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: experiment-loops

Englisches Verzeichnis

React and Next.js performance guidance for writing, reviewing, and refactoring production UI code.

30K
Stars
88/100
Trust
Kategorie: coding-agentsAudit

access to david ondrej's personal agent skills

2.7K
Stars
76/100
Trust
Kategorie: utilityAudit

A curated collection of reusable agent skills for designers and builders to generate UI prompts and workflows using AI coding agents.

4.8K
Stars
86/100
Trust
Kategorie: design-creativeAudit
Aim82

Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.

6.2K
Stars
82/100
Trust
Kategorie: ml-automationAudit

ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution

6.7K
Stars
86/100
Trust
Kategorie: ml-automationAudit

A curated list of autonomous improvement loops, research agents, and autoresearch-style systems inspired by Karpathy's autoresearch.

2.5K
Stars
77/100
Trust
Kategorie: agent-frameworksAudit

An easy to use and powerful chaos engineering experiment toolkit.(阿里巴巴开源的一款简单易用、功能强大的混沌实验注入工具)

6.4K
Stars
86/100
Trust
Kategorie: devopsAudit

🧊 Open source LLM observability platform. One line of code to monitor, evaluate, and experiment. YC W23 🍓

6.0K
Stars
86/100
Trust
Kategorie: developmentAudit

A library of practical AI-agent loops and an installable skill for finding, adapting, and designing repeatable agent workflows.

2.6K
Stars
85/100
Trust
Kategorie: agent-frameworksAudit

A Claude Code skill that generates interactive HTML courses from any codebase for non-technical users.

5.2K
Stars
76/100
Trust
Kategorie: developmentAudit

The collaborative spreadsheet for AI. Chain cells into powerful pipelines, experiment with prompts and models, and evaluate LLM responses in real-time. Work together seamlessly to build and iterate on AI applications.

1.1K
Stars
84/100
Trust
Kategorie: rag-knowledgeAudit