What problem does it solve? Building multi-turn conversations for language models in Effect requires correctly assembling messages, parts, tool calls, and history; this Skill provides the complete Prompt API patterns so you avoid malformed messages, mutation bugs, and incorrect streaming-history conversion. ## Core Features & Use Cases - Message and Part Construction: Create system, user, assistant, and tool messages with text, reasoning, file, tool-call, tool-result, and tool-approval parts using Prompt.makeMessage and Prompt.makePart constructors. - Prompt Composition and History: Merge prompts with Prompt.concat, manage system messages with setSystem/prependSystem/appendSystem, and convert streaming responses into history via Prompt.fromResponseParts. - Serialization and Provider Options: Encode prompts to JSON with Schema.fromJsonString(Prompt.Prompt) and attach provider-specific options like Anthropic cache control through module augmentation. - Use Case: Build a chat loop that keeps conversation state in a SubscriptionRef, appends user input with Prompt.concat, calls LanguageModel.generateText, and folds the response back into history with Prompt.fromResponseParts. ## Quick Start Ask the AI to build an Effect AI chat loop that constructs a prompt with a system message and user input, generates text with LanguageModel, and appends the response to conversation history using Prompt.fromResponseParts.
