Sonstige Ingenieurtechnologen und -techniker (außer Zeichner)

Architektur- und Ingenieurberufe · Berufliche Fähigkeiten & Kompetenzen · SOC 17-3029 · 148 Skills

Beruflicher Kontext

Diese O*NET-Aufgaben beschreiben den Beruf und geben Kontext für die SOC-Zuordnung. Sie bedeuten nicht, dass jeder aufgeführte Skill jede Aufgabe erfüllt.

Interpret the results of all methods of non-destructive testing (NDT), such as acoustic emission, electromagnetic, leak, liquid penetrant, magnetic particle, neutron radiographic, radiographic, thermal or infrared, ultrasonic, vibration analysis, and visual testing.
Interpret or evaluate test results in accordance with applicable codes, standards, specifications, or procedures.
Identify defects in solid materials, using ultrasonic testing techniques.

Die O*NET-Aufgaben werden in ihrer Ausgangssprache (Englisch) angezeigt. Die Aufgabendaten sind der O*NET® 29.1 Database der USDOL/ETA entnommen und werden gemäß CC BY 4.0 verwendet.

Repräsentative Quellen

Eine begrenzte Auswahl beliebter, diesem Beruf zugeordneter Skills, mit höchstens einem Skill je GitHub-Repository. Darunter folgt die vollständige Zuordnung.

openakita-skills-baidu-yijian

openakita/openakita

Baidu Yijian visual management skill for industrial scenarios. Provides visual recognition capabilities across 20+ industries including retail, energy, mining, ports, chemical, and steel. Use for industrial visual inspection and management.

environmental-stress-testing

a5c-ai/babysitter

Design and execute environmental stress tests for embedded hardware — temperature cycling, vibration, humidity, and EMC validation.

factory-acceptance-test

mohitagw15856/pm-claude-skills

Write a factory acceptance test (FAT) plan or report — test coverage matrix against spec, AQL sampling plan, pass/fail criteria, golden-sample handling, deviation log, and sign-off structure. Use when asked to write a FAT plan, define outgoing quality inspection, set AQL levels, prepare for a factory acceptance or pre-shipment inspection, or document FAT results. Produces a complete FAT plan or report with sampling tables, defect classification, and a sign-off block.

fdm-printing-beginner-guide

FerroxLabs/wayland

Teaches beginners FDM 3D printing fundamentals: slicer software settings (layer height, infill percentage, support structures), bed adhesion methods, common print failure diagnosis (stringing, warping, layer shifting, under-extrusion), and a first-print sequence that builds understanding progressively. Use when the user wants to start 3D printing, asks about slicer settings, print quality troubleshooting, filament selection, or bed adhesion. Do NOT use for resin (SLA/DLP) printing, 3D modeling and CAD design, or professional manufacturing processes.

Alle zugeordneten Skills

Durchsuchen Sie die vollständige Zuordnung nach Repository-Beliebtheit oder jüngster Quellaktivität.

Namen und Beschreibungen der Skills bleiben in der vom Quell-Repository bereitgestellten Sprache.

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openakita-skills-baidu-yijian

openakita/openakita

Baidu Yijian visual management skill for industrial scenarios. Provides visual recognition capabilities across 20+ industries including retail, energy, mining, ports, chemical, and steel. Use for industrial visual inspection and management.

environmental-stress-testing

a5c-ai/babysitter

Design and execute environmental stress tests for embedded hardware — temperature cycling, vibration, humidity, and EMC validation.

factory-acceptance-test

mohitagw15856/pm-claude-skills

Write a factory acceptance test (FAT) plan or report — test coverage matrix against spec, AQL sampling plan, pass/fail criteria, golden-sample handling, deviation log, and sign-off structure. Use when asked to write a FAT plan, define outgoing quality inspection, set AQL levels, prepare for a factory acceptance or pre-shipment inspection, or document FAT results. Produces a complete FAT plan or report with sampling tables, defect classification, and a sign-off block.

fdm-printing-beginner-guide

FerroxLabs/wayland

Teaches beginners FDM 3D printing fundamentals: slicer software settings (layer height, infill percentage, support structures), bed adhesion methods, common print failure diagnosis (stringing, warping, layer shifting, under-extrusion), and a first-print sequence that builds understanding progressively. Use when the user wants to start 3D printing, asks about slicer settings, print quality troubleshooting, filament selection, or bed adhesion. Do NOT use for resin (SLA/DLP) printing, 3D modeling and CAD design, or professional manufacturing processes.

benchflow-ai/benchflow

Analyzes 3D mesh files (STL) to calculate geometric properties (volume, components) and extract attribute data. Use this skill to process noisy 3D scan data and filter debris.

flat-inspection

Grigorij-Dudnik/RoboCrew

Inspect flat walls with a Tello drone, find renovation artifacts, and save visible artifact photos.

blender-render-automation

TerminalSkills/skills

Automate Blender rendering from the command line. Use when the user wants to set up renders, batch render scenes, configure Cycles or EEVEE, set up cameras and lights, render animations, create materials and shaders, or build a render pipeline with Blender Python scripting.

blender-scripting

TerminalSkills/skills

Write and run Blender Python scripts for 3D automation and procedural modeling. Use when the user wants to automate Blender tasks, create 3D models from code, run headless scripts, manipulate scenes, batch process .blend files, build geometry with bmesh, apply modifiers, generate procedural shapes, or import/export 3D models using the bpy API.

blender-vse-pipeline

TerminalSkills/skills

Automate video editing in Blender's Video Sequence Editor with Python. Use when the user wants to add video, image, or audio strips, create transitions, apply effects, build edit timelines, batch assemble footage, estimate render times, or script any VSE workflow from the command line.

NVIDIA-Omniverse/kit-cae

Capture clean render-only images and movies from Kit-CAE visualizations. Produces output without application UI - just the rendered scene. Supports high-resolution PNG/EXR screenshots, MP4 video with NVENC hardware encoding, and animated camera orbits. Triggers on requests for "capture image", "render screenshot", "export movie", "high-res render", "save visualization", "record animation", "camera pan", or any request to produce clean output from a Kit-CAE scene.

perceiving-next-item

graph-robots/open-robot-skills

Loop-head perception for pack-all / clean-all-items tasks. From ONE observation it localizes BOTH the destination container (basket, bin, box) AND the next remaining target item, using the pairwise VLM crop tournament with a container-excluding description so the target is never confused with the basket, then makes a clean found / none decision: an item was found (grasp it) or only the container remains (all items packed — exit the loop to done). Use when a workflow must pick up EVERY object and place each into a container in a loop (pack-all / clean-all-items) and each pass must reliably answer "is there still a graspable item, or are we done?" while also exposing a fresh container OBB for the downstream place. This subgraph is self-contained and takes NO inputs (inputs: {}); the container and item phrases are literal strings written inside the perception nodes, never subgraph parameters — do not declare item_name / container_name / item_description as subgraph inputs.

perceiving-objects

graph-robots/open-robot-skills

Fast single-path 3D object perception. Runs Grounding-DINO broad detection, a pairwise VLM crop tournament to identify the target box, SAM3 box segmentation, and depth back-projection to a world-frame point cloud, finished by geometry.filter_and_compute_obb for a clean oriented bounding box, mask, and cloud. Multi-camera rigs get a verified wrist-camera fallback gate. Use when a manipulation workflow needs to localize one named object quickly — uncluttered scenes with visually distinct targets, latency-bound loops, or platforms deploying only DINO + VLM + SAM3 + geometry.