Results for “object”

Use when writing, reviewing, debugging, or testing PowerShell `.ps1` scripts and advanced functions, including parameters, pipeline objects, streams, native commands, filesystem safety, PowerShell 7 portability, Pester, and PSScriptAnalyzer. Do not use merely to run an existing script, author Bash or batch files, package a module or DSC resource, or perform live system or tenant administration.

Generates setup table and page objects for Business Central following singleton pattern. Creates setup tables with single-record pattern, primary key field, setup page with auto-initialization on OnOpenPage, GetRecordOnce codeunit helper for performance, and common setup field patterns (Enable/Disable toggles, Default values, Number Series references, Path fields, User IDs). Handles DrillDownPageID and LookupPageID for seamless navigation. Supports multi-tenant and cloud-ready patterns. Use when creating setup tables, implementing module configuration, adding settings tables, creating admin configuration pages, building extension setup, or implementing singleton setup patterns for Business Central.

Generate driving scenes, scenarios, road surfaces, and 3D content from scenariobuilder.* sensor data (GPS, camera, lidar, actor tracks) using Scenario Builder for Automated Driving Toolbox. BUILD, EXPORT, or AUGMENT a virtual scenario/scene/map: ego or actor trajectories, trajectory smoothing, OpenCRG road-surface extraction, 3D asset generation, static-object placement, point-cloud georeferencing + elevation, lane-based ego localization, sensor-fusion tracking, scenario-event extraction (cut-ins, hard brakes, near-misses, ADAS disengagements), or export to RoadRunner, drivingScenario, OpenDRIVE, OpenCRG, OpenSCENARIO, or Unreal Engine. Also: log-to-scenario, scenario harvesting, accident/near-miss reconstruction, SOTIF (ISO 21448) and ISO 26262 scenario coverage, USGS-aerial-lidar augmentation, traffic-sign placement, vision-based vehicle classification for actor assets. NOT for raw-data import or multi-sensor sync/crop/offset/timestamp normalization — route those to matlab-import-driving-data.

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in...