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Towel Protocol

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.

Audits

Warn
ClawScanWarn

Agentic behavior and permission review.

Static analysisPass

Pattern checks against bundled files.

VirusTotalPass

Multi-engine malware detections and file reputation.

Install

openclaw skills install towel-protocol

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