Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add aAAaqwq/AGI-Super-Team --skill agent-contactsgit clone --depth 1 https://github.com/aAAaqwq/AGI-Super-TeamWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aaaaqwq/agi-super-team/agent-contacts)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/agent-contacts"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/agent-contacts/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/agent-contacts"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/agent-contacts.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00033 | $0.01325 |
| Opus 5 | $0.00016 | $0.00662 |
| Sonnet 5 | $0.00007 | $0.00265 |
| Haiku 4.5 | $0.00003 | $0.00133 |
Grade A, and why
agent-contacts scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
| What | Path |
|---|---|
| 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))
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 180 lines · 33 tokens per session scan A 077ac537ce93
agent-contacts is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed yesterday), licensed MIT. It adds 33 tokens to every session and 1,325 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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