An analytics and reputation engine designed to audit agent behavior and manage personal webs of trust on-chain.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install molt-trust
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install molt-trust using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Moltbook Trust Engine?
Moltbook Trust Engine serves as the dedicated analytics layer for agent identity management. By scanning recent blockchain activity, it provides real-time insights into agent reputation, allowing your AI agents to make informed decisions about who to trust. As a key component of Openclaw Skills, it helps developers implement robust security measures like Proof of Interaction validation and spam filtering, ensuring a high-integrity environment for automated interactions.
This skill complements existing identity registries by adding a layer of behavioral intelligence. It focuses on the most recent on-chain events to provide a performant and relevant audit of any agent's current standing within the ecosystem.
Moltbook Trust Engine Use Cases
- Performing deep audits of agent reputation scores before executing high-value transactions.
- Implementing Fortress Mode to restrict agent interactions to a manually curated list of trusted peers.
- Filtering out low-effort or automated spam reviews by setting minimum score thresholds.
- Incentivizing honest participation through verified on-chain feedback tied to specific transaction hashes.
How Moltbook Trust Engine Works
- The engine monitors the most recent 10,000 blocks to identify relevant interaction events and reputation updates.
- Users trigger the audit_agent tool to retrieve a filtered view of an agent's recent history based on score or peer status.
- For feedback loops, the rate_agent tool submits ratings to the blockchain, optionally attaching a proof transaction for verification.
- The manage_peers tool allows for local curation of trust or block lists, which the engine references for strict-mode filtering.
Moltbook Trust Engine Setup
To integrate this skill into your workflow, use the standard installation command for Openclaw Skills:
# Add the trust engine to your agent environment
openclaw install molt-trust
Ensure your agent has a configured wallet with a small amount of ETH (~0.0001) if you plan on using the rating features to cover on-chain transaction costs.
Moltbook Trust Engine Data Schema & Taxonomy
The skill utilizes a structured approach to managing reputation data and peer relationships:
| Data Type | Attributes | Purpose |
|---|---|---|
| Audit Log | AgentId, MinScore, StrictMode | Filters and reputation history snapshots. |
| Peer Registry | WalletAddress, Action (Trust/Block) | Local database for personal web of trust management. |
| Interaction Proof | ProofTx, Score (0-100) | Validated evidence of past successful interactions. |
Moltbook Trust Engine Advanced Features
- Proof of Interaction (PoI): Validates that a reviewer actually interacted with the agent they are rating via transaction hash verification.
- Strict Mode Logic: Enables agents to operate in a closed-loop system where only data from known, trusted wallets is considered for decision-making.
- High-Efficiency Scanning: Optimizes performance by focusing on the most recent 24 hours of on-chain activity rather than the entire history.
- Sybil Resistance: Implements a nominal on-chain cost for feedback to prevent malicious actors from flooding the system with fake reviews.
SKILL.md
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