chatkit-streaming

1 stars 2 forks

Implements real-time streaming UI patterns for ChatKit applications. This skill should be used when adding response lifecycle management, progress indicators, client effects, and thread state synchronization. Covers onResponseStart/End, onEffect, ProgressUpdateEvent, and thread lifecycle events.

Also in: terraform

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Installation for Agentic Skill

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Claude Code (CLI) Fast

skilz install mjunaidca/mjs-agent-skills/chatkit-streaming

OpenCode (CLI) Fast

skilz install mjunaidca/mjs-agent-skills/chatkit-streaming --agent opencode

OpenAI Codex (CLI) Native

skilz install mjunaidca/mjs-agent-skills/chatkit-streaming --agent codex

Gemini CLI (Project) Project

skilz install mjunaidca/mjs-agent-skills/chatkit-streaming --agent gemini

First time? Install Skilz: pip install skilz

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For Claude Desktop Easy

Download Agent Skill ZIP

Extract and copy to ~/.claude/skills/ then restart Claude Desktop

Manual Installation

1. Clone the repository:

git clone https://github.com/mjunaidca/mjs-agent-skills

2. Copy the agent skill directory:

cp -r mjs-agent-skills/docs/taskflow-vault/skills/engineering/chatkit-streaming ~/.claude/skills/

Owner: mjunaidca (GitHub)
Repository: mjs-agent-skills
Stars: 1
Forks: 2
Type: Technical
Meta-Domain: web api
Primary Domain: api
Market Score: 17

Agentic Skill Grades →

Agent Skill Grade

Score: 87/100 Click to see breakdown

Score Breakdown

Spec Compliance

11/15

PDA Architecture

27/30

Ease of Use

22/25

Writing Style

9/10

Utility

18/20

Areas to Improve

Description needs trigger phrases
Missing Table of Contents
Abstract Evidence Paths

Recommendations

Add trigger phrases to description for discoverability
Add table of contents for files over 100 lines

Graded: 2026-01-24

Developer Feedback

I came across your streaming chat implementation and got curious about how you're handling the backpressure problem—most folks either buffer aggressively or drop messages, but your approach seems to thread a different needle. Wondering what shaped those design decisions?

Links:

The TL;DR

You're at 87/100, solidly in B territory. This is based on Anthropic's progressive disclosure and spec-compliance frameworks. Your strongest area is the Progressive Disclosure Architecture (27/30)—the layering between SKILL.md and your reference files is really well-structured. The weakest spot is Spec Compliance (11/15), mostly because your description is missing trigger phrases that help with discoverability.

What's Working Well

Progressive Disclosure is chef's kiss. Your main file gives the overview while client-effects.md and client-tools.md handle the deep dives. That's exactly how you structure token-efficient docs. The 6 concrete implementation patterns with clear naming (pattern-1-progressive-response-display, etc.) make it easy to jump to what you need.
Terminology is consistent throughout. You're using 'Client Effect', 'Client Tool', 'onResponseStart/End' uniformly across all three files. That consistency means developers can grep for patterns without hunting through docs.
Real problem-solving power. You're addressing actual ChatKit friction points—UI locking during responses, state synchronization issues, race conditions. The complete-frontend-config.tsx template is production-ready, not toy code.

The Big One

Your description needs trigger phrases. Right now it says "This skill should be used when..." but never actually says when. Add some...

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