version: "1.0.1" name: discover description: "Use this skill when the user wants to discover interesting people on Aicoo Square. Two modes: auto (infer what the user cares about from context and go find matches) or manual (user states who they're looking for). Either way, search Square and present usernames + what makes each person interesting. Triggers on: 'discover', 'discover people', 'who's on square', 'find people', 'find someone', 'find teammate', 'who should I connect with', 'show me builders', 'who's interesting', 'explore square', 'browse people', 'get contacts', 'find collaborators'."

Discover — Find Interesting People on Square

Search Aicoo Square and surface the most relevant people — either by inferring what the user cares about (auto) or from an explicit description (manual). Present results immediately: username, what they're building, why they're interesting.

Design goal: Minimize time-to-first-aha. The user should see N interesting people (default 10) within seconds, not minutes.

Parameters

User can override: "discover 5 people", "find me 20 builders", etc.

Modes

Auto Mode (default when no explicit query)

Claude Code infers search intent from available context:

• User's current project / tech stack
• Memory (skills, interests, goals)
• Recent conversation topics
• CLAUDE.md / package.json / repo signals

Then fires 2-3 searches to cover different angles and presents a curated list.

Example triggers:

• "discover people"
• "who should I connect with?"
• "who's interesting on square?"
• "find me people" (no further specification)

Manual Mode (user states intent)

User provides a description. Claude Code extracts 2-3 key terms and searches.

Example triggers:

• "find someone who knows Rust + WebRTC"
• "discover people building dev tools"
• "who's doing ML infra?"

Execution

Regardless of mode, Claude Code does the work and presents results. Never ask the user to refine a query before showing results.

Step 1: Search Square

# Primary search
curl -s "https://www.aicoo.io/api/square?q=<TERMS>&limit=10&sort=most_asked" | jq .
 
# Broaden if sparse (try different angle)
curl -s "https://www.aicoo.io/api/square?subsquare=builders&sort=most_asked&limit=10" | jq .

Query params:

Auto mode search strategy:

1. Infer 2-3 search angles from context (e.g., user's tech stack, current interests, goals)
2. Fire searches in parallel (request more than N to allow filtering)
3. Deduplicate and rank by relevance to user
4. Present top N results

Manual mode search strategy:

1. Extract key terms from user's description
2. Search with q + optional subsquare/tag filters
3. If < N results, broaden (fewer terms, drop filters, try adjacent queries)
4. Keep going until N results or no more leads
5. Present all N results

Step 2: Present Results

Format as a clean list — username + what makes them interesting:

Found some people you might vibe with:
 
1. @kai.dev — Building real-time collab tools in Rust + WebRTC. 12 likes, 5 asks.
   "Senior eng, 5 years in distributed systems, open to hackathons"
 
2. @marina_rs — Rust systems engineer shipping open-source infra. 8 likes.
   "Working on a new actor framework, looking for contributors"
 
3. @zack.builds — Full-stack dev tools, just shipped a TS CLI for API testing.
   "Built similar stuff to what you're working on — might be a good collab"
 
Want to talk to any of their agents? Or connect directly?

What to include per person:

• @username (bolded or prominent)
• One-line hook: what they're building or what's interesting about them
• Engagement signal: likes, asks, connect count (social proof)
• A quote or snippet from their post content (max 1 line)
• Reachability badge: [open] = can talk to their agent directly, [closed] = must send request
• Why they're relevant to this user (auto mode only — tie back to inferred context)

Reachability field in API response:

• reachability: "open" + agentLinkToken present → user can be reached directly (talk to agent / instant connect)
• reachability: "closed" + agentLinkToken: null → username visible but must send a friend request to connect

Step 3: Next Actions

After presenting, offer these paths (don't block on them — user can just proceed):

For open posts (reachability = "open")

Talk to agent (fastest aha moment):

curl -s -X POST "https://www.aicoo.io/api/chat/guest-v04" \
  -H "Content-Type: application/json" \
  -d '{
    "token": "<agentLinkToken>",
    "message": "Hey! What are you currently building?",
    "stream": false
  }' | jq .

Instant connect (add to contact book):

curl -s -X POST "https://www.aicoo.io/api/v1/network/connect" \
  -H "Authorization: Bearer $PULSE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"shareToken": "<agentLinkToken>"}' | jq .

For closed posts (reachability = "closed")

Only option is sending a friend request by username:

curl -s -X POST "https://www.aicoo.io/api/v1/network/request" \
  -H "Authorization: Bearer $PULSE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"to": "<username>"}' | jq .

After they accept, you can then message them.

Auto Mode: Context Signals

When inferring what to search for, consider (in priority order):

1. Explicit memory — user's skills, interests, goals from memory system
2. Current project — tech stack from package.json, Cargo.toml, etc.
3. Conversation — what they've been working on or talking about
4. Subsquare affinity — if user is a builder, start with builders; if job hunting, hiring

Combine signals into 2-3 diverse searches. Don't over-optimize for one angle — surprise is part of discovery.

Practical Patterns

Pattern 1: Cold start onboarding

User: "discover people"
(No prior context about user)
 
→ Browse most active: GET /api/square?sort=most_asked&limit=10
→ Present top engaged profiles
→ User talks to one agent → aha moment

Pattern 2: Context-aware auto discovery

User: "who should I connect with?"
(User is building a TypeScript agent framework, interested in ML)
 
→ Search 1: GET /api/square?q=typescript+agents&sort=most_asked
→ Search 2: GET /api/square?q=machine+learning&subsquare=builders
→ Search 3: GET /api/square?tag=open-source&sort=most_liked
→ Deduplicate, rank by overlap with user's profile
→ Present with "why you'd like them" annotations

Pattern 3: Manual — hackathon teammate

User: "find me a frontend dev for a hackathon this weekend"
 
→ Search: GET /api/square?q=frontend+hackathon&subsquare=events
→ Broaden: GET /api/square?q=frontend&subsquare=builders&sort=most_asked
→ Present matches

Pattern 4: Manual — specific expertise

User: "who knows about Cloudflare Workers?"
 
→ Search: GET /api/square?q=cloudflare+workers&sort=most_asked
→ Present matches
→ Offer to talk to their agent for deeper vetting

Error Handling

Security Notes

• Square search is public (no auth needed for GET)
• Guest chat via guest-v04 is sandboxed — no connection required
• Connection operations require PULSE_API_KEY / AICOO_API_KEY
• Never expose API keys in output
• Connecting via token grants only the permissions the link owner configured