Results for “batch-scanning”

Batch Signal Isolation with Recipe-Driven Processing

Import object detection models from HuggingFace or NVIDIA NGC into a DeepStream pipeline with automated ONNX download, TensorRT engine build, custom parser, multi-stream benchmark, and PDF report generation.

Write, debug, and maintain Windows batch files (.bat/.cmd) for system administration, automation, and CLI tool development.

Guides scaling Qdrant query throughput (QPS) through performance tuning, horizontal scaling with read replicas, and disk I/O optimization.

Diagnose and fix automatic batching regressions in React 18 class components with a decision tree for setState behavior changes.

Apply consistent photo adjustments across a set of images so they look like they were edited together, using Adobe tools for color, tone, cropping, and optional selective enhancements.

Process and manipulate images using ImageMagick: resize, convert formats, batch process, and retrieve metadata.

Build automated AI workflows combining multiple models and services for batch processing, scheduled tasks, event-driven pipelines, and agent loops using the inference.sh CLI.

Multi-project signal isolation with cascading recipe resolution

Apply operations across multiple files simultaneously. Pattern-based bulk modifications, search-and-replace across codebases, consistent changes to many files at once.

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.

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

SA-1B: Segment Anything 1 Billion Masks Dataset

The choice is not about scale or sophistication.

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.

Design, review, and debug Batch Apex contracts, scope sizing, stateful behavior, chaining, and AsyncApexJob monitoring.

Stitched Image Quality Control

Launches a model evaluation batch with parameter collection, pre-flight checks, execution, and post-run analysis for interactive or foreground runs.

AI image generation with OpenAI, Google and DashScope APIs. Supports text-to-image, reference images, aspect ratios. Sequential by default; parallel generation available on request. Use when user asks to generate, create, or draw images.

Run full computational-pathology workflows with PathML — whole-slide-image (WSI) analysis across 160+ slide formats, multiplexed immunofluorescence (CODEX, Vectra, MERFISH), nucleus segmentation/classification (HoVer-Net, HACTNet), tissue- and cell-graph construction, HDF5 dataset management, and deep-learning model training on pathology data. Use when the user builds end-to-end deep-learning pathology pipelines, analyzes multiplexed or spatial-proteomics slides, or segments nuclei. For lightweight H&E slide preprocessing, tissue masking, or plain Random/Grid/Score tile extraction prefer alterlab-histolab instead. Part of the AlterLab Academic Skills suite.

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.

Batch-generate images via OpenAI Images API. Random prompt sampler + `index.html` gallery.

Batch visual production workflow for campaigns. Accepts a content calendar or campaign brief, produces multiple platform-ready assets with organized bundle output and machine-readable manifest. Trigger for 'batch design', 'content calendar to assets', 'campaign batch', 'produce all campaign visuals', or 'bulk asset generation'.

Diagnoses and resolves slow Qdrant indexing and data ingestion by optimizing batching, sharding, HNSW parameters, and payload indexing strategies.

Bulk-retouch a folder of portrait photos using Adobe tools — designed for wedding photographers and event photographers who need fast, walk-away batch processing.

Scan installed skills to extract cross-cutting principles and distill them into rules files.

Generate professional product images for e-commerce, including hero shots, packshots, lifestyle scenes, and detail close-ups using AI via the inference.sh CLI.

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.

Generate and edit images using OpenAI's GPT-Image-2 model via the inference.sh CLI, supporting text-to-image, image editing, inpainting, and batch generation.

Scaling Vision with Sparse Mixture of Experts

Integrates SAST, DAST, and SCA security scanning into CI/CD pipelines using open-source tools like Semgrep, Trivy, OWASP ZAP, and Gitleaks.

Performs advanced network reconnaissance using Nmap's scripting engine, timing controls, evasion techniques, and output parsing to discover hosts, enumerate services, detect vulnerabilities, and fingerprint operating systems across authorized target networks.

One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.

CI security scanning: secrets, deps, SAST, triage, expiring exceptions