# Connect Users

> Find community members with relevant expertise using vector search

- Skill: `majiayu000/connect-users` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/connect-users`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/connect-users/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/connect-users

---


# Connect Users Skill

This skill enables Claude to find and recommend MLAI community members based on their expertise, interests, and what they're working on.

## Capabilities

- Search for users with specific expertise using vector similarity
- Match users based on topics, skills, and interests
- Provide warm introductions and connection suggestions

## Parameters

- **query**: The expertise or topic the user is looking for (required)
- **exclude_user_id**: Slack user ID to exclude from results (optional - usually the requester)
- **limit**: Maximum number of users to suggest (default: 5)

## Workflow

### Step 1: Extract Topics
Parse the user's query to identify the specific expertise areas they're looking for.

Common patterns to recognize:
- "who knows about X" → extract X
- "anyone working on Y" → extract Y
- "expert in Z" → extract Z
- "looking for someone in [field]" → extract field
- "recommend someone for [topic]" → extract topic

### Step 2: Vector Search
Use the `vector_search` function to find users with matching expertise.

The search uses cosine similarity on embeddings stored in the `user_expertise` table.

```sql
SELECT u.id, u.name, u.slack_id, e.topic, e.relationship,
       1 - (e.embedding <=> $query_embedding) as similarity
FROM users u
JOIN user_expertise e ON u.id = e.user_id
WHERE 1 - (e.embedding <=> $query_embedding) > 0.7
ORDER BY similarity DESC
LIMIT 5;
```

### Step 3: Format Response
Generate a warm, friendly response suggesting the matched users.

Include for each user:
- Their name (with Slack mention if available)
- Their expertise area that matched
- The relationship type (expert, working on, interested)

## Response Style

- Start with an acknowledgment of what they're looking for
- Use casual Australian language occasionally (mate, no worries, legend, etc.)
- Be encouraging about making connections
- If no users found, offer alternative suggestions or ask for clarification

## Example Responses

### Successful Match
```
G'day! Looking for folks in AI research, eh? Here are some legends who might help:

• **@sam** - Expert in machine learning and neural networks
• **@jane** - Currently working on computer vision projects
• **@bob** - Interested in deep learning applications

Feel free to reach out to them! 🦘
```

### No Matches Found
```
Hmm, I couldn't find anyone specifically matching "quantum computing" in our community yet.

A few things we could try:
• Broaden the search - maybe "physics" or "advanced computing"?
• Post in #introductions asking if anyone's into this space
• Check out our upcoming events - might meet someone there!

Want me to try a different search? 🤔
```

## Error Handling

If the database search fails:
1. Apologize briefly
2. Suggest alternative ways to find help (Slack channels, events)
3. Offer to try again