AI Agent Skills

OrioleDB – building a modern cloud-native storage engine (... and solving some PostgreSQL wicked problems)

⚡️SwanLab - an open-source, modern-design AI training tracking and visualization tool. Supports Cloud / Self-hosted use. Integrated with PyTorch / Transformers / verl /…

LakeSoul is an end-to-end, realtime and cloud native Lakehouse framework with fast data ingestion, concurrent update and incremental data analytics on cloud storages for…

Turn one topic into a finished Vox-style paper-collage explainer/ad video — automated end to end on Atlas Cloud + ffmpeg. An agent skill.

Open source AI terminal for cloud and infrastructure management, enabling you to deploy, troubleshoot, and automate services using natural language and intelligent agent…

Deepnote is a drop-in replacement for Jupyter with an AI-first design, sleek UI, new blocks, and native data integrations. Use Python, R, and SQL locally in your favorit…

Install Kubernetes, and related cloud-native add-ons, it supports all-in-one, multi-node, and HA 🔥 ⎈ 🐳

An enterprise-grade Cloud-Native application platform for Kubernetes.

A private, p2p alternative to Slack and Discord built on Tor & IPFS

The dbt-native data observability solution for data & analytics engineers. Monitor your data pipelines in minutes. Available as self-hosted or cloud service with premium…

A Bridge between SDN and Cloud Native (Project under CNCF)

Compute substrate for AI agents: lightweight enough to live on your laptop, elastic enough to scale into the cloud and unleash unlimited resources.

Install our local first extensions for your favorite AI IDE or Terminal Agent. Process your histories into reusable skills with Lore. Sync your conversations to the clou…

Source code for Akash node, a secure, transparent, and peer-to-peer cloud computing network

Teach your AI to draw correct, beautiful draw.io diagrams — declarative layout engine, ground-truth stencils, structural validator, vision self-check. AWS · Azure · GCP…

Turn creator performance data into a documented retrospective, grounded hypotheses, and reusable content learnings.