effect-ai-streaming

Name: effect-ai-streaming
Availability: InStock
Author: front-depiction

Manage real-time AI streaming with start/delta/end protocols and history synchronization.

Updated Aug 27, 2026

Search Tags:#concurrency #AI #streaming #Effect #history-management #StreamPart

Ready to run

npx skills add https://github.com/front-depiction/claude-setup --skill effect-ai-streaming

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: effect-ai-streaming
Source: https://github.com/front-depiction/claude-setup/tree/main/skills/effect-ai-streaming
Command: npx skills add https://github.com/front-depiction/claude-setup --skill effect-ai-streaming

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides a complete, battle-tested framework for real-time AI streaming using the Effect ecosystem, including a robust start/delta/end protocol, incremental accumulation, and safe history management with SubscriptionRef.

Core Features & Use Cases

Real-time streaming: manage incremental model outputs with text, reasoning, and tool calls.
History synchronization: merge streaming parts into a persistent prompt history while preserving checkpoints.
Concurrency protection: coordinate multiple streams to prevent race conditions and resource contention.

Quick Start

Install and import the required Effect libraries, then wire a simple chat stream using the Stream and AI tooling described in this skill.

Frequently Asked Questions about effect-ai-streaming

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I stream AI responses safely while preserving history in Effect?▼

To stream AI responses safely in Effect, use a start/delta/end protocol with SubscriptionRef to merge incremental parts into persistent history, preventing race conditions during multi-stream coordination.

What is the best way to manage concurrent AI streams without race conditions?▼

Managing concurrent AI streams without race conditions requires a framework that coordinates multiple streams and protects resources. An incremental start/delta/end protocol with safe history syncing prevents data contention.

How do I accumulate incremental text and tool calls from a real-time AI stream?▼

Accumulating incremental text and tool calls from a real-time AI stream requires typed StreamPart accumulation strategies. This merges deltas into a complete output while handling reasoning and tool call data.

Does the Effect ecosystem support real-time AI streaming with typed stream parts?▼

The Effect ecosystem supports real-time AI streaming with typed stream parts by utilizing Stream, Channel, and Match components alongside @effect/ai tooling to load and manipulate streaming data incrementally.

How do I sync incremental model outputs into a persistent prompt history?▼

Syncing incremental model outputs into a persistent prompt history requires merging streaming parts into checkpoints. Using SubscriptionRef ensures history synchronization remains safe during concurrent updates.

When do I need a start/delta/end protocol for AI streaming?▼

A start/delta/end protocol for AI streaming is needed when building chat interfaces that require incremental responses, multi-stream coordination, and safe history syncing to manage real-time model outputs without data loss.