Results for “detection”

Coordinates multi-agent swarms with shared spatial memory, task management, conflict detection, and destination navigation via an Eywa room.

Proactively hunt for threats by analyzing IOCs, detecting behavioral anomalies in telemetry, and prioritizing signals mapped to MITRE ATT&CK.

Automate Fraudlabs Pro fraud detection operations through Composio's toolkit via Rube MCP, including tool discovery, connection management, and execution.

Runs time series machine learning tasks—classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search—using the scikit-learn compatible aeon toolkit.

Analyze images using Azure AI Vision SDK for Java, enabling captioning, OCR, object detection, tagging, and smart cropping.

Train, evaluate, export, prune, and run inference for PointPillars 3D object detection models from LiDAR point clouds using NVIDIA TAO.

Monitor dividend portfolios for risk triggers and route anomalies into a human review queue without auto-selling.

Runs Semgrep static analysis with custom YAML rules, supporting taint tracking, metavariable comparisons, and pattern-not-inside exclusions for precise vulnerability detection.

Executes GCP CLI commands with credential injection and enforces anti-hallucination rules for billing data analysis, including net cost calculation, currency detection, and anomaly detection.

N+1 Query Detection and Resolution

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.

Configure fault tolerance, straggler detection, preemption, in-process restart, and re-run state machine for Megatron Bridge training jobs.

Blocks Claude from finishing a session until deterministic quality checks pass: learning library freshness, disk space, and rationalization pattern detection.

Detonate granular AWS, Azure, GCP, and Kubernetes attack techniques to validate detections with Stratus Red Team.

Aggregate and rank signals from multiple edge-finding skills into a prioritized conviction dashboard with weighted scoring, deduplication, and contradiction detection.

Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision systems.

Trains, evaluates, exports, quantizes, and runs inference for Sparse4D multi-camera temporal 3D object detection and tracking models using TAO.

Reduce SIEM alert fatigue by systematically tuning detection rules in Splunk and Elastic, using statistical baselines, whitelists, and precision/recall metrics.

Train, evaluate, export, distill, quantize, or run inference for a TAO DINO 2D object detector using transformer-based detection with denoising training and multi-scale features.

Perform time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search using a scikit-learn compatible Python toolkit.

Analyze AWS CloudTrail logs for security monitoring, threat detection, and forensic investigation using Athena, CloudWatch Logs Insights, and SIEM integration.

Detect intrusion attempts, unauthorized access, and privilege escalation on Linux hosts using the auditd framework with ausearch and aureport utilities.

Deploy Zeek network security monitor to capture, parse, and analyze network traffic metadata for threat detection, anomaly identification, and forensic investigation.

Turns any folder of files into a navigable knowledge graph with community detection, producing interactive HTML, GraphRAG-ready JSON, and a plain-language report.

Evaluates text-to-image models on spatial relationship accuracy using the VISOR metric, separating object detection from spatial correctness to reveal biases like object priority and merging.

Implements a continual learning loop for AI coding agents using hooks, two-tier memory (global and local), and automatic pattern detection to persist and apply learnings across sessions.

Trains, evaluates, exports, quantizes, and runs inference for a Grounding DINO model that detects objects described by text prompts without a fixed class vocabulary.

Run Semgrep static analysis scans with automatic language detection, parallel subagent execution, and merged SARIF output. Supports full ruleset coverage or high-confidence security vulnerability filtering.

Detects fake reviews, Sybil rings, rating manipulation, and reputation laundering in ERC-8004 agent ratings across 20+ blockchains using the TrustLayer API.

Build network traffic baselines from NetFlow/IPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly/daily traffic pattern profiling.

Design and implement a comprehensive DevSecOps pipeline in GitLab CI/CD integrating SAST, DAST, container scanning, dependency scanning, and secret detection.

Map adversary TTPs against organizational assets using the MITRE ATT&CK framework, assess detection coverage gaps, and prioritize defensive investments.

Analyzes dependencies between stories, code modules, tickets, or specs to compute an optimal implementation order with parallel batches, cycle detection, and critical path analysis.

Analyze draft prompts to identify intent, scope, and missing context, then generate an optimized prompt with ECC component recommendations. Advisory only — never executes the task.

Coordinates purple team exercises by running MITRE ATT&CK-mapped attack scenarios with real-time detection testing and collaborative gap remediation.

Deploy and operationalize Amazon GuardDuty for continuous threat detection across AWS accounts and workloads, including enabling protection plans, interpreting findings, and building automated response workflows.