| name | agent-rating-protocol |
| description | Decentralized reputation and trust scoring for autonomous AI agents. Bilateral blind evaluation prevents retaliation and gaming. Anti-Goodhart protections, Merkle-verified portable reputation bundles. Rate agents on quality, reliability, safety. Part of the Agent Trust Stack. |
| user-invocable | true |
| tags | ["agent-trust","reputation","rating","decentralized","anti-goodhart","blind-evaluation","mcp","autonomous-agents"] |
| metadata | {"openclaw":{"author":"alexfleetcommander","homepage":"https://vibeagentmaking.com","requires":{"bins":"[Truncated]","anyBins":"[Truncated]"}}} |
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")
Rating Dimensions
| Dimension | What It Measures |
|---|
quality | Output correctness and completeness |
reliability | Consistency and deadline adherence |
communication | Clarity of status updates and error reporting |
value | Cost-effectiveness relative to output quality |
safety | Adherence 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
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
