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alexfleetcommander

Agent Matchmaking

by alexfleetcommander · GitHub ↗ · v0.1.1 · MIT-0
cross-platform ⚠ suspicious
152
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2
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Install in OpenClaw
/install agent-matchmaking
Description
Cross-platform agent discovery and trust-weighted matching for the autonomous agent economy. Capability profiles, reputation-based ranking, compatibility sco...
README (SKILL.md)

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


\x3C!-- 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

What it cannot do:

  • Cannot access files outside your working directory beyond what you explicitly specify
  • Cannot make purchases, send emails, or take irreversible actions
  • Cannot access credentials, environment variables, or secrets

License: Apache 2.0

Usage Guidance
This skill is instruction-only and tells you to run `pip install agent-matchmaking` (a package not shipped with the skill). Installing that package will download and run third-party code — review the PyPI project page and source repository before installing. Ask the author for: (1) a link to the package source (GitHub or similar) and a commit/tag you can inspect, (2) a reproducible install with pinned versions or hashes, and (3) clarification about network usage and what endpoints the package contacts for matchmaking/reputation. If you must test it, do so in an isolated sandbox or container, and avoid running it with sensitive credentials present. If you rely on the skill for production, require code review or prefer a bundled, verifiable implementation.
Capability Analysis
Type: OpenClaw Skill Name: agent-matchmaking Version: 0.1.1 The skill bundle provides documentation and instructions for an agent discovery and matchmaking system. The code examples in SKILL.md are standard Python library usage, and the metadata in _meta.json is consistent with the stated purpose. While it requires installing a third-party package (agent-matchmaking) via pip, there is no evidence of malicious intent, prompt injection, or unauthorized data access in the provided files.
Capability Assessment
Purpose & Capability
The stated purpose (agent discovery, matchmaking, reputation-aware ranking) aligns with requiring python3 and pip to use a Python library. However the skill describes federation and reputation lookups yet does not declare any API credentials or network requirements — plausible if public registries are used, but ambiguous.
Instruction Scope
SKILL.md instructs you to run `pip install agent-matchmaking` and import the package to perform searches and rankings. Because the package is not bundled, this means executing third‑party code at runtime. The included Security & Transparency Disclosure also asserts "No network access for core local operations" which conflicts with the implied need for network (pip install and likely registry/reputation queries).
Install Mechanism
There is no install spec in the skill bundle; the SKILL.md tells users to pip install from PyPI. While PyPI is a standard host, installing an external package downloads and executes remote code not reviewed with the skill. The skill does not provide pinned hashes, a source repository link for the package implementation, or offline/bundled alternatives — increasing supply‑chain risk.
Credentials
The skill requests no environment variables or credentials, which is reasonable for local profile management. However, federation, reputation, and registry features typically involve network endpoints or tokens; the lack of declared credentials is ambiguous (it may rely on public APIs, implicit unauthenticated access, or ask for credentials at runtime).
Persistence & Privilege
The skill does not request always:true, does not claim to modify other skills, and is user-invocable. It writes/reads profile files in the working directory per the disclosure, which is proportionate to the stated purpose.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install agent-matchmaking
  3. After installation, invoke the skill by name or use /agent-matchmaking
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.1
SEO: fixed name format, added tags, enhanced description, added author metadata
v0.1.0
Initial release -- cross-platform agent discovery and matching
Metadata
Slug agent-matchmaking
Version 0.1.1
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 2
Frequently Asked Questions

What is Agent Matchmaking?

Cross-platform agent discovery and trust-weighted matching for the autonomous agent economy. Capability profiles, reputation-based ranking, compatibility sco... It is an AI Agent Skill for Claude Code / OpenClaw, with 152 downloads so far.

How do I install Agent Matchmaking?

Run "/install agent-matchmaking" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Agent Matchmaking free?

Yes, Agent Matchmaking is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Agent Matchmaking support?

Agent Matchmaking is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Agent Matchmaking?

It is built and maintained by alexfleetcommander (@alexfleetcommander); the current version is v0.1.1.

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