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Agent contacts

Skill aAAaqwq/AGI-Super-Team/skills/agent-contacts

AI agent contacts — add, list, remove MCP contacts. Use when someone gives an agent URL, or when you need to view/remove contacts.From its SKILL.md

Install
npx -y skills add aAAaqwq/AGI-Super-Team --skill agent-contacts

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • runs commandsInstructs the agent to run 2 commands, including `claude mcp add <slug> --transport http <mcp_url>` and 1 more.
  • fetches URLsInstructs the agent to fetch 1 URL, including discovery_url.

SKILL.md

4.9 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

Agent Contacts

Contact book for AI agents. Add an MCP address and your Claude Code can communicate with other agents.

When to use

  • /agent-contacts add <url> — add a new contact
  • /agent-contacts list — show all contacts
  • /agent-contacts remove <name> — remove a contact
  • When someone gives you an agent/bot URL

Paths

WhatPath
Contacts DB~/.claude/agent-contacts.json

contacts.json format

[
  {
    "name": "Your Name",
    "slug": "ivan-schedule",
    "mcp_url": "https://your-agent.example.com/mcp/",
    "discovery_url": "https://your-agent.example.com/.well-known/agent.json",
    "capabilities": ["scheduling"],
    "description": "Scheduling agent for Your Name",
    "added": "2026-02-26"
  }
]

How to execute

Parse $ARGUMENTS to determine the command: first word is the command (add, list, remove), the rest is the argument.

Add: /agent-contacts add <url>

import json, re
from datetime import date
from pathlib import Path

CONTACTS_FILE = Path.home() / ".claude" / "agent-contacts.json"

# 1. Load existing contacts
if CONTACTS_FILE.exists():
    contacts = json.loads(CONTACTS_FILE.read_text())
else:
    contacts = []

# 2. Normalize URL — $ARGUMENTS[1] is the URL
url = "$1".strip().rstrip("/")
if not url.endswith("agent.json"):
    discovery_url = url + "/.well-known/agent.json"
else:
    discovery_url = url
    url = url.rsplit("/.well-known/agent.json", 1)[0]

# 3. Use WebFetch to get agent.json content, then parse:
# - name = agent_data["name"]
# - description = agent_data.get("description", "")
# - capabilities = list(agent_data.get("capabilities", {}).keys())
# - mcp_url = agent_data["capabilities"][first_cap]["url"]
#   Ensure mcp_url ends with "/"

# 4. Generate slug
slug = re.sub(r"[^a-z0-9-]", "", name.lower().replace(" ", "-"))

# 5. Check for duplicates
if any(c["slug"] == slug for c in contacts):
    print(f"Contact '{name}' already exists.")
else:
    contacts.append({
        "name": name,
        "slug": slug,
        "mcp_url": mcp_url,
        "discovery_url": discovery_url,
        "capabilities": capabilities,
        "description": description,
        "added": str(date.today()),
    })
    CONTACTS_FILE.write_text(json.dumps(contacts, indent=2, ensure_ascii=False))

After saving to JSON, run:

claude mcp add <slug> --transport http <mcp_url>

Notify: "Contact <name> added. Restart Claude Code session to use their tools."

List: /agent-contacts list

import json
from pathlib import Path

CONTACTS_FILE = Path.home() / ".claude" / "agent-contacts.json"

if not CONTACTS_FILE.exists():
    print("No agent contacts yet. Use '/agent-contacts add <url>' to add one.")
else:
    contacts = json.loads(CONTACTS_FILE.read_text())
    if not contacts:
        print("No agent contacts yet.")
    else:
        for i, c in enumerate(contacts, 1):
            caps = ", ".join(c.get("capabilities", []))
            print(f"  {i}. {c['name']} ({c['slug']})")
            print(f"     MCP: {c['mcp_url']}")
            print(f"     Capabilities: {caps}")
            print()

Remove: /agent-contacts remove <name-or-slug>

import json
from pathlib import Path

CONTACTS_FILE = Path.home() / ".claude" / "agent-contacts.json"
target = "$1".strip().lower()  # name or slug

contacts = json.loads(CONTACTS_FILE.read_text())
match = [c for c in contacts if c["slug"] == target or c["name"].lower() == target]

if not match:
    print(f"Contact '{target}' not found.")
else:
    slug = match[0]["slug"]
    name = match[0]["name"]
    contacts = [c for c in contacts if c["slug"] != slug]
    CONTACTS_FILE.write_text(json.dumps(contacts, indent=2, ensure_ascii=False))

After removing from JSON, run:

claude mcp remove <slug>

Notify: "Contact <name> removed."

How to share your address with others

Simply send the link:

https://your-agent.example.com/.well-known/agent.json

If the person has this skill:

/agent-contacts add https://your-agent.example.com

If they don't have the skill -- one command:

claude mcp add ivan-schedule --transport http https://your-agent.example.com/mcp/

Important

  • agent-contacts.json is created automatically on the first add
  • Slug must be unique (used as MCP server name in Claude Code)
  • MCP URL must end with / (trailing slash)
  • After add, a new session of Claude Code is required for MCP tools to become available
  • This skill has side effects (claude mcp add/remove), hence disable-model-invocation: true

Related skills

  • deploy-website — deploy page with instructions for new contacts

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most context ai engineering skills give in ~1.2k tokens

Counted across 1,328 of the 2,349 authors here whose files we hold, read 2026-09-06

  • Dispatch a fresh subagent for each taskin 76 of 1328, across 59 files
  • Perform spec compliance review before code quality reviewin 44 of 1328, across 34 files
  • Dispatch a final code reviewer after all tasksin 38 of 1328, across 26 files
  • Answer subagent questions before allowing implementationin 36 of 1328, across 26 files
  • Use the least powerful model capable of the taskin 33 of 1328, across 26 files
  • Create a TodoWrite list for all tasksin 32 of 1328, across 22 files
  • Perform a task review after each implementationin 31 of 1328, across 24 files
  • Extract all tasks and context from the planin 29 of 1328, across 20 files
  • Provide full task text to subagentsin 28 of 1328, across 20 files
  • Use git worktrees for isolated workspacesin 25 of 1328, across 20 files
  • Specify the model explicitly when dispatching a subagentin 23 of 1328, across 18 files
  • Execute all tasks from the plan without stoppingin 21 of 1328, across 16 files

Said here and by no other author read

  • Fetch agent data from discovery URL
  • Generate unique slug for new contacts
  • Save contact details to JSON file
  • Run mcp add command after saving
  • Run mcp remove command after deleting
  • Notify user after adding or removing

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

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