247 skills · sorted by relevance
Plan and review home network changes before altering settings. The skill checks VLANs, which separate device groups, local DNS filtering, and VPN access, then organizes staged changes, validation checks, and recovery steps.
Plan inventory for retailers with multiple stores by choosing demand forecasts, calculating stock buffers and replenishment quantities, and assessing promotions. Account for demand drops after promotions, supplier lead times, and seasonal markdown timing.
Use SAM to separate objects from images by marking points or drawing boxes, without training a model for each task. Covers automatic region generation, refining selections, preparing annotation data, and exporting models for deployment.
Analyze time series with aeon, a Python toolkit for data that changes over time. Supports classification, regression, clustering, forecasting, anomaly detection, and pattern search, with guidance on preprocessing, algorithm selection, and evaluation.
Investigate attackers moving between internal systems by correlating login records with network traffic. Supports detection rules, suspicious SMB and RDP activity analysis, and reports showing attack timelines, affected hosts, and monitoring gaps.
Assess authorized networks through host discovery, service checks, vulnerability identification, and controlled exploitation. Follow a structured testing process to document attack paths, demonstrate business impact, clean up test artifacts, and prioritize remediation.
Contain an active security breach by isolating endpoints, blocking attacker communications, and revoking compromised access. Follow procedures to limit spread between systems, preserve evidence, verify containment, and report remaining risks and business impact.
Conduct authorized WiFi security assessments covering authentication, encryption, rogue access points, and network separation. Test WPA2/WPA3 configurations and client behavior, then document weaknesses, evidence, business impact, and recommended fixes.
Review customer retention and customer success plans through six focused questions. Examine churn causes, customer segments, staffing capacity, hiring, and compensation, then produce a decision report with a verdict and three concrete next steps.
Plan customer retention and customer success staffing for a startup. Break down renewal and churn patterns, group customers by value and fit, and compare service models to guide resource allocation and hiring.
Practice spoken answers to product manager interview questions about designing or improving products. Follow six steps to clarify assumptions, choose users, prioritize a problem, and compare solutions before selecting an MVP, a first version with limited scope.
Analyze Mixpanel data with Python to explore usage trends, conversion steps, returning users, and differences between user groups. Also manage business context documents that explain an organization's structure and goals to AI assistants.
Write comprehensive literature reviews for medical imaging AI research. Use when writing survey papers, systematic reviews, or literature analyses on topics like segmentation, detection, classification in CT, MRI, X-ray, ultrasound, or pathology imaging. Triggers on requests for "review paper", "survey", "literature review", "综述", "systematic review", or mentions of writing academic reviews on deep learning for medical imaging.
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Domain routing and boundary guidance for authorized network-layer security testing, including exposed services, protocol downgrade, man-in-the-middle risks, and segmentation bypasses. Use when a task belongs to the network testing domain and needs scope, evidence, pivot, or exit criteria.
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World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.
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Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides, H&E stained images, multiplex immunofluorescence (CODEX, Vectra), spatial proteomics, nucleus detection/segmentation, tissue graph construction, or training ML models on pathology data. Supports 160+ slide formats including Aperio SVS, NDPI, DICOM, OME-TIFF for digital pathology workflows.
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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
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Data-backed email marketing skill for AI agents. Use when building or running email automation, driving an ESP from an agent (MCP/connectors), diagnosing deliverability, writing or de-slopping email copy, directing AI email design, choosing a platform, or pulling benchmarks. Covers flows, segmentation, compliance, cold email, WhatsApp, SMS and RCS, and 19 industry playbooks.
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Automate Mixpanel tasks via Rube MCP (Composio): events, segmentation, funnels, cohorts, user profiles, JQL queries. Always search tools first for current schemas.
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Runs standard or fixed-channel softmax finetuning of NV-Segment-CT VISTA3D on CT NIfTI image/label datasets, with optional MONAI-native MLflow tracking and checkpoint evidence. Uses softmax for predefined, mutually exclusive classes; keeps the standard workflow when point prompts or runtime-variable classes are needed. Not for clinical validation.
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Used for running NV-Segment-CT VISTA3D on CT NIfTI volumes and recording label-map evidence.
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How to swap the DeepStream CV detection model in the VSS Alerts Blueprint verification (2d_cv) mode - covers ONNX export, custom bbox parsers, compose mount gotchas, nvinfer config, runtime TRT engine build, deployment, and a segmentation-capable model addendum handoff.
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Used for running NV-Segment-CTMR on CT or MRI NIfTI volumes and recording label-map evidence. Not for clinical interpretation.
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Use when planning, executing, and independently validating Jetson Video Codec SDK or PyNvVideoCodec encode/decode, transcode, segmentation, container decode, AV1, or concise acceptance workflows.
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