominant false positive). Use when calling or tracking tumor mutations from plasma cfDNA, setting a VAF threshold, or deciding whether a low-VAF call is tumor versus CHIP.

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Import raw data (CSV, XLSX, TXT, or MATLAB tables) into formats used by Sensor Fusion and Tracking Toolbox. Handles both ground truth trajectories and sensor detection data. For truth: builds trackingScenarioRecording, tuning timetable, truthlog, or converted table. For sensor data: builds task-oriented dataFormat structs (preferred) or objectDetection arrays (legacy). Use when importing flight logs, GPS logs, radar detections, IR measurements, lidar/camera bounding boxes, ADS-B data, AIS ship tracks, or any recorded data for use with trackers, filter tuning, or tracker evaluation.

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Configure, simulate, debug, and analyze radarDataGenerator within radarScenario. Use for: interactively building radar detection scenarios from datasheets or performance requirements; diagnosing missed detections and configuration errors; interpreting sensor spherical, body, and scenario-frame outputs; deriving ReferenceRange from hardware specs via link budget; scan mode configuration (mechanical, electronic/AESA, hybrid); and validating simulation results against analytical predictions.

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Computer vision engineering for object detection, segmentation, and visual AI, covering CNN and Vision Transformer architectures and ONNX/TensorRT deployment. Use when building detection pipelines, training models, or optimizing inference.

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This skill should be used when the user asks to "build web automation scripts", "check browser automation for detection", "generate web scraping code", "create form filling automation", or "build anti-detection browser scripts".

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Expert blueprint for stealth games (Splinter Cell, Hitman, Dishonored, Thief) covering AI detection systems, vision cones, sound propagation, alert states, light/shadow mechanics, and systemic design. Use when building stealth-action, tactical infiltration, or immersive sim games requiring enemy awareness systems. Keywords vision cone, detection, alert state, sound propagation, light level, systemic AI, gradual detection.

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Expert blueprint for stealth games (Splinter Cell, Hitman, Dishonored, Thief) covering AI detection systems, vision cones, sound propagation, alert states, light/shadow mechanics, and systemic design. Use when building stealth-action, tactical infiltration, or immersive sim games requiring enemy awareness systems. Keywords vision cone, detection, alert state, sound propagation, light level, systemic AI, gradual detection.

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Designs and reviews detections from telemetry prerequisites, correlation logic, Sigma metadata, severity, status, and benign cases. Use for detection rules, Sigma review, analytic coverage, or tuning; not generic logging advice or live deployment.

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Collaborative purple-team operations — threat-informed adversary emulation planning (ATT&CK, CTID, Atomic Red Team, CALDERA), the detect-tune-validate loop, detection coverage measurement (DeTT&CT/Navigator), safe execution and deconfliction, and MTTD/coverage reporting

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Use the open-source free `QuickDup` clone detector for .NET repositories. Use when a repo needs duplicate C# code discovery, structural clone detection, DRY refactoring candidates, or repeatable duplication. USE FOR: the repo wants QuickDup; the team needs repeatable duplicate-code scans for C#; the user asks about DRY cleanup, copy-paste detection, clone detection, or duplicate. DO NOT USE FOR: formatting-only work; repos that intentionally use a different clone detector and do not want overlap. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Audit iOS and Android mobile applications against OWASP MASVS / MASTG — insecure storage, weak crypto, certificate pinning, deeplinks, IPC, jailbreak/root detection, reverse-engineering resistance. Use when the user mentions 'mobile security,' 'iOS security,' 'Android security,' 'mobile audit,' 'mobile pentest,' 'MASVS,' 'MASTG,' 'certificate pinning,' 'jailbreak detection,' 'root detection,' 'deeplink,' 'URL scheme,' 'app transport security,' 'keychain,' 'keystore,' 'mobile reverse engineering,' or has a mobile app to review.

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Use when hunting threats or engineering detections — ATT&CK Detection-Strategies, Sigma + correlation with Detection-as-Code CI, Windows endpoint hunting (Sysmon/ETW/LSASS/LOLBins), network C2 hunting (JA4+, beaconing, DNS tunneling), cloud-identity hunting, Atomic Red Team purple-team validation

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Build automated PII detection and redaction pipelines using spaCy NER, Microsoft Presidio, and AWS Macie integration. Includes confidence scoring, custom entity type definitions, batch processing workflows, and multi-format document scanning for structured and unstructured data sources.

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