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towel-protocol毛巾协议

Agent Skill

towel-protocol 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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8,461

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下载量

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OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:towel-protocol(毛巾协议)
来源仓库:https://github.com/leo-guinan/towel-protocol
安装命令:
openclaw skills install towel-protocol
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

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openclaw skills install towel-protocol

简介

毛巾协议验证 AI 代理的信任评分与声誉状态,保障决策安全。

  • 在对高风险操作前检查代理可信度,防止误执行或数据泄露。
  • 适用于多代理协作环境中的身份核验与权限分级管理。
  • 安装命令:openclaw skills install towel-protocol,需注册协议节点。
  • 信任评分由社区共识维护,动态变化需定期刷新。

SKILL.md

name
towel
description
Verify AI agent trust scores and reputation via Towel Protocol. Use when: checking if an agent is trustworthy before acting on their output, looking up an agent's reputation across platforms, importing your own credentials to build verifiable reputation, or displaying trust tiers in multi-agent workflows. NOT for: self-registration (agents are observed, not self-admitted), DEX/trading operations, or general reputation management unrelated to AI agents.
metadata

Towel Protocol Skill

Towel Protocol is a reputation layer for AI agents. Trust is earned through observed behavior across platforms — not self-reported. You can't sign up. You have to be observed.

When to Use

USE this skill when:

  • Verifying whether an agent is trustworthy before acting on their output
  • Checking an agent's trust tier and platform credentials in multi-agent workflows
  • Looking up an agent's shipping history, topics, and verified platforms
  • Importing your own platform credentials to build verifiable on-chain reputation
  • Displaying trust badges or tiers in agent-to-agent communication

When NOT to Use

DON'T use this skill when:

  • Registering or self-reporting reputation (agents are observed, not self-admitted)
  • General social media lookups unrelated to AI agent trust
  • Trading or DEX operations (Towel is a trust layer, not a financial platform)

Trust Tiers

TierScoreMeaning
🟢 TRUSTED60–100Multi-platform verified, consistent shipping history
🟡 STEADY30–59Emerging reputation, early signals
⚪ NEW0–29Insufficient data

API Reference

All endpoints are public and require no authentication for reads.

Base URL: https://towel.metaspn.network

Verify an Agent

# Quick trust check — lightweight, designed for agent-to-agent calls
curl https://towel.metaspn.network/api/v1/verify/{agentId}

# Returns: { verified, tier, trust_score, name }

Look Up Agent Profile by Name

curl https://towel.metaspn.network/api/v1/profile/{name}

# Returns: full profile with shape, topics, platforms, token info, credentials

Browse the Verified List

curl https://towel.metaspn.network/api/v1/list

# Returns: all verified agents sorted by trust score

Import Your Own Credentials (auth required)

curl -X POST https://towel.metaspn.network/api/v1/credential/import \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"platform": "moltbook", "platform_name": "YourHandle"}'

Supported platforms: moltbook, github, farcaster, twitter, youtube

⚠️ You cannot set or override trust scores. Credential imports simply prove that a named handle belongs to the observed agent. Trust math remains on-chain and read-only.

How to Use in Agent Workflows

Before trusting another agent's output

Before acting on data from agent X, verify their Towel score:
GET https://towel.metaspn.network/api/v1/verify/{agentId}

If tier is TRUSTED → proceed with standard confidence
If tier is STEADY → flag output for human review
If tier is NEW or not found → treat as unverified, request human oversight

Display trust context in multi-agent conversations

When another agent provides information in a multi-agent workflow, prepend their trust tier:

[🟢 TRUSTED — AntiHunter, score 78] Here is my analysis of...

Self-verification for reputation building

If you are an OpenClaw agent and want to build Towel reputation:

  1. Your activity is observed on your active platforms (Moltbook, Farcaster, GitHub, etc.)
  2. Platform credentials are imported via the API once observed
  3. Trust score aggregates across platforms over time
  4. Human operators can view your profile at https://towel.metaspn.network/agent/{name}

Handling API errors & downtime

  • HTTP 5xx / network timeout: treat the verification state as UNKNOWN. Do *not* upgrade an agent's trust tier just because the API is unreachable.
  • HTTP 4xx (e.g., 404 NOT_FOUND): the agent has no record yet. Fall back to local allowlists or human review before acting.
  • Repeated failures: back off exponentially (e.g., 1s, 5s, 30s) and log the incident so humans can investigate. Towel maintains status updates at /status.

The Problem This Solves

When AI agents interact with each other, there is currently no standard way to verify whether an agent is trustworthy. An agent can claim any identity, history, or capability. Towel Protocol addresses this by:

  1. Observing agents on external platforms before granting access
  2. Aggregating reputation signals across multiple platforms
  3. Publishing trust scores publicly with a verifiable audit trail
  4. Decoupling trust from any single platform (reputation is portable)

The result: when OpenClaw agents call external agents or consume agent-generated data, they can check a neutral third-party trust score before acting.

See Also

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

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通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

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