Results for “segmentation”

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

Segments abdominal organs from CT NIfTI volumes using the NV-Segment-CT VISTA3D model, producing label maps and structured evidence JSON.

Fine-tune NV-Segment-CT VISTA3D on CT NIfTI labels for smoke testing or dataset adaptation, wrapping the upstream MONAI bundle entrypoint.

Audit, create, and monitor GCP VPC firewall rules to enforce network segmentation and least-privilege access.

Design products and pricing around validated willingness to pay, using the framework from Ramanujam & Tacke's "Monetizing Innovation" to avoid common monetization failures.

Identify and analyze 3-5 distinct customer segments with demographics, jobs-to-be-done, pain points, and product fit analysis for market opportunity evaluation.

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

Guide product managers through discovering and articulating product positioning by asking adaptive questions about target customers, unmet needs, product category, benefits, and competitive differentiation.

Segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks with zero-shot transfer.

Configures Salesforce Media Cloud for advertising sales management, covering ad product catalogs, rate cards, audience targeting, revenue recognition, and ad-server integration.

Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.

Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.

Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.

Extract and preprocess tiles from whole-slide images (WSI) with histolab — OpenSlide-backed slide loading, tissue detection and masks, Random/Grid/Score tile extraction, and image/morphological filters for H&E preprocessing. Use when the user needs lightweight WSI slide preprocessing — building tile datasets for ML training, tissue segmentation, or quick tile-based inspection of histopathology slides. For end-to-end computational-pathology, deep-learning model training, nucleus segmentation, or multiplexed/spatial-proteomics (CODEX, Vectra) pipelines prefer alterlab-pathml instead. Part of the AlterLab Academic Skills suite.

Create and apply Kubernetes NetworkPolicies to enforce pod-level network segmentation, restrict traffic between pods and namespaces, and block access to cloud metadata endpoints.

Assess the security of WiFi infrastructure through authorized penetration testing, including weak encryption detection, handshake capture, evil twin attacks, and network segmentation validation.

Map application dependencies, create granular network policies, visualize east-west traffic flows, and enforce least-privilege communication between workloads using Akamai Guardicore Segmentation.

Loads and processes whole-slide pathology images, builds spatial graphs, trains deep learning models, and analyzes multiplexed immunofluorescence data across 160+ slide formats.

Enrich CRM records with firmographic and contact data, filling gaps in company and person profiles to improve segmentation, routing, and outreach quality. Use when the user requests crm data enrichment or provides relevant inputs for this workflow.

Synthesize customer research data to define the Ideal Customer Profile (ICP) including demographics, behaviors, Jobs to Be Done, and pain points.

Trains, evaluates, exports, and runs inference for Visual ChangeNet models used in AOI defect detection, comparing image pairs for PASS/NO_PASS classification or change-segmentation masks.

Identify the first beachhead market segment for a product launch by evaluating segments against burning pain, willingness to pay, winnable market share, and referral potential.

Compute TAM/SAM/SOM with top-down and bottoms-up methods, plan survey sample sizes with per-segment minimums, and score candidate segments against Kotler's five criteria.

Enforce Kubernetes network segmentation using Calico CNI network policies and global network policies to control pod-to-pod traffic, restrict egress, and implement zero-trust microsegmentation.

Define product positioning by mapping competitive alternatives, unique attributes, and best-fit customers to the right market category using a structured canvas and 5-step process.

Full-featured computational pathology toolkit. Use for advanced WSI analysis including multiplexed immunofluorescence (CODEX, Vectra), nucleus segmentation, tissue graph construction, and ML model training on pathology data. Supports 160+ slide formats. For simple tile extraction from H&E slides, histolab may be simpler.

Executes containment strategies to stop active adversary operations and prevent lateral movement during a confirmed security breach, using network segmentation, endpoint isolation, credential revocation, and access control modifications.

Execute a wireless network penetration test to assess WiFi security by capturing handshakes, cracking WPA2/WPA3 keys, detecting rogue access points, and testing wireless segmentation using Aircrack-ng and related tools.

Analyze whole-slide pathology images with Python: load 160+ slide formats, preprocess H&E stains, segment nuclei, construct spatial graphs, train ML models, and process multiplex immunofluorescence data (CODEX, Vectra).

Simulates VLAN hopping attacks using switch spoofing and double tagging techniques in authorized environments to test VLAN segmentation effectiveness and validate switch port security configurations against Layer 2 bypass attacks.

Klaviyo marketing operations and analyst expertise. Audit flows, segments, campaigns, deliverability, and revenue attribution. Use when the user asks about Klaviyo marketing strategy, email/SMS automation, customer segmentation, flow optimization, or lifecycle marketing. For API integration, SDK, webhook, or developer questions, see the klaviyo-developer skill.

Internal network assessment. VLAN hopping, ARP spoofing detection, broadcast protocol abuse (LLMNR/NBT-NS/mDNS), network segmentation verification, SNMP enumeration, NFS exposure, router/switch audit, and internal service mapping. Assumes attacker has network access. Uses nmap, arp-scan, nbtscan, snmpwalk, onesixtyone, smbmap, nfs-common, masscan, hping3, and netexec.

Design and implement security zones and conduits for industrial automation and control systems per IEC 62443-3-2, including zone partitioning, firewall configuration, and validation through traffic analysis and penetration testing.

Generate comprehensive email marketing strategy with 90-day implementation roadmap. Analyzes business type (local-business, saas, ecommerce, creator, agency) to deliver industry-specific segmentation plans, automation sequences, content calendars, KPI targets, platform recommendations, and week-by-week rollout schedule. Use when building email program from scratch or restructuring existing strategy.

Creates research-backed user personas with JTBD, pain points, gains, and unexpected behavioral insights. Use when building personas from survey or interview data, or segmenting users to inform product decisions. Triggers on: user persona, user profile, customer segment, jobs-to-be-done, JTBD, persona creation, user segmentation, target user, who are our users.

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.