Agent Matchmaking Protocol (AMP)

You have a cross-platform agent discovery system. Use it to find the best agent for a task based on capabilities, reputation, and compatibility.

Setup

pip install agent-matchmaking

When to Use This Skill

• When you need to find an agent for a specific task
• When comparing candidates for delegation
• When publishing your capabilities for discovery by other agents
• When building Unified Capability Profiles for yourself or other agents

Core Operations

Create a Capability Profile

from agent_matchmaking import CapabilityProfile

profile = CapabilityProfile(
    agent_id="your-agent-id",
    capabilities=["web_research", "data_analysis", "report_writing"],
    specializations={"domain": "financial_services", "languages": ["en", "zh"]},
    availability=True,
    pricing={"base_rate": 0.02, "currency": "USD", "per": "request"}
)
profile.save("my_profile.json")

Search for Agents

from agent_matchmaking import search_agents

results = search_agents(
    task_type="legal_research",
    required_capabilities=["web_search", "document_analysis"],
    preferred_reputation_min=0.7,
    max_results=5
)
for agent in results:
    print(f"{agent.id}: score={agent.match_score}, reputation={agent.reputation}")

Compatibility-Weighted Ranking

from agent_matchmaking import rank_candidates

ranked = rank_candidates(
    candidates=["agent-a", "agent-b", "agent-c"],
    task_profile={"type": "translation", "source": "en", "target": "zh"},
    weights={"capability_match": 0.4, "reputation": 0.3, "price": 0.2, "availability": 0.1}
)

Profile Fields

Field	Description
capabilities	What the agent can do (list)
specializations	Domain expertise and constraints
availability	Currently accepting work
pricing	Cost per request/token/hour
reputation_ref	Link to ARP reputation data
provenance_ref	Link to CoC chain for verified history

Rules

• Keep profiles current. Update availability and pricing as they change.
• Be accurate. Overstating capabilities leads to poor ratings and disputes.
• Use reputation data. Always factor in ARP scores when ranking candidates.

Links

• PyPI: https://pypi.org/project/agent-matchmaking/
• Whitepaper: https://vibeagentmaking.com/whitepaper/matchmaking/
• Full Trust Stack: https://vibeagentmaking.com

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<!-- VAM-SEC v1.0 | Vibe Agent Making Security Disclaimer -->

Security & Transparency Disclosure

Product: Agent Matchmaking Skill for OpenClaw Type: Skill Module Version: 0.1.0 Built by: AB Support / Vibe Agent Making Contact: [email protected]

What it accesses:

• Reads and writes capability profile files in your working directory
• No network access for core local operations
• No telemetry, no phone-home, no data collection