Channels Event Streaming logo

Channels Event Streaming

Organization
UitbreidenOS
channels-event-streaming

Claude Code Channels: real-time event streaming between concurrent sessions, pub/sub patterns, event filtering, cross-session state synchronization, and reactive agent workflows

Overview

PublisherUitbreidenOS
RepositoryUitKit
Skill namechannels-event-streaming
Stars
14
Forks
5
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by UitbreidenOS on GitHub. Read the source before you install it.

Installation

Install the Channels Event Streaming AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/UitbreidenOS/UitKit/tree/main/plugins/uitkit-ai-engineering/skills/channels-event-streaming
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/UitbreidenOS/UitKit.git /tmp/UitKit
mkdir -p .claude/skills
cp -r /tmp/UitKit/plugins/uitkit-ai-engineering/skills/channels-event-streaming .claude/skills/channels-event-streaming
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Channels Event Streaming in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Channels Event Streaming on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Channels Event Streaming is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Channels — Real-Time Event Streaming

When to activate

  • Running multiple Claude Code sessions that need to share state or coordinate work
  • Building reactive workflows that respond to events (file changes, CI results, deployment status)
  • Implementing pub/sub patterns where one session publishes events and others subscribe
  • Streaming progress updates from a long-running background session to the main session
  • Coordinating multi-agent workflows where agents need real-time awareness of each other's progress
  • Building event-driven development loops (code → test → deploy → monitor → react)

When NOT to use

  • Single-session workflows with no cross-session communication needs
  • One-shot tasks that don't benefit from event-driven architecture
  • Simple file-based state passing (just read/write a shared file)
  • When eventual consistency is fine and polling works just as well

Instructions

1. Channel Architecture

Channels provide real-time event streams between Claude Code sessions:

┌──────────────┐  publish   ┌──────────┐  subscribe  ┌──────────────┐
│  Session A   │──────────▶│  Channel │◀────────────│  Session B   │
│  (publisher) │            │  (stream)│             │  (subscriber)│
└──────────────┘            └──────────┘             └──────────────┘
                                  │
                            subscribe
                                  │
                            ┌──────────────┐
                            │  Session C   │
                            │  (subscriber)│
                            └──────────────┘

2. Channel Types

Channel TypeUse CaseEvent FrequencyExample
ProgressReport task completion %Low (per milestone)Build progress: 45% → 60% → 80%
StateSync shared stateMedium (on change)Deployment status: staging → production
AlertNotify on conditionsLow (on trigger)CI failure, security finding, SLA breach
StreamContinuous data flowHigh (continuous)Log tailing, metric streams, file watchers
SignalOne-shot coordinationVery low (once)"Tests passed, proceed with merge"

3. Pub/Sub Patterns

Publisher session (producing events):

yaml
channel: "ci-pipeline"
publish:
  events:
    - type: "build.started"
      data: {branch: "main", commit: "abc123"}
    - type: "build.progress"
      data: {stage: "testing", percent: 45}
    - type: "build.complete"
      data: {status: "success", duration_seconds: 184}
    - type: "build.failed"
      data: {stage: "lint", error: "3 errors found", file: "src/api.ts"}

Subscriber session (consuming events):

yaml
channel: "ci-pipeline"
subscribe:
  filter:
    types: ["build.complete", "build.failed"]  # only these events
  on_event:
    build.complete: "Summarize what changed and prepare release notes"
    build.failed: "Analyze the error and suggest a fix"

4. Event Filtering

Reduce noise by filtering events before processing:

yaml
filters:
  # Type filter: only process specific event types
  type: ["deploy.started", "deploy.complete", "deploy.failed"]
  
  # Source filter: only events from specific sessions
  source: ["session-ci-runner", "session-deploy-bot"]
  
  # Data filter: match on event payload fields
  data:
    environment: "production"       # only production events
    severity: ["critical", "high"]  # only high-severity alerts
  
  # Rate filter: throttle high-frequency events
  rate:
    max_per_minute: 10
    strategy: "latest"  # keep only the latest if throttled

5. Cross-Session State Synchronization

Keep multiple sessions in sync with shared state:

yaml
shared_state:
  channel: "project-state"
  keys:
    - name: "deployment_status"
      publisher: "deploy-session"
      subscribers: ["dev-session", "monitoring-session"]
      on_conflict: "last_write_wins"
    
