teamind

Name: teamind
Availability: InStock
Author: corellis

Index Slack channel messages semantically for fast retrieval of discussions and decisions.

27|1|Updated Mar 19, 2026

Search Tags:#indexing #memory #slack #semantic-search #digest #team-chat #thread-summaries

Ready to run

npx skills add https://github.com/CorellisOrg/Corellis --skill teamind

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: teamind
Source: https://github.com/CorellisOrg/Corellis/tree/main/templates/teamind
Command: npx skills add https://github.com/CorellisOrg/Corellis --skill teamind

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Teamind enables semantic search across Slack group chat history, helping you locate decisions, context, and conclusions from conversations.

Core Features & Use Cases

Semantic indexing of Slack channels and thread-level summaries to surface relevant discussions
Quick retrieval of past decisions, architecture decisions, and outcomes
Digest generation to deliver per-lobster summaries for teams

Quick Start

Index your Slack channels and run a semantic search to locate relevant conversations.

Frequently Asked Questions about teamind

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

FAQPage Schema

How do I search Slack history for past decisions and context semantically?▼

Semantic search of Slack history works by indexing team messages locally using an embedding provider to vectorize conversations. This enables fast retrieval of past decisions and context missing from standard notes.

Can I generate a digest of thread summaries for specific Slack channels?▼

Yes, you can generate a digest of thread summaries for specific Slack channels. The indexing process supports per-channel registration and creates thread-level summaries to deliver overviews for your team.

Do I need an external embedding provider to index Slack messages?▼

Yes, an external embedding provider is required to index Slack messages. The Skill runs locally but relies on this provider for vectorization to enable semantic search across team chat history.

What is the best way to find context across cross-channel Slack conversations?▼

The best way to find context across cross-channel Slack conversations is semantic indexing. By registering channels and vectorizing histories, you retrieve relevant discussions even when context spans multiple groups.

Does semantic search work for retrieving architecture decisions from team chat?▼

Yes, semantic search works for retrieving architecture decisions from team chat. By indexing group-channel histories, you quickly surface specific outcomes and technical conclusions from past discussions.

Why use semantic indexing instead of standard Slack search for team discussions?▼

Semantic indexing provides better retrieval than standard Slack search by understanding the meaning of discussions rather than matching exact keywords. It captures context, decisions, and thread summaries across channels.