Skill UI

This guide provides a comprehensive local development setup for LLM applications using Langfuse. It enables fast iteration cycles, real-time trace debugging, and hot reloading, making it ideal for developers building and testing complex AI workflows locally. Instructions cover environment setup, advanced SDK usage, and optional self-hosted Docker deployment.

This guide provides essential security best practices for implementing Langfuse, an LLM observability platform. It covers critical areas such as secure credential management (public vs. secret keys), implementing PII scrubbing techniques before tracing, hardening self-hosted environments, and enforcing the principle of least privilege. Use this guide to ensure your LLM integrations meet strict data privacy standards (e.g., GDPR, HIPAA).

This comprehensive guide details the deployment process for Mistral AI-powered applications across various cloud and container platforms, including Vercel, Docker, Google Cloud Run, and self-hosted vLLM setups. It covers secure API key management, platform-specific secret configuration, and the full steps required to establish robust, scalable production pipelines for your AI model.

This skill automates the setup and configuration of Ollama, allowing users to deploy Large Language Models (LLMs) locally. It performs system assessment (OS, GPU, RAM), selects optimal models based on hardware availability, and handles installation across macOS, Linux, and Docker. Use this when you require free, self-hosted AI alternatives, need offline inference capabilities, or wish to eliminate reliance on costly hosted API services.

A comprehensive guide for deploying PostHog analytics platform across diverse production environments. Covers advanced configurations for Next.js/Vercel (including reverse proxy for ad blocker bypass and edge functions for server-side capture), self-hosted setups using Docker, and deployment patterns for Google Cloud Run. Ensures robust, reliable user behavior tracking regardless of the hosting platform.

This guide provides comprehensive patterns for optimizing Turborepo build pipelines within monorepos. It covers local and remote caching strategies, including integration with Vercel and self-hosted solutions. Use it when setting up new monorepos, improving CI/CD performance, or debugging complex build dependency issues.

Self-hosted open-source alternative to NotebookLM for organizing research, ingesting diverse sources, generating AI notes, running context-aware chats, performing vector search, producing multi-speaker podcasts, and automating workflows via a REST API.

A comprehensive guide on deploying Cloudflare Access and Tunnel to establish a Zero Trust Network Access (ZTNA) solution. This method replaces traditional VPNs by securely exposing self-hosted internal applications (web, SSH, RDP) without opening inbound ports. It integrates identity-aware policies, device posture checks, and robust identity providers (Okta, Azure AD) for highly secure, least-privilege access.

This guide details deploying Tailscale (or self-hosted Headscale) to create a WireGuard-based zero trust mesh VPN. It establishes encrypted peer-to-peer connections between devices, eliminating the need for traditional VPN servers. Features include identity-aware Access Control Lists (ACLs), subnet routing, and secure connectivity for compliance-driven architectures. Use this when enforcing granular, identity-authenticated access control.

Guides on using SwanLab for open-source ML experiment tracking, offering local or self-hosted dashboards, rich media logging, and integrations with PyTorch, Transformers, Lightning, or Fastai for comparing runs and monitoring metrics.

Self-hosted open-source alternative to Google NotebookLM. Organize research sources, generate AI notes and summaries, create multi-speaker podcasts, chat with documents, and search materials via full-text or vector search with 16+ AI providers.

This guide provides comprehensive steps for setting up and authenticating the official ClickHouse client for both Node.js and Python. It covers connecting to ClickHouse Cloud or a self-hosted instance, managing environment variables, and verifying the connection using sample code snippets. Ideal for new ClickHouse projects or integrating data analytics pipelines into existing applications.