How does this skill manage conversation context in Slack?

The skill utilizes thread mapping, linking Slack's thread_ts or ts identifiers to specific agent tasks to ensure conversation continuity.

Does this skill include security best practices?

Yes, it provides patterns for Slack signature verification, encrypted token storage, and idempotency logic to prevent duplicate event processing.

Can I use this for both DMs and public channel mentions?

Absolutely. The integration logic is designed to process multiple event types including app_mentions, direct messages, and channel reactions.

Does it handle the Slack 3-second timeout requirement?

The architectural patterns suggest asynchronous processing to respond to Slack quickly while the agent handles complex tasks in the background.

Slack Agent Channel Integration

Name: Slack Agent Channel Integration
Author: rbarazi

byrbarazi

Collaboration Tools

Integrates AI agents with Slack to enable seamless communication through mentions, direct messages, and threaded conversations.

About

This skill provides a comprehensive framework for building Slack as a primary communication channel for AI agents. It simplifies the implementation of OAuth v2 flows, webhook event processing, and secure message pipelines. By mapping Slack threads to agent tasks and utilizing emoji reactions for real-time status updates, developers can create intuitive, interactive agent experiences within a familiar enterprise collaboration environment.

Key Features

Secure OAuth v2 flow implementation for workspace installation
Real-time event processing for mentions, DMs, and reactions
Automated thread mapping for maintaining conversation context
Emoji reaction-based status tracking (received, processing, done)
Built-in security patterns for signature verification and idempotency
0 GitHub stars

Use Cases

Building a Slack-based interface for custom LLM-powered assistants
Automating support ticket processing via @mentions in public channels
Creating secure enterprise bots that handle sensitive data via encrypted tokens

About

Key Features

Secure OAuth v2 flow implementation for workspace installation
Real-time event processing for mentions, DMs, and reactions
Automated thread mapping for maintaining conversation context
Emoji reaction-based status tracking (received, processing, done)
Built-in security patterns for signature verification and idempotency
0 GitHub stars

Use Cases

Building a Slack-based interface for custom LLM-powered assistants
Automating support ticket processing via @mentions in public channels
Creating secure enterprise bots that handle sensitive data via encrypted tokens