| name | connect-users |
| description | Find community members with relevant expertise using vector search |
| routing | {"use_when":"The user wants to FIND, MEET, or BE INTRODUCED to community members by expertise, interest, or background: \"anyone who…\", \"who knows…\", \"connect me with…\", \"looking for a mentor/teammate/cofounder in…\".\n","avoid_when":"Connecting SERVICES or integrations (github-integration). Questions about a topic itself rather than about people (\"explain X\" is chat).\n","examples":[{"text":"do you know anyone in AI research?","action":"search"},{"text":"anyone in the community working with medical imaging?","action":"search"},{"text":"connect me with someone who writes blog content","action":"search"},{"text":"my ecg project needs a teammate, anyone interested in medical AI?","action":"search"},{"text":"looking for a mentor in data engineering, any suggestions?","action":"search"}],"negative_examples":[{"text":"connect github again for mlai.au","instead":"github-integration"},{"text":"explain what a vector database is","instead":"respond_in_chat"}]} |
| actions | [{"name":"search","description":"Search the community for members matching an expertise/interest query.","params":{"query":{"type":"string","description":"The expertise or topic to search for."},"limit":{"type":"integer","description":"Max suggestions (default 5)."}}}] |
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.
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:
- Apologize briefly
- Suggest alternative ways to find help (Slack channels, events)
- Offer to try again
