teamind

name	teamind
description	Search group chat memory — semantic search across Slack channel history. Use when you need to find what was discussed, look up decisions/conclusions, find context about a topic not in your MEMORY.md, or answer questions about team conversations.
metadata	{"openclaw":{"emoji":"🧠","requires":{"bins":["node"]}}}

Teamind — Group Chat Memory Search

Search across your team's Slack group chat history using semantic search. Teamind indexes messages with vector embeddings and generates thread-level summaries, letting you find relevant discussions even without exact keywords.

Architecture

Slack channels → [indexer.js] → SQLite (messages + embeddings + thread summaries)
                                    ↓
Lobster search → [search.js] → Semantic similarity → Results
                                    ↓
Digest cron   → [digest.js]  → Per-lobster personalized daily digest

Everything runs locally. No external API needed (except embedding provider).

Setup

cd scripts/teamind
npm install
node setup.js                                        # init database
node indexer.js --add-channel C0XXXXXXX general     # register channels
node indexer.js                                       # run first index

Cron (recommended)

# Incremental index every hour
0 * * * * cd $(pwd)/scripts/teamind && node indexer.js >> /tmp/teamind-index.log 2>&1

# Daily digest at 04:00 UTC
0 4 * * * cd $(pwd)/scripts/teamind && node digest.js >> /tmp/teamind-digest.log 2>&1

When to Use

✅ USE this skill when:

• You need context about a topic discussed in group chats but not in your MEMORY.md
• User asks about past team discussions, decisions, or conclusions
• Looking up technical decisions, architecture choices, or meeting outcomes
• User asks "what was discussed about X" or "who talked about Y"

❌ DON'T use when:

• The answer is already in your MEMORY.md (check there first!)
• You need to send a message (use the message tool)
• You need real-time channel activity (use message read)

Commands

Search (preferred — fast, cheap)

# Basic search
node search.js "API design decision" --json

# With filters
node search.js "API design" --channel C0XXXXXXX --type decision --after 2026-03-01 --json --limit 10

Filters (all composable):

• --channel <ID> — specific channel
• --type <type> — decision | bug_fix | brainstorm | status_update | qa | casual | announcement
• --after <date> — ISO date/datetime
• --before <date> — ISO date/datetime
• --participant <name> — filter by participant name (partial match)
• --limit <N> — max results (default: 5)
• --json — JSON output (recommended for programmatic use)

Response (JSON mode):

{
  "query": "API design decision",
  "threads": [
    {
      "thread_ts": "1772606486.369999",
      "title": "API Redesign Proposal V2",
      "summary": "Discussed DB schema, API endpoints...",
      "thread_type": "decision",
      "key_points": ["Create landing_page_recommendation table"],
      "participants": [{"name": "alice", "role": "solution design"}],
      "open_items": [{"item": "tracking confirmation", "assignee": null}],
      "msg_count": 43,
      "score": 0.892
    }
  ],
  "messages": [
    {
      "username": "bob",
      "text": "message content...",
      "created_at": "2026-03-04T06:41:26",
      "score": 0.834
    }
  ]
}

Index (admin)

node indexer.js                                # incremental
node indexer.js --full                          # full re-index
node indexer.js --channel C0XXXXXXX          # specific channel
node indexer.js --hours 48                     # last 48h only
node indexer.js --add-channel C0XXX general    # register channel
node indexer.js --dry-run                      # preview

Digest (admin)

node digest.js                     # all lobsters
node digest.js --lobster alice     # single lobster
node digest.js --hours 48          # custom window
node digest.js --dry-run           # preview

Environment Variables

Variable	Required	Default	Description
SLACK_BOT_TOKEN	Yes (indexer)	—	Slack bot token
EMBEDDING_PROVIDER	Yes	openai	openai or gemini
OPENAI_API_KEY	If openai	—	OpenAI API key
GEMINI_API_KEY	If gemini	—	Google Gemini API key
LLM_PROVIDER	For indexer	anthropic	anthropic or openai
ANTHROPIC_API_KEY	If anthropic	—	For thread summaries
SUMMARY_MODEL	No	claude-sonnet-4-20250514	LLM model for summaries
EMBEDDING_MODEL	No	text-embedding-3-small	Embedding model
EMBEDDING_DIM	No	1536	Embedding dimensions
DB_PATH	No	./teamind.db	SQLite database path
BATCH_SIZE	No	50	Messages per embedding batch

Decision Guide

Do I know the answer from my own memory?
  → YES: Don't call Teamind
  → NO: Is it about group chat history?
    → YES: Use search.js (cheap + fast)
    → NO: Don't call Teamind

Tips

• Always try search first — you can synthesize from raw results
• Use time filters — --after / --before are your best friend
• Use --type filter — skip casual for technical questions
• key_points have the most specific, actionable info
• participants great for "who decided X" questions
• Database is a single SQLite file — easy to backup, move, or inspect