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.

Suchergebnisse: icml-2019

Englisches Verzeichnis

[ICML 2026] Video generation via code

1.8K
Stars
80/100
Trust
Kategorie: agent-frameworksAudit

[ICCV 2019] Monocular depth estimation from a single image

4.5K
Stars
66/100
Trust
Kategorie: robotics-iotAudit

MedRAX: Medical Reasoning Agent for Chest X-ray - ICML 2025

1.2K
Stars
74/100
Trust
Kategorie: geo-scienceAudit

Official implementation for "RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers" (ICML 2025) , UltraViCo (ICLR 2026) and UltraImage

808
Stars
73/100
Trust
Kategorie: media-automationAudit

[ICML 2026] Official codebase for "Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation" & Causal Forcing++

786
Stars
72/100
Trust
Kategorie: media-automationAudit

MLNLP: This repository is a collection of AI top conferences papers (e.g. ACL, EMNLP, NAACL, COLING, AAAI, IJCAI, ICLR, NeurIPS, and ICML) with open resource code

2.7K
Stars
68/100
Trust
Kategorie: robotics-iotAudit

[ICML 2026] RLAnything & DemyAgent: General and scalable agentic RL algorithms across terminal, GUI, SWE, and tool-call settings

585
Stars
69/100
Trust
Kategorie: agent-frameworksAudit

Code to create Stylized-ImageNet, a stylized version of standard ImageNet (ICLR 2019 Oral)

526
Stars
73/100
Trust
Kategorie: robotics-iotAudit

[ICML 2024] LLMCompiler: An LLM Compiler for Parallel Function Calling

1.9K
Stars
69/100
Trust
Kategorie: agent-frameworksAudit

Official Repo for ICML 2024 paper "Executable Code Actions Elicit Better LLM Agents" by Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji.

1.7K
Stars
71/100
Trust
Kategorie: agent-frameworksAudit

Data augmentation for NLP, presented at EMNLP 2019

1.7K
Stars
67/100
Trust
Kategorie: rag-knowledgeAudit