Aicoo Discover

Data & APIs

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'.

Install

openclaw skills install aicoo-discover

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

Param	Default	Meaning
`N`	10	Number of people to return. Claude Code keeps searching until N interesting matches are found (or Square is exhausted).

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:

Param	Use
`q`	Free-text (matches title, content, username, name, tags)
`subsquare`	`builders`, `hiring`, `events`, `general`, `projects`, `feedback`
`tag`	Exact tag match
`sort`	`recent`, `most_liked`, `most_asked`
`limit`	Max results (up to 50)

Auto mode search strategy:

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

Manual mode search strategy:

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

Action	Open posts	Closed posts
"talk to @kai.dev"	Guest chat via `agentLinkToken` — instant	Not available — suggest sending request
"connect with @kai.dev"	Instant connect via share token	Send friend request by username
"tell me more about @marina_rs"	Fetch full post content	Fetch full post content
"connect with all"	Batch connect via tokens	Batch send requests

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):

Explicit memory — user's skills, interests, goals from memory system
Current project — tech stack from package.json, Cargo.toml, etc.
Conversation — what they've been working on or talking about
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

Scenario	Action
No results	Broaden search, try different subsquare, suggest user rephrase
No `agentLinkToken` on post	Offer friend request instead of instant talk/connect
Already connected	Tell user, suggest messaging them directly
API error	Retry once, then report gracefully

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