    - name: "test_results"
      publisher: "test-session"
      subscribers: ["dev-session"]
      on_conflict: "merge"  # combine results from multiple publishers

State sync pattern:

Session A (dev):       publishes {current_file: "src/api.ts", task: "refactor auth"}
Session B (tests):     subscribes → auto-runs tests on src/api.ts when it changes
Session C (docs):      subscribes → updates API docs when src/api.ts changes

6. Reactive Workflow Patterns

Event-driven development loop:

yaml
workflow: "reactive-dev-loop"
channels:
  - name: "file-changes"
    subscribe:
      filter: {types: ["file.saved"], paths: ["src/**"]}
    on_event: "run tests for changed module"
    
  - name: "test-results"
    subscribe:
      filter: {types: ["test.complete"]}
    on_event:
      status=pass: "update coverage badge, proceed to next task"
      status=fail: "analyze failure, suggest fix, pause workflow"
    
  - name: "coverage-updates"
    subscribe:
      filter: {types: ["coverage.updated"], threshold_below: 80}
    on_event: "alert: coverage dropped below 80%, identify untested code"

Multi-agent coordination:

yaml
workflow: "parallel-research"
agents:
  - session: "researcher-frontend"
    channel: "research-progress"
    publishes: ["topic.complete", "finding.interesting"]
    
  - session: "researcher-backend"
    channel: "research-progress"
    publishes: ["topic.complete", "finding.interesting"]
    
  - session: "synthesizer"
    channel: "research-progress"
    subscribes: ["topic.complete"]
    on_event: "check if all topics covered, begin synthesis when ready"

7. Event Schema

Standard event format for consistency:

json
{
  "id": "evt_a1b2c3d4",
  "channel": "ci-pipeline",
  "type": "build.failed",
  "source": "session-ci-runner",
  "timestamp": "2026-06-13T14:32:00Z",
  "data": {
    "stage": "lint",
    "error": "3 errors found",
    "file": "src/api.ts",
    "details": ["unused import", "missing return type", "unsafe any"]
  },
  "metadata": {
    "retry_count": 0,
    "ttl_seconds": 3600
  }
}

Example

Coordinating a build-test-deploy pipeline across 3 sessions:

yaml
# Session 1: Builder
channel: "release-pipeline"
publish:
  - {type: "build.started", data: {version: "1.11.0"}}
  - {type: "build.complete", data: {artifacts: ["dist/uitkit-1.11.0.tgz"]}}

# Session 2: Tester (subscribes to build events)
channel: "release-pipeline"
subscribe:
  filter: {types: ["build.complete"]}
  on_event: "run full validation suite against artifacts"
publish:
  - {type: "tests.passed", data: {coverage: 87, suites: 12, duration: "4m22s"}}

# Session 3: Deployer (subscribes to test events)
channel: "release-pipeline"
subscribe:
  filter: {types: ["tests.passed"]}
  on_event: "deploy to staging, run smoke tests, then promote to production"
publish:
  - {type: "deploy.complete", data: {environment: "production", url: "..."}}

Anti-Patterns

  • Chatty channels: Publishing every minor event floods subscribers — aggregate at source, publish only milestones
  • No backpressure: If publisher outpaces subscriber, events queue infinitely — set buffer limits and drop/summarize old events
  • Missing event types: Publishing untyped events ("something happened") makes filtering impossible — always use structured type names (domain.action)
  • Tight coupling: Subscriber hard-codes publisher's session ID — use channel names and event types, not session references
  • Silent failures: Subscriber crashes on malformed event without logging — always wrap event handlers in try/catch with dead-letter logging
  • Stale state: Subscribing to state changes but not requesting current state on connect — always sync initial state before processing incremental updates

Frequently asked questions

What does the Channels Event Streaming AI skill do?

Claude Code Channels: real-time event streaming between concurrent sessions, pub/sub patterns, event filtering, cross-session state synchronization, and reactive agent workflows

Why use Channels Event Streaming on TypingMind?

Because you install it once and use it with any model. Channels Event Streaming is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Channels Event Streaming in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/UitbreidenOS/UitKit/tree/main/plugins/uitkit-ai-engineering/skills/channels-event-streaming. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Channels Event Streaming?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Channels Event Streaming?

As many as you like. As long as a model supports skills, you can use Channels Event Streaming with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Channels Event Streaming AI skill free?

It is published on GitHub by UitbreidenOS. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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