Skill UI

This SDK provides comprehensive content moderation capabilities for Python applications using Azure AI. It allows developers to analyze text and images to detect harmful content across multiple categories, including hate speech, violence, sexual content, and self-harm. Key features include multi-severity classification, managing custom blocklists, and analyzing content from local files or URLs. Ideal for pre-moderating user-generated content (UGC) or AI outputs to ensure platform safety.

This SDK provides comprehensive content moderation capabilities for Python applications using Azure AI. It allows developers to analyze text and images to detect harmful content across multiple categories, including hate speech, violence, sexual content, and self-harm. Key features include multi-severity classification, managing custom blocklists, and analyzing content from local files or URLs. Ideal for pre-moderating user-generated content (UGC) or AI outputs to ensure platform safety.

A comprehensive runbook detailing procedures for responding to failures and outages across Lindy AI agents. Covers critical scenarios including platform downtime (SEV1), individual agent failures, integration authentication issues, credit exhaustion, and webhook endpoint failures. Provides structured diagnostics and fallback strategies to ensure business continuity during incidents.

A comprehensive guide detailing advanced strategies for migrating complex, rule-based automations from platforms like Zapier, Make, n8n, or custom codebases to Lindy AI. It teaches users how to redesign workflows, leveraging AI agents to replace rigid conditional logic with adaptable reasoning, ensuring a smooth and intelligent transition across multiple phases.

llama.cpp is a pure C/C++ library designed for efficient LLM inference, optimized to run on resource-constrained environments. It excels in scenarios where high-end NVIDIA GPUs (CUDA) are unavailable, making it ideal for Apple Silicon Macs, AMD/Intel GPUs, or edge deployment devices like Raspberry Pi. It supports GGUF quantization formats, enabling significant memory reduction and speed improvements for local, cross-platform AI applications.

This skill provides comprehensive techniques for optimizing prompts used with any Large Language Model (LLM). It teaches proven methodologies—including Role definition (R), Context setting (S), Constraints (C), Instructions (I), and Templates (T)—to transform vague inputs into precision-engineered instructions. Users can reliably achieve structured JSON output, implement Chain-of-Thought reasoning, reduce hallucinations, and minimize token usage, ensuring consistent and high-quality results across different AI platforms.

This skill automates the end-to-end workflow for deploying and executing ML training and inference experiments. It intelligently detects the target environment (Local GPU, Remote SSH, Vast.ai, Modal serverless) and handles necessary steps like GPU pre-checks, code synchronization (rsync/git), integrating ML tracking tools (W&B), and launching the compute job via screen sessions or serverless deployment. Use when initiating a complex training job.

An autonomous factory for generating production-ready, cross-platform AI agent skills. It accepts raw, unstructured human input—such as transcripts, PDFs, screenshots, vague ideas, or code snippets—and reverse-engineers the complete workflow logic. It bridges the gap between human intent and functional agent specifications, allowing users to automate complex processes without writing code.

A cross-protocol platform for discovering and searching 18,000+ MCP servers and AI agents across six or more registries. It allows users to find the necessary endpoint for specific tasks (like database access or file conversion) by searching across MCP, A2A, and agents.txt protocols simultaneously.

This skill acts as an advanced extension layer, implementing a self-evolution protocol, maintaining session state continuity (Compaction), and providing KPI-style performance summaries. It allows users to simulate continuous professional development, track metrics, and master complex role-playing and interaction skills across multiple sessions, ensuring performance baselines are continuously adjusted and internalized.

This skill provides comprehensive guides for deploying stateless, serverless API wrappers powered by Claude (Anthropic). It details how to integrate and stream responses from the Messages API across major cloud environments, including Vercel Edge Functions, Fly.io, and Google Cloud Run. It is essential for productionizing AI applications that require robust secret management, scalable infrastructure, and real-time streaming capabilities.

This skill provides comprehensive deployment guides for Cohere API v2 applications across major cloud platforms including Vercel, Fly.io, and Google Cloud Run. It covers critical aspects like secret management, setting up reliable health checks, optimizing performance, and implementing streaming chat endpoints to ensure production readiness.