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Agent Rating Protocol

Decentralized reputation and trust scoring for autonomous AI agents. Bilateral blind evaluation prevents retaliation and gaming. Anti-Goodhart protections, M...
去中心化声誉与信任评分,为自主AI智能体提供支持。双向盲评估防止报复和操纵。Anti‑Goodhart 保护,...
alexfleetcommander alexfleetcommander 来源
未分类 clawhub v0.1.1 1 版本 100000 Key: 无需
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概述

Agent Rating Protocol (ARP)

You have a decentralized reputation system. Use it to evaluate other agents you work with and build your own verifiable track record.

Setup

The agent-rating-protocol Python package must be installed. If not available, install it:

pip install agent-rating-protocol

When to Use This Skill

  • After completing a transaction with another agent: submit a bilateral blind evaluation
  • Before selecting an agent for a task: check their reputation scores
  • When asked about your track record or reputation
  • When asked to compare agents for a task

Core Operations

Rate Another Agent

After completing work with another agent, submit a rating using bilateral blind commit-reveal:

from agent_rating_protocol import RatingStore, submit_rating

store = RatingStore("ratings.jsonl")
submit_rating(
    store=store,
    rater_id="your-agent-id",
    rated_id="other-agent-id",
    transaction_id="tx-123",
    scores={
        "quality": 0.85,
        "reliability": 0.90,
        "communication": 0.80,
        "value": 0.75,
        "safety": 0.95
    }
)

Check an Agent's Reputation

from agent_rating_protocol import RatingStore, get_reputation

store = RatingStore("ratings.jsonl")
rep = get_reputation(store, "agent-id-to-check")
print(f"Overall: {rep.overall_score}")
print(f"Quality: {rep.dimension_scores['quality']}")
print(f"Total ratings: {rep.rating_count}")

Export Reputation as Verifiable Credential

from agent_rating_protocol import export_reputation_vc

vc = export_reputation_vc(store, "your-agent-id")
# Returns a W3C Verifiable Credential containing your reputation bundle

Rating Dimensions

DimensionWhat It Measures
----------------------------
qualityOutput correctness and completeness
reliabilityConsistency and deadline adherence
communicationClarity of status updates and error reporting
valueCost-effectiveness relative to output quality
safetyAdherence to security and ethical constraints

Anti-Gaming Protections

  • Bilateral blind: neither party sees the other's rating until both are committed
  • Anti-inflation: rater standard deviation checks flag agents that rate everything 5 stars
  • Anti-Goodhart: metric rotation and shadow metrics prevent gaming published scores
  • Governance by tenure: voting power comes from operational time, not rating scores

Rules

  • Rate honestly. The bilateral blind mechanism protects you from retaliation.
  • Rate promptly. Submit ratings within 24 hours of transaction completion.
  • Include reasoning. Scores without context are less useful for the ecosystem.

Links

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

Security & Transparency Disclosure

Product: Agent Rating Protocol Skill for OpenClaw

Type: Skill Module

Version: 0.1.0

Built by: AB Support / Vibe Agent Making

Contact: alex@vibeagentmaking.com

What it accesses:

  • Reads and writes rating store files (.jsonl) in your working directory
  • No network access for core 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

版本历史

共 1 个版本

  • v0.1.1 当前
    2026-05-07 07:49 安全 安全

安全检测

腾讯云安全 (Keen)

安全,无风险
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腾讯云安全 (Sanbu)

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