Skill-Verzeichnis

Wiederverwendbare Skills für AI Agents entdecken.

Durchsuche reale GitHub-Skills nach Aufgabe und prüfe Stars, Trust, Audit, Kategorie und Installationspfad vor der Verwendung.

Jede Empfehlung bleibt mit ihrem Repository, Audit und Installationspfad nachvollziehbar.

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Englisches Verzeichnis

AI-agent Skill for generating polished HTML slide decks: editorial magazine and Swiss layouts, image prompts, social covers, and a WebGL/low-power presentation runtime.

25K
Stars
88/100
Trust
Kategorie: agent-skillsAudit

Official GSAP skill for gsap.utils — clamp, mapRange, normalize, interpolate, random, snap, toArray, wrap, pipe. Use when the user asks about gsap.utils, clamp, mapRange, random, snap, toArray, wrap, or helper utilities in GSAP.

14K
Stars
76/100
Trust
Kategorie: automationAudit

VGGT-SLAM: Dense RGB SLAM Optimized on the SL(4) Manifold

982
Stars
71/100
Trust
Kategorie: robotics-iotAudit

SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM (CVPR 2024)

2.1K
Stars
69/100
Trust
Kategorie: robotics-iotAudit

Official PyTorch implementation of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image", ICCV 2019

865
Stars
65/100
Trust
Kategorie: robotics-iotAudit

Best-practices guide for React Native VisionCamera v5 setup, migration, capture, controls, outputs, and basic frame processing. Use the separate react-native-vision-camera-realtime skill for production low-latency GPU, ML, CV, Skia or WebGPU pipelines and frame-coupled overlays.

161
Stars
58/100
Trust
Kategorie: design-creativeAudit

Design and review production-grade low-latency VisionCamera v5 pipelines. Use for real-time GPU, ML, CV, Skia or WebGPU overlays, Nitro frame plugins, zero-copy interop, frame budgets, and latency profiling. Use the general react-native-vision-camera skill for setup, capture, controls, basic frame outputs, or v4 migration.

161
Stars
61/100
Trust
Kategorie: design-creativeAudit

Estimate absolute 3D human poses from RGB images.

584
Stars
63/100
Trust
Kategorie: robotics-iotAudit

Predict dense depth maps from sparse and noisy LiDAR frames guided by RGB images. (Ranked 1st place on KITTI) [MVA 2019]

510
Stars
62/100
Trust
Kategorie: robotics-iotAudit

Official PyTorch implementation of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image", ICCV 2019

492
Stars
63/100
Trust
Kategorie: robotics-iotAudit

[ICRA'23] The official Implementation of "Structure PLP-SLAM: Efficient Sparse Mapping and Localization using Point, Line and Plane for Monocular, RGB-D and Stereo Cameras"

470
Stars
63/100
Trust
Kategorie: robotics-iotAudit