skills

Use when building, changing, or extending a software project so work stays continuous, prioritized, and consistent across sessions

Use while building or maintaining a project to continuously improve the development system itself by fixing recurring process failures, tightening docs/skills, and proposing larger improvements when uncertain

Use when doing any git operations (branching, committing, pushing) so the repo history stays clean and matches the preferred workflow

Define reusable AI assistants with RubyLLM::Agent. Use this skill when creating class-based agents with persistent configuration, runtime context, prompt management, Rails-backed agents, and agentic workflows.

Convert speech to text with RubyLLM. Use this skill for transcribing meetings, podcasts, lectures, interviews with support for speaker diarization, timestamps, and multiple languages.

Generate vector embeddings with RubyLLM. Use this skill for semantic search, recommendations, content similarity, RAG applications, and any task requiring numerical text representations.

Generate images from text with RubyLLM. Use this skill for creating images with DALL-E 3, GPT Image 1, Imagen, editing existing images, and working with masks for constrained editing.

ActiveSupport::Notifications instrumentation for RubyLLM. Use this skill when building custom monitoring, logging, or analytics for RubyLLM usage. Publishes events for chat completions, tools, embeddings, images, moderation, and transcription.

Model Context Protocol (MCP) client for RubyLLM. Use this skill when connecting RubyLLM to MCP servers for tools, resources, and prompts. Supports stdio, HTTP streaming, and SSE transports with OAuth 2.1 authentication.

Content safety checking with RubyLLM. Use this skill to identify potentially harmful content in text before sending to LLMs, screen user inputs, and moderate AI outputs for safety.

Rails engine for RubyLLM monitoring dashboards and alerts. Use this skill when you need visibility into LLM costs, throughput, response times, and error rates. Captures events from ruby_llm-instrumentation.

OpenTelemetry tracing for RubyLLM. Use this skill when you need observability into LLM applications with support for Langfuse, Datadog, Honeycomb, Jaeger, Arize Phoenix, and any OpenTelemetry-compatible backend.

Rails integration for RubyLLM. Use this skill when setting up ActiveRecord-backed chats, Hotwire/Turbo streaming, background job processing, chat UI generation, agents in Rails, file attachments with ActiveStorage, and multi-tenant LLM contexts.

Local LLM execution with quantized GGUF models for RubyLLM. Use this skill when running models locally for zero latency, no API costs, complete privacy, and offline capability. Supports Metal (macOS), CUDA (NVIDIA), and CPU.

Ruby DSL for JSON Schema creation. Use this skill when defining structured data schemas for LLM function calling or structured outputs with RubyLLM. Provides Rails-inspired API for creating complex nested schemas.

One beautiful Ruby API for GPT, Claude, Gemini, and more. Use this skill when building AI-powered applications with RubyLLM - chatbots, AI agents, RAG applications, content generators, vision/audio analysis, embeddings, image generation, and Rails integration. Supports 15+ providers with a unified interface.

Function calling for RubyLLM. Use this skill when creating tools that let AI call your Ruby code, declaring parameters with the params DSL, using tools in chat, monitoring tool calls with callbacks, handling tool security, and implementing advanced patterns like halt and provider-specific parameters.

LLM evaluation and testing for RubyLLM. Use this skill when you need to verify AI response quality, test for hallucinations, check safety, run red team attacks, or integrate LLM-as-judge assertions into your test suite